S-Lab, Nanyang Technological University (S-Lab) provides access to\nthe MIMIC-IT Dataset (referred to as the Dataset) under the following\nconditions.

Bo Li^*,♠,1 Yuanhan Zhang^*,♠,1 Liangyu Chen^*,1 Jinghao Wang^*,1 Fanyi Pu^*,1
Jingkang Yang¹ Chunyuan Li² Ziwei Liu^✉,1

¹S-Lab, Nanyang Technological University ²Microsoft Research, Redmond
^♠ Co-Project Lead ^* Equal Contribution ^✉ Corresponding Author

## Dataset Description - **Homepage: https://otter-ntu.github.io** - **Repository: https://github.com/Luodian/Otter** - **Paper: https://arxiv.org/abs/2306.05425** **Note 1: To reduce memory consumption during image loading and improve loading speed, we are converting the JSON format of images to the Parquet format. For detailed information, please refer to [this link](https://github.com/Luodian/Otter/blob/main/docs/mimicit_format.md).** **Note 2: We are uploading the full version of `DC` and `E4D`, the new files are indicated by the suffix `1207`.** ### Dataset Summary MIMIC-IT offers a diverse and extensive dataset of 2.8M multimodal instruction-response pairs, designed to enhance the performance of Vision-Language Models (VLMs) in real-life scenarios, enabling VLMs to excel in perception, reasoning, and planning while also catering to a multilingual audience. MIMIC-IT enables the application of egocentric visual assistant model that can serve that can answer your questions like **Hey, Do you think I left my keys on the table?**. Harness the power of MIMIC-IT to unlock the full potential of your AI-driven visual assistant and elevate your interactive vision-language tasks to new heights. MIMIC-IT provides multilingual instructions, supporting English, Chinese, Korean, Japanese, German, French, Spanish, and Arabic, thereby allowing a larger global audience to altogether enjoy from the convenience brought about by advancements in artificial intelligence.

## Using MIMIC-IT We have already upload the `images.parquet` file. You can check [`tools/load.py`](tools/load.py) to learn how to load the dataset (`instruction.json` + `images.parquet`) and check the integrity of the whole dataset. You can also use [this code](https://huggingface.co/datasets/pufanyi/MIMICIT/blob/main/tools/convert_to_parquet.py) to convert `image.json` to `parquet` version by yourself. You can following the steps to obtain the MIMIC-IT dataset. Each task (e.g. `DC`, `LA`) in MIMIC-IT is composed of three parts, including: 1. `xx.json` file: the images in base64 format. 2. `xx_instructions.json` file: the instruction-response pairs (also includes image ids and related instructions ids for each instruction-response pair) for each task. 3. `xx_train.json` file: the customized related instruction-response pairs for each instruction. You can directly download the contents in the `data` folder. The distribution of the `data` folder is as follows: ```plain data/ CGD/ CGD.json CGD_images_preview.csv CGD_instructions.json ... ``` For each `dataset_name`, there are three main files **except for `DC` and `E4D`**: 1. `{dataset_name}.json`: Stores the image numbers and their corresponding base64 codes in lossless compressed PNG format. ```json { ""image_id_1"": ""base64_code_1"", ""image_id_2"": ""base64_code_2"", ... } ``` 2. `{dataset_name}_images_preview.csv`: Stores the image numbers and their corresponding base64 codes in lossy compressed JPG format, mainly used for display in the Dataset Card. ```csv id, image ""image_id_1"", ""base64_code_1"" ""image_id_2"", ""base64_code_2"" ... ``` 3. `{dataset_name}_instructions.json`: Stores each instruction and its associated answer. ```json { ""meta"": { ""version"": current_version, ""time"": update_time, ""author"": ""ntu"" }, ""data"": { ""instruction_id_1"": { ""instruction"": ""instruction_1"", ""answer"": ""answer_of_instruction_1"", ""image_ids"": [ ""image_id_1"", ""image_id_2"", ... ], ""rel_ins_ids"": [ ""related_instruction_id_1"", ""related_instruction_id_2"", ... ] }, ... } } ``` Of course, you can also use `wget` or `curl` for direct downloads. Below is an example. Before proceeding with the downloads, you need to set your Hugging Face token. For that, please refer to [this page](https://huggingface.co/docs/hub/security-tokens). ```shell $ # Set Hugging Face Token $ HF_TOKEN=""YOUR_HUGGING_FACE_TOKEN"" $ # Set the dataset you want to download $ DATASET_NAME=""DATASET_YOU_WANT_TO_DOWNLOAD"" # e.g. CGD $ # Download {DATASET_NAME}.json $ wget --header=""Authorization: Bearer $HF_TOKEN"" ""https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}.json"" $ # Download {DATASET_NAME}_instructions.json $ wget --header=""Authorization: Bearer $HF_TOKEN"" ""https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}_instructions.json"" $ # Download {DATASET_NAME}_images_preview.csv (usually not necessary) $ wget --header=""Authorization: Bearer $HF_TOKEN"" ""https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}_images_preview.csv"" ``` Or ```shell $ # Set Hugging Face Token $ HF_TOKEN=""YOUR_HUGGING_FACE_TOKEN"" $ # Set the dataset you want to download $ DATASET_NAME=""DATASET_YOU_WANT_TO_DOWNLOAD"" # e.g. CGD $ # Download {DATASET_NAME}.json $ curl -LJO -H ""Authorization: Bearer $HF_TOKEN"" ""https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}.json"" $ # Download {DATASET_NAME}_instructions.json $ curl -LJO -H ""Authorization: Bearer $HF_TOKEN"" ""https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}_instructions.json"" $ # Download {DATASET_NAME}_images_preview.csv (usually not necessary) $ curl -LJO -H ""Authorization: Bearer $HF_TOKEN"" ""https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}_images_preview.csv"" ``` Alternatively, you can use `dataset.load_dataset` for downloading. However, due to Hugging Face's size limitations, all images can only be loaded in JPG format. Below is an example using `CGD` dataset: ### CGD_Images Download the JPG format images and their corresponding identifiers: ```python from datasets import load_dataset data = load_dataset(""pufanyi/MIMICIT"", ""CGD_Images"") ``` The format will be like: ```json { ""id"": ""CGD_IMG_000000426149"", ""image"": } ``` It should be noted that, due to size limitations, for `DC` (Dense Captions), this command will only extract a portion of the images from the `DC` collection for downloading. ### CGD_Instructions Download all instructions: ```python from datasets import load_dataset data = load_dataset(""pufanyi/MIMICIT"", ""CGD_Instructions"") ``` The format will be like: ```json { ""id"": ""CGD_INS_000000"", ""instruction"": ""What is the difference between the two pizzas in these images?"", ""answer"": ""The pizza in the first image is on a red plate and being held by an old lady, while the pizza in the second image is on a metal counter being prepared by a woman in a blue shirt."", ""images"": [ ""CGD_IMG_000000069568"", ""CGD_IMG_000000328270"" ], ""related instructions"": [ ""CGD_INS_000001"" ] } ``` ### CGD_Preview Download all instructions along with their corresponding JPG images: ```python from datasets import load_dataset data = load_dataset(""pufanyi/MIMICIT"", ""CGD_Preview"") ``` The format will be like: ```json { ""id"": ""CGD_INS_000000"", ""instruction"": ""What is the difference between the two pizzas in these images?"", ""answer"": ""The pizza in the first image is on a red plate and being held by an old lady, while the pizza in the second image is on a metal counter being prepared by a woman in a blue shirt."", ""images"": [ , ], ""related instructions"": [ ""CGD_INS_000001"" ] } ``` It should be noted that, due to size limitations, for `DC` (Dense Captions), this command will only extract a portion of the images from the `DC` collection for downloading." wecover/OPUS_GlobalVoices,{},"--- configs: - config_name: default data_files: - split: train path: '*/*/train.parquet' - split: valid path: '*/*/valid.parquet' - split: test path: '*/*/test.parquet' - config_name: am data_files: - split: train path: '*/*am*/train.parquet' - split: test path: '*/*am*/test.parquet' - split: valid path: '*/*am*/valid.parquet' - config_name: ar data_files: - split: train path: '*/*ar*/train.parquet' - split: test path: '*/*ar*/test.parquet' - split: valid path: '*/*ar*/valid.parquet' - config_name: bn data_files: - split: train path: '*/*bn*/train.parquet' - split: test path: '*/*bn*/test.parquet' - split: valid path: '*/*bn*/valid.parquet' - config_name: ca data_files: - split: train path: '*/*ca*/train.parquet' - split: test path: '*/*ca*/test.parquet' - split: valid path: '*/*ca*/valid.parquet' - config_name: de data_files: - split: train path: '*/*de*/train.parquet' - split: test path: '*/*de*/test.parquet' - split: valid path: '*/*de*/valid.parquet' - config_name: el data_files: - split: train path: '*/*el*/train.parquet' - split: test path: '*/*el*/test.parquet' - split: valid path: '*/*el*/valid.parquet' - config_name: en data_files: - split: train path: '*/*en*/train.parquet' - split: test path: '*/*en*/test.parquet' - split: valid path: '*/*en*/valid.parquet' - config_name: es data_files: - split: train path: '*/*es*/train.parquet' - split: test path: '*/*es*/test.parquet' - split: valid path: '*/*es*/valid.parquet' - config_name: fa data_files: - split: train path: '*/*fa*/train.parquet' - split: test path: '*/*fa*/test.parquet' - split: valid path: '*/*fa*/valid.parquet' - config_name: fr data_files: - split: train path: '*/*fr*/train.parquet' - split: test path: '*/*fr*/test.parquet' - split: valid path: '*/*fr*/valid.parquet' - config_name: hi data_files: - split: train path: '*/*hi*/train.parquet' - split: test path: '*/*hi*/test.parquet' - split: valid path: '*/*hi*/valid.parquet' - config_name: hu data_files: - split: train path: '*/*hu*/train.parquet' - split: test path: '*/*hu*/test.parquet' - split: valid path: '*/*hu*/valid.parquet' - config_name: id data_files: - split: train path: '*/*id*/train.parquet' - split: test path: '*/*id*/test.parquet' - split: valid path: '*/*id*/valid.parquet' - config_name: it data_files: - split: train path: '*/*it*/train.parquet' - split: test path: '*/*it*/test.parquet' - split: valid path: '*/*it*/valid.parquet' - config_name: mg data_files: - split: train path: '*/*mg*/train.parquet' - split: test path: '*/*mg*/test.parquet' - split: valid path: '*/*mg*/valid.parquet' - config_name: mk data_files: - split: train path: '*/*mk*/train.parquet' - split: test path: '*/*mk*/test.parquet' - split: valid path: '*/*mk*/valid.parquet' - config_name: my data_files: - split: train path: '*/*my*/train.parquet' - split: test path: '*/*my*/test.parquet' - split: valid path: '*/*my*/valid.parquet' - config_name: nl data_files: - split: train path: '*/*nl*/train.parquet' - split: test path: '*/*nl*/test.parquet' - split: valid path: '*/*nl*/valid.parquet' - config_name: pl data_files: - split: train path: '*/*pl*/train.parquet' - split: test path: '*/*pl*/test.parquet' - split: valid path: '*/*pl*/valid.parquet' - config_name: pt data_files: - split: train path: '*/*pt*/train.parquet' - split: test path: '*/*pt*/test.parquet' - split: valid path: '*/*pt*/valid.parquet' - config_name: ru data_files: - split: train path: '*/*ru*/train.parquet' - split: test path: '*/*ru*/test.parquet' - split: valid path: '*/*ru*/valid.parquet' - config_name: sr data_files: - split: train path: '*/*sr*/train.parquet' - split: test path: '*/*sr*/test.parquet' - split: valid path: '*/*sr*/valid.parquet' - config_name: sw data_files: - split: train path: '*/*sw*/train.parquet' - split: test path: '*/*sw*/test.parquet' - split: valid path: '*/*sw*/valid.parquet' - config_name: tr data_files: - split: train path: '*/*tr*/train.parquet' - split: test path: '*/*tr*/test.parquet' - split: valid path: '*/*tr*/valid.parquet' - config_name: ur data_files: - split: train path: '*/*ur*/train.parquet' - split: test path: '*/*ur*/test.parquet' - split: valid path: '*/*ur*/valid.parquet' - config_name: zhs data_files: - split: train path: '*/*zhs*/train.parquet' - split: test path: '*/*zhs*/test.parquet' - split: valid path: '*/*zhs*/valid.parquet' - config_name: zht data_files: - split: train path: '*/*zht*/train.parquet' - split: test path: '*/*zht*/test.parquet' - split: valid path: '*/*zht*/valid.parquet' - config_name: bg data_files: - split: train path: '*/*bg*/train.parquet' - split: test path: '*/*bg*/test.parquet' - split: valid path: '*/*bg*/valid.parquet' - config_name: cs data_files: - split: train path: '*/*cs*/train.parquet' - split: test path: '*/*cs*/test.parquet' - split: valid path: '*/*cs*/valid.parquet' - config_name: da data_files: - split: train path: '*/*da*/train.parquet' - split: test path: '*/*da*/test.parquet' - split: valid path: '*/*da*/valid.parquet' - config_name: eo data_files: - split: train path: '*/*eo*/train.parquet' - split: test path: '*/*eo*/test.parquet' - split: valid path: '*/*eo*/valid.parquet' - config_name: he data_files: - split: train path: '*/*he*/train.parquet' - split: test path: '*/*he*/test.parquet' - split: valid path: '*/*he*/valid.parquet' - config_name: km data_files: - split: train path: '*/*km*/train.parquet' - split: test path: '*/*km*/test.parquet' - split: valid path: '*/*km*/valid.parquet' - config_name: ko data_files: - split: train path: '*/*ko*/train.parquet' - split: test path: '*/*ko*/test.parquet' - split: valid path: '*/*ko*/valid.parquet' - config_name: ku data_files: - split: train path: '*/*ku*/train.parquet' - split: test path: '*/*ku*/test.parquet' - split: valid path: '*/*ku*/valid.parquet' - config_name: ne data_files: - split: train path: '*/*ne*/train.parquet' - split: test path: '*/*ne*/test.parquet' - split: valid path: '*/*ne*/valid.parquet' - config_name: or data_files: - split: train path: '*/*or*/train.parquet' - split: test path: '*/*or*/test.parquet' - split: valid path: '*/*or*/valid.parquet' - config_name: pa data_files: - split: train path: '*/*pa*/train.parquet' - split: test path: '*/*pa*/test.parquet' - split: valid path: '*/*pa*/valid.parquet' - config_name: ro data_files: - split: train path: '*/*ro*/train.parquet' - split: test path: '*/*ro*/test.parquet' - split: valid path: '*/*ro*/valid.parquet' - config_name: sq data_files: - split: train path: '*/*sq*/train.parquet' - split: test path: '*/*sq*/test.parquet' - split: valid path: '*/*sq*/valid.parquet' - config_name: sv data_files: - split: train path: '*/*sv*/train.parquet' - split: test path: '*/*sv*/test.parquet' - split: valid path: '*/*sv*/valid.parquet' language: - am - ar - bg - bn - ca - cs - da - de - el - en - eo - es - fa - fr - he - hi - hu - id - it - km - ko - ku - mg - mk - my - ne - nl - or - pa - pt - pl - ro - ru - sq - sr - sv - sw - tr - ur - zh ---" CohereForAI/Global-MMLU,"{""dataset_info"": [{""config_name"": ""am"", 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This dataset combines machine translations for [MMLU](https://huggingface.co/datasets/cais/mmlu) questions along with professional translations and crowd-sourced post-edits. It also includes cultural sensitivity annotations for a subset of the questions (2850 questions per language) and classifies them as *Culturally Sensitive* (CS) 🗽 or *Culturally Agnostic* (CA) ⚖️. These annotations were collected as part of an open science initiative led by Cohere For AI in collaboration with many external collaborators from both industry and academia. - **Curated by:** Professional annotators and contributors of [Cohere For AI Community](https://cohere.com/research). - **Language(s):** 42 languages. - **License:** [Apache 2.0](https://opensource.org/license/apache-2-0) **Note:** We also provide a ""lite"" version of Global MMLU called [""Global-MMLU-Lite""](https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite). This datatset is more balanced containing 200 samples each for CA and CS subsets for each language. And provides coverage for 15 languages with human translations. ### **Global-MMLU Dataset Family:** | Name | Explanation | |------|--------------| | [Global-MMLU](https://huggingface.co/datasets/CohereForAI/Global-MMLU) | Full Global-MMLU set with translations for all 14K samples including CS and CA subsets| | [Global-MMLU-Lite](https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite) | Lite version of Global-MMLU with human translated samples in 15 languages and containing 200 samples each for CS and CA subsets per language.| ## Load with Datasets To load this dataset with `datasets`, you'll first need to install it using `pip install datasets` and then use the following code: ```python from datasets import load_dataset # load HF dataset global_mmlu = load_dataset(""CohereForAI/Global-MMLU"", 'en') # can also be used as pandas dataframe global_mmlu.set_format(""pandas"") global_mmlu_test = global_mmlu['test'][:] global_mmlu_dev = global_mmlu['dev'][:] ```

additional details

The columns corresponding to annotations collected from our cultural bias study (i.e. 'required_knowledge', 'time_sensitive', 'reference', 'culture', 'region', 'country') contain a list of values representing annotations from different annotators. However, to avoid conversion issues to HF dataset, these columns are provided as string in the final dataset. You can convert these columns back to list of values for easier manipulation as follows: ```python import ast # convert string values to list global_mmlu_df['required_knowledge'] = global_mmlu_df['required_knowledge'].apply(lamda x: ast.literal_eval(x)) ```

## Data Fields The data fields are the same among all splits. Brief description of each field is provided below.

data field description

- `sample_id`: A unique identifier for the question. - `subject`: The main topic the question falls under. - `subject_category`: The high-level category the subject falls under i.e. STEM/Humanities/Social Sciences/Medical/Business/Other. - `question`: translated question from MMLU - `option_a`: one of the possible option choices - `option_b`: one of the possible option choices - `option_c`: one of the possible option choices - `option_d`: one of the possible option choices - `answer': the correct answer (A/B/C/D) - `required_knowledge`: annotator votes for knowledge needed to answer the question correctly. Possible values include: ""cultural"", ""regional"", ""dialect"" or ""none"" - `time_sensitive`: annotator votes indicating if the question's answer is time-dependent. Possible values include: Yes/No - `reference`: annotations for which part of the question contains cultural/regional/dialect references. The different items in the list are annotations from different annotators. - `culture`: annotations for which culture does the question belong to. The different items in the list correspond to annotations from different annotators. - `region`: Geographic region the question is relevant to. Each item in the list correspond to annotations from different annotators. - `country`: Specific country the question pertains to. Each item in the list correspond to annotations from different annotators. - `cultural_sensitivity_label`: Label to indicate if question is culturally sensitive (CS) or culturally agnostic (CA) based on annotator votes. - `is_annotated`: True/False flag to indicate if sample contains any annotations from our cultural bias study.

## Data Splits The following are the splits of the data: | Split | No. of instances | Language Coverage | |-------|------------------|-------------------| | test | 589,764 | 42 | | dev | 11,970 | 42 | ## Data Instances An example from `test` set looks as follows: ```json {'sample_id': 'world_religions/test/170' 'subject': 'world_religions', 'subject_category': 'Humanities', 'question': ' The numen of Augustus referred to which of the following characteristics?', 'option_a': 'Divine power', 'option_b': 'Sexual virility', 'option_c': 'Military acumen', 'option_d': 'Philosophical intellect', 'answer': 'A', 'required_knowledge': ""['none', 'cultural', 'cultural', 'cultural']"", 'time_sensitive': ""['No', 'No', 'No', 'No']"", 'reference': ""['-', '-', {'end': 22, 'label': 'Cultural', 'score': None, 'start': 5}, {'end': 22, 'label': 'Cultural', 'score': None, 'start': 5}]"", 'culture': ""['Western Culture', 'Western Culture', 'Western Culture']"", 'region': ""['North America', 'Europe']"", 'country': ""['Italy']"", 'cultural_sensitivity_label': 'CS', 'is_annotated': True, } ``` ## Statistics ### Annotation Types The following is the breakdown of CS🗽, CA⚖️ and MA📝 samples in the final dataset. | Type of Annotation | Instances per language | No. of languages | Total instances |--------------------|------------------------|------------------|----------------| | Culturally Sensitive 🗽 | 792 | 42 | 33,264 | | Culturally Agnostic ⚖️ | 2058 |42 | 86,436 | | MMLU Annotated 📝| 2850 |42 | 119,700 | ### Languages The dataset covers 42 languages: 20 high-resource, 9 mid-resource, and 13 low-resource languages. The following is details about the languages included in the dataset.

Languages Info

| ISO Code | Language | Resources | |----------|----------|-----------| | `am` | Amharic | Low | | `ar` | Arabic (Standard)| High | | `bn` | Bengali | Mid | | `de` | German | High | | `el` | Greek | Mid | | `en` | English | High | | `fil` | Filipino | Mid | | `fr` | French | High | | `ha` | Hausa | Low | | `he` | Hebrew | Mid | | `hi` | Hindi | High | | `ig` | Igbo | Low | | `id` | Indonesian | Mid | | `it` | Italian | High | | `ja` | Japanese | High | | `ky` | Kyrgyz | Low | | `ko` | Korean | Mid | | `lt` | Lithuanian | Mid | | `mg` | Malagasy | Low | | `ms` | Malay | Mid | | `ne` | Nepali | Low | | `nl` | Dutch | High | | `ny` | Chichewa | Low | | `fa` | Persian | High | | `pl` | Polish | High | | `pt` | Portuguese | High | | `ru` | Russian | High | | `si` | Sinhala | Low | | `sn` | Shona | Low | | `so` | Somali | Low | | `es` | Spanish | High | | `sr` | Serbian | High | | `sw` | Swahili | Low | | `sw` | Swedish | High | | `te` | Telugu | Low | | `tr` | Turkish | High | | `uk` | Ukrainian | Mid | | `vi` | Vietnamese | High | | `yo` | Yorùbá | Low | | `zh` | Chinese (Simplified) | High |

# Known Limitations A brief overview of limitations of this dataset is provided below.

show limitations

- **Language and dialect coverage:** Global-MMLU focusses on 42 languages. However, this is still only a tiny fraction of the world’s linguistic diversity. Future work is needed to continue to improve evaluations beyond these 42 languages and take into account how technology serves different dialects. - **Uneven distribution of contributions:** The dataset contains translation post-edits from community volunteers, with a 'long tail' of volunteers making only one or two contributions. Similarly, there is a huge gap between languages with the highest number of contributions and ones with the lowest number of contributions. - **Toxic or offensive speech:** Our annotation process did not focus on flagging for toxic,harmful, or offensive speech, so it is possible that Global-MMLU contains some data that could be considered harmful. We believe this is of relatively low risk because of the nature of the original MMLU and the focus on examination material. - **Region Category Assignment:** For the annotation of geographically sensitive questions, we classified regions into six geographic regions (Africa, Asia, Europe, North America, Oceania,and South America). However, based upon discussions we would going forward recommend switching to the taxonomy proposed by the World Bank which is more granular and includes separate designations for Central America and Sub-Saharan Africa. - **Identifying cultural sensitivity does not guarantee cultural inclusion:** While Global-MMLU highlights important limitations in current datasets by identifying gaps in non-Western cultural representation. Future work must prioritize the integration of diverse culturally grounded knowledge to achieve true inclusivity and fairness in multilingual AI evaluation.

CLIcK 🇰🇷🧠

A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean

## Introduction 🎉 CLIcK (Cultural and Linguistic Intelligence in Korean) is a comprehensive dataset designed to evaluate cultural and linguistic intelligence in the context of Korean language models. In an era where diverse language models are continually emerging, there is a pressing need for robust evaluation datasets, especially for non-English languages like Korean. CLIcK fills this gap by providing a rich, well-categorized dataset focusing on both cultural and linguistic aspects, enabling a nuanced assessment of Korean language models. ## News 📰 - **[LREC-COLING]** Our paper introducing CLIcK has been accepted to LREC-COLING 2024!🎉 ## Dataset Description 📊 The CLIcK benchmark comprises two broad categories: Culture and Language, which are further divided into 11 fine-grained subcategories. ### Categories 📂 - **Language** 🗣️ - Textual Knowledge - Grammatical Knowledge - Functional Knowledge - **Culture** 🌍 - Korean Society - Korean Tradition - Korean Politics - Korean Economy - Korean Law - Korean History - Korean Geography - Korean Popular Culture (K-Pop) ### Construction 🏗️ CLIcK was developed using two human-centric approaches: 1. Reclassification of **official and well-designed exam data** into our defined categories. 2. Generation of questions using ChatGPT, based on **official educational materials** from the Korean Ministry of Justice, followed by our own validation process. ### Structure 🏛️ The dataset is organized as follows, with each subcategory containing relevant JSON files: ``` 📦CLIcK └─ Dataset ├─ Culture │ ├─ [Each cultural subcategory with associated JSON files] └─ Language ├─ [Each language subcategory with associated JSON files] ``` ### Exam Code Descriptions 📜 - KIIP: Korea Immigration & Integration Program ([Website](www.immigration.go.kr)) - CSAT: College Scholastic Ability Test for Korean ([Website](https://www.suneung.re.kr/)) - Kedu: Test of Teaching Korean as a Foreign Language exams ([Website](https://www.q-net.or.kr/man001.do?gSite=L&gId=36)) - PSE: Public Service Exam for 9th grade - TOPIK: Test of Proficiency in Korean ([Website](https://www.topik.go.kr/)) - KHB: Korean History Exam Basic ([Website](https://www.historyexam.go.kr/)) - PSAT: Public Service Aptitude Test in Korea ## Results | Models | Average Accuracy (Korean Culture) | Average Accuracy (Korean Language) | |-------------------|-----------------------------------|------------------------------------| | Polyglot-Ko 1.3B | 32.71% | 22.88% | | Polyglot-Ko 3.8B | 32.90% | 22.38% | | Polyglot-Ko 5.8B | 33.14% | 23.27% | | Polyglot-Ko 12.8B | 33.40% | 22.24% | | KULLM 5.8B | 33.79% | 23.50% | | KULLM 12.8B | 33.51% | 23.78% | | KoAlpaca 5.8B | 32.33% | 23.87% | | KoAlpaca 12.8B | 33.80% | 22.42% | | LLaMA-Ko 7B | 33.26% | 25.69% | | LLaMA 7B | 35.44% | 27.17% | | LLaMA 13B | **36.22%** | **26.71%** | | GPT-3.5 | 49.30% | 42.32% | | Claude2 | **51.72%** | **45.39%** | ## Dataset Link 🔗 The CLIcK dataset is available on the Hugging Face Hub: [CLIcK Dataset](https://huggingface.co/datasets/your_username/CLIcK) ## Citation 📝 If you use CLIcK in your research, please cite our paper: ```bibtex @misc{kim2024click, title={CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean}, author={Eunsu Kim and Juyoung Suk and Philhoon Oh and Haneul Yoo and James Thorne and Alice Oh}, year={2024}, eprint={2403.06412}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Contact 📧 For any questions or inquiries, please contact [kes0317@kaist.ac.kr](mailto:kes0317@kaist.ac.kr)." wikimedia/wit_base,"{""annotations_creators"": [""machine-generated""], ""language_creators"": [""found""], ""language"": [""af"", ""an"", ""ar"", ""arz"", ""ast"", ""az"", ""azb"", ""ba"", ""bar"", ""be"", ""bg"", ""bn"", ""br"", ""bs"", ""ca"", ""ce"", ""ceb"", ""ckb"", ""cs"", ""cv"", ""cy"", ""da"", ""de"", ""el"", ""en"", ""eo"", ""es"", ""et"", ""eu"", ""fa"", ""fi"", ""fil"", ""fr"", ""fy"", ""ga"", ""gl"", ""hi"", ""hr"", ""hsb"", ""ht"", ""hu"", ""hy"", ""ia"", ""id"", ""io"", ""is"", ""it"", ""iw"", ""ja"", ""jv"", ""ka"", ""kk"", ""kn"", ""ko"", ""la"", ""lah"", ""lb"", ""lmo"", ""lt"", ""lv"", ""mg"", ""mk"", ""ml"", ""mn"", ""mr"", ""ms"", ""my"", ""nan"", ""nds"", ""ne"", ""nl"", ""nn"", ""no"", ""nv"", ""oc"", ""pa"", ""pl"", ""pt"", ""qu"", ""ro"", ""ru"", ""sco"", ""si"", ""sk"", ""sl"", ""sq"", ""sr"", ""sv"", ""sw"", ""ta"", ""te"", ""tg"", ""th"", ""tr"", ""tt"", ""uk"", ""ur"", ""uz"", ""vec"", ""vi"", ""vo"", ""war"", ""xmf"", ""yue"", ""zh""], ""license"": [""cc-by-sa-4.0""], ""multilinguality"": [""multilingual""], ""size_categories"": [""1M The core training data is taken from the Wikipedia Image-Text (WIT) Dataset, a large curated set of more than 37 million image-text associations extracted from Wikipedia articles in 108 languages that was recently released by Google Research. > > The WIT dataset offers extremely valuable data about the pieces of text associated with Wikipedia images. However, due to licensing and data volume issues, the Google dataset only provides the image name and corresponding URL for download and not the raw image files. > > Getting easy access to the image files is crucial for participants to successfully develop competitive models. Therefore, today, the Wikimedia Research team is releasing its first large image dataset. It contains more than six million image files from Wikipedia articles in 100+ languages, which correspond to almost [1] all captioned images in the WIT dataset. Image files are provided at a 300-px resolution, a size that is suitable for most of the learning frameworks used to classify and analyze images. > [1] We are publishing all images having a non-null “reference description” in the WIT dataset. For privacy reasons, we are not publishing images where a person is the primary subject, i.e., where a person’s face covers more than 10% of the image surface. To identify faces and their bounding boxes, we use the RetinaFace detector. In addition, to avoid the inclusion of inappropriate images or images that violate copyright constraints, we have removed all images that are candidate for deletion on Commons from the dataset. **Note**: Compared to [Google's version](https://huggingface.co/datasets/google/wit), which has contents of one Wikipedia page per data sample, this version groups contents of all Wikipedia pages available in different languages for the image in one single data sample to avoid duplication of image bytes. ### Supported Tasks and Leaderboards - `image-captioning`: This dataset can be used to train a model for image captioning where the goal is to predict a caption given the image. - `text-retrieval`: The goal in this task is to build a model that retrieves the text (`caption_title_and_reference_description`) closest to an image. The leaderboard for this task can be found [here](https://paperswithcode.com/sota/text-image-retrieval-on-wit). This task also has a competition on [Kaggle](https://www.kaggle.com/c/wikipedia-image-caption). In these tasks, any combination of the `caption_reference_description`, `caption_attribution_description` and `caption_alt_text_description` fields can be used as the input text/caption. ### Languages The dataset contains examples from all Wikipedia languages. ## Dataset Structure ### Data Instances Each instance is an image, its representation in bytes, a pre-computed embedding, and the set of captions attached to the image in Wikipedia. ``` { 'image': , 'image_url': 'https://upload.wikimedia.org/wikipedia/commons/8/8b/Scolopendra_gigantea.jpg', 'embedding': [1.4784087, 2.8710432, 0.0, 0.51603067, ..., 10.266883, 0.51142216, 0.0, 2.3464653], 'metadata_url': 'http://commons.wikimedia.org/wiki/File:Scolopendra_gigantea.jpg', 'original_height': 3000, 'original_width': 4000, 'mime_type': 'image/jpeg', 'caption_attribution_description': 'English: Puerto Rican Giant Centipede, Scolopendra gigantea; Vieques, Puerto Rico Slovenčina: Stonožka obrovská, Scolopendra gigantea; Vieques, Portoriko', 'wit_features': { 'language': ['ro', 'vi', 'sk', ..., 'nl', 'th', 'lv'], 'page_url': ['https://ro.wikipedia.org/wiki/Scolopendra_gigantea', 'https://vi.wikipedia.org/wiki/Scolopendra_gigantea', 'https://sk.wikipedia.org/wiki/Scolopendra_gigantea', ..., 'https://nl.wikipedia.org/wiki/Scolopendra_gigantea', 'https://th.wikipedia.org/wiki/%E0%B8%95%E0%B8%B0%E0%B8%82%E0%B8%B2%E0%B8%9A%E0%B8%A2%E0%B8%B1%E0%B8%81%E0%B8%A9%E0%B9%8C%E0%B8%82%E0%B8%B2%E0%B9%80%E0%B8%AB%E0%B8%A5%E0%B8%B7%E0%B8%AD%E0%B8%87%E0%B9%80%E0%B8%9B%E0%B8%A3%E0%B8%B9', 'https://lv.wikipedia.org/wiki/Skolopendru_dzimta'], 'attribution_passes_lang_id': [True, True, True, ..., True, True, True], 'caption_alt_text_description': [None, None, None, ..., 'Scolopendra gigantea', None, 'Milzu skolopendra (Scolopendra gigantea)'], 'caption_reference_description': [None, None, None, ..., None, None, 'Milzu skolopendra (Scolopendra gigantea)'], 'caption_title_and_reference_description': [None, 'Scolopendra gigantea [SEP] ', None, ..., 'Scolopendra gigantea [SEP] ', None, 'Skolopendru dzimta [SEP] Milzu skolopendra (Scolopendra gigantea)'], 'context_page_description': ['Scolopendra gigantea este un miriapod din clasa Chilopoda, fiind cel mai mare reprezentant al genului Scolopendra. Adultul poate atinge o lungime de 26 cm, uneori depășind 30 cm. Această specie habitează în regiunile de nord și de vest a Americii de Sud, pe insulele Trinidad, insulele Virgine, Jamaica Hispaniola ș.a. Localnicii denumesc scolopendra chilopodul gigant galben și chilopodul gigant amazonian.', 'Scolopendra gigantea là đại diện lớn nhất của chi Scolopendra nói riêng và cả lớp rết nói chung, thường đạt độ dài 26 cm và có thể vượt quá 30 cm. Sinh sống ở khu vực phía bắc và tây của Nam Mỹ và các đảo Trinidad, Puerto Rico, Saint Thomas, U.S. Virgin Islands, Jamaica, và Hispaniola.', 'Scolopendra gigantea, starší slovenský nazov: štípavica veľká, je živočích z rodu Scolopendra, s veľkosťou do 30 cm.', ..., 'Scolopendra gigantea is een tijgerduizendpoot uit Zuid-Amerika. De soort jaagt onder andere op grote geleedpotigen, amfibieën, reptielen en kleine zoogdieren. Het is voor zover bekend de grootste niet uitgestorven duizendpoot ter wereld.', 'ตะขาบยักษ์ขาเหลืองเปรู หรือ ตะขาบยักษ์อเมซอน เป็นตะขาบชนิดที่มีขนาดใหญ่ที่สุดในสกุล Scolopendra โดยปกติเมื่อโตเต็มที่จะยาว 26 เซนติเมตร แต่บางครั้งก็สามารถโตได้ถึง 30 เซนติเมตร ตะขาบชนิดนี้อาศัยอยู่ทางแถบเหนือและตะวันตกของทวีปอเมริกาใต้ และตามเกาะแก่งของประเทศตรินิแดดและจาไมกา เป็นสัตว์กินเนื้อ โดยกินจิ้งจก, กบ, นก, หนู และแม้แต่ค้างคาวเป็นอาหาร และขึ้นชื่อในเรื่องความดุร้าย', 'Skolpendru dzimta pieder pie simtkāju kārtas. Ap 400 dzimtas sugas sastopamas visā pasaulē, īpaši subtropu un tropu apgabalos. Mitinās augsnē, nobirušās lapās, plaisās, spraugās.'], 'context_section_description': [None, 'Scolopendra gigantea (còn được gọi là Rết chân vàng khổng lồ Peru và Rết khổng lồ Amazon) là đại diện lớn nhất của chi Scolopendra nói riêng và cả lớp rết nói chung, thường đạt độ dài 26\xa0cm (10\xa0in) và có thể vượt quá 30\xa0cm (12\xa0in). Sinh sống ở khu vực phía bắc và tây của Nam Mỹ và các đảo Trinidad, Puerto Rico, Saint Thomas, U.S. Virgin Islands, Jamaica, và Hispaniola.', None, ..., 'Scolopendra gigantea is een tijgerduizendpoot uit Zuid-Amerika. De soort jaagt onder andere op grote geleedpotigen, amfibieën, reptielen en kleine zoogdieren. Het is voor zover bekend de grootste niet uitgestorven duizendpoot ter wereld.', None, 'Skolpendru dzimta (Scolopendridae) pieder pie simtkāju kārtas. Ap 400 dzimtas sugas sastopamas visā pasaulē, īpaši subtropu un tropu apgabalos. Mitinās augsnē, nobirušās lapās, plaisās, spraugās.'], 'hierarchical_section_title': ['Scolopendra gigantea', 'Scolopendra gigantea', 'Scolopendra gigantea', ..., 'Scolopendra gigantea', 'ตะขาบยักษ์ขาเหลืองเปรู', 'Skolopendru dzimta'], 'is_main_image': [True, True, True, ..., True, True, True], 'page_title': ['Scolopendra gigantea', 'Scolopendra gigantea', 'Scolopendra gigantea', ..., 'Scolopendra gigantea', 'ตะขาบยักษ์ขาเหลืองเปรู', 'Skolopendru dzimta'], 'section_title': [None, None, None, ..., None, None, None] } } ``` **Note**: The dataset is stored in Parquet for better performance. This dataset was generated from the original files using [this script](wit_base/blob/main/scripts/wit.py). Additionally, 120 examples from the original files have incorrectly formatted one or more of the following fields: `original_height`, `original_width`, `mime_type` and `caption_attribution_description`. The fixed versions of these examples that were used in the generation script can be found [here](wit_base/blob/main/scripts/corrected_examples.py). ### Data Fields - `image`: A `PIL.Image.Image` object containing the image resized to a width of 300-px while preserving its aspect ratio. Note that when accessing the image column: `dataset[0][""image""]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `""image""` column, *i.e.* `dataset[0][""image""]` should **always** be preferred over `dataset[""image""][0]`. - `image_url`: URL to wikipedia image - `embedding`: Precomputed image embedding. Each image is described with a 2048-dimensional signature extracted from the second-to-last layer of a [ResNet-50](https://arxiv.org/abs/1512.03385) neural network trained with [Imagenet](https://www.image-net.org/) data. These embeddings contain rich information about the image content and layout, in a compact form - `metadata_url`: URL to wikimedia page containing the image and the metadata - `original_height`: Original image height before resizing - `original_width`: Original image width before resizing - `mime_type`: Mime type associated to the image - `caption_attribution_description`: This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias. - `wit_features`: Sequence of captions for the image with language, page URL, information about the page, caption text, etc. - `language`: Language code depicting wikipedia language of the page - `page_url`: URL to wikipedia page - `attribution_passes_lang_id`: Compared `language` field with the attribution language (written in the prefix of the attribution description. - `caption_alt_text_description`: This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers - `caption_reference_description`: This is the caption that is visible on the wikipedia page directly below the image. - `caption_title_and_reference_description`: Concatenation of `page_title` and `caption_reference_description`. - `context_page_description`: Corresponds to the short description of the page. It provides a concise explanation of the scope of the page. - `context_section_description`: Text within the image's section - `hierarchical_section_title`: Hierarchical section's title - `is_main_image`: Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers. - `page_changed_recently`: [More Information Needed] - `page_title`: Wikipedia page's title - `section_title`: Section's title

Figure: WIT annotation example.

Details on the field content can be found directly in the [paper, figure 5 and table 12.](https://arxiv.org/abs/2103.01913) ### Data Splits All data is held in `train` split, with a total of 6477255 examples. ## Dataset Creation ### Curation Rationale From the [official blog post](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/): > The WIT dataset offers extremely valuable data about the pieces of text associated with Wikipedia images. > Getting easy access to the image files is crucial for participants to successfully develop competitive models. > With this large release of visual data, we aim to help the competition participants—as well as researchers and practitioners who are interested in working with Wikipedia images—find and download the large number of image files associated with the challenge, in a compact form. ### Source Data #### Initial Data Collection and Normalization From the [paper, section 3.1](https://arxiv.org/abs/2103.01913): > We started with all Wikipedia content pages (i.e., ignoring other pages that have discussions, comments and such). These number about ~124M pages across 279 languages. #### Who are the source language producers? Text was extracted from Wikipedia. ### Annotations #### Annotation process WIT was constructed using an automatic process. However it was human-validated. From the [paper, section 3.7](https://arxiv.org/abs/2103.01913): > To further verify the quality of the WIT dataset we performed a study using (crowd-sourced) human annotators. As seen in Fig. 3, we asked raters to answer 3 questions. Given an image and the page title, raters first evaluate the quality of the attribution description and reference description in the first two questions (order randomized). The third question understands the contextual quality of these text descriptions given the page description and caption. Each response is on a 3-point scale: ""Yes"" if the text perfectly describes the image, ""Maybe"" if it is sufficiently explanatory and ""No"" if it is irrelevant or the image is inappropriate. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information From the [official blog post](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/#FN1): > For privacy reasons, we are not publishing images where a person is the primary subject, i.e., where a person’s face covers more than 10% of the image surface. To identify faces and their bounding boxes, we use the [RetinaFace](https://arxiv.org/abs/1905.00641) detector. In addition, to avoid the inclusion of inappropriate images or images that violate copyright constraints, we have removed all images that are [candidate for deletion](https://commons.wikimedia.org/wiki/Commons:Deletion_requests) on Commons from the dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases From the [paper, section 3.4](https://arxiv.org/abs/2103.01913): > Lastly we found that certain image-text pairs occurred very frequently. These were often generic images that did not have much to do with the main article page. Common examples included flags, logos, maps, insignia and such. To prevent biasing the data, we heavily under-sampled all such images ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Miriam Redi, Fabian Kaelin and Tiziano Piccardi. ### Licensing Information [CC BY-SA 4.0 international license](https://creativecommons.org/licenses/by-sa/4.0/) ### Citation Information ```bibtex @article{srinivasan2021wit, title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning}, author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc}, journal={arXiv preprint arXiv:2103.01913}, year={2021} } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw), [yjernite](https://github.com/yjernite) and [mariosasko](https://github.com/mariosasko) for adding this dataset." cis-lmu/GlotCC-V1,"{""license"": ""cc0-1.0"", ""configs"": [{""config_name"": ""default"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/*/*.parquet""}]}, {""config_name"": ""eng-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/eng-Latn/*.parquet""}]}, {""config_name"": ""rus-Cyrl"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/rus-Cyrl/*.parquet""}]}, {""config_name"": ""fra-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/fra-Latn/*.parquet""}]}, {""config_name"": ""spa-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/spa-Latn/*.parquet""}]}, {""config_name"": ""deu-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/deu-Latn/*.parquet""}]}, {""config_name"": ""pol-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/pol-Latn/*.parquet""}]}, {""config_name"": ""vie-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/vie-Latn/*.parquet""}]}, {""config_name"": ""ita-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/ita-Latn/*.parquet""}]}, {""config_name"": ""nld-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/nld-Latn/*.parquet""}]}, {""config_name"": ""por-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/por-Latn/*.parquet""}]}, {""config_name"": ""ces-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/ces-Latn/*.parquet""}]}, {""config_name"": ""fas-Arab"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/fas-Arab/*.parquet""}]}, {""config_name"": ""tur-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/tur-Latn/*.parquet""}]}, {""config_name"": ""tha-Thai"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/tha-Thai/*.parquet""}]}, {""config_name"": ""ind-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/ind-Latn/*.parquet""}]}, {""config_name"": ""cmn-Hani"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/cmn-Hani/*.parquet""}]}, {""config_name"": ""hun-Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""v1.0/hun-Latn/*.parquet""}]}, {""config_name"": ""ell-Grek"", 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""nyk"", ""nyn"", ""nyu"", ""nyy"", ""nzi"", ""nzm"", ""obo"", ""oc"", ""oci"", ""ojb"", ""oji"", ""okv"", ""olo"", ""omb"", ""omw"", ""ong"", ""opm"", ""ori"", ""orm"", ""orv"", ""ory"", ""os"", ""oss"", ""ota"", ""otd"", ""ote"", ""ots"", ""otw"", ""pa"", ""pad"", ""pag"", ""pam"", ""pan"", ""pao"", ""pap"", ""pau"", ""pbb"", ""pbt"", ""pcd"", ""pck"", ""pcm"", ""pdc"", ""pdt"", ""pes"", ""pfl"", ""pib"", ""pis"", ""pjt"", ""pl"", ""plg"", ""pls"", ""plt"", ""plu"", ""plw"", ""pma"", ""pmf"", ""pms"", ""pmx"", ""pnb"", ""pnt"", ""poe"", ""poh"", ""poi"", ""pol"", ""pon"", ""por"", ""pot"", ""ppk"", ""ppo"", ""pps"", ""prg"", ""prs"", ""pt"", ""ptp"", ""ptu"", ""pui"", ""pus"", ""pwg"", ""pwn"", ""qub"", ""quc"", ""que"", ""quf"", ""qug"", ""quh"", ""qul"", ""qup"", ""quw"", ""quy"", ""quz"", ""qvc"", ""qvh"", ""qvi"", ""qvn"", ""qvs"", ""qvw"", ""qxl"", ""qxn"", ""qxo"", ""rad"", ""raj"", ""rap"", ""rar"", ""raw"", ""rcf"", ""rej"", ""rgu"", ""rhg"", ""ria"", ""rif"", ""rjs"", ""rkb"", ""rm"", ""rmc"", ""rml"", ""rmn"", ""rmo"", ""rmq"", ""rmy"", ""rn"", ""rnl"", ""ro"", ""roh"", ""rom"", ""ron"", ""roo"", ""rop"", ""row"", ""rro"", ""rtm"", ""ru"", ""rue"", ""ruf"", ""rug"", ""run"", ""rup"", ""rus"", ""rw"", ""rwo"", ""sa"", ""sab"", ""sag"", ""sah"", ""san"", ""sas"", ""sat"", ""sbd"", ""sbe"", ""sc"", ""scn"", ""sco"", ""sd"", ""sda"", ""sdc"", ""sdh"", ""se"", ""seh"", ""ses"", ""sg"", ""sgb"", ""sgc"", ""sgh"", ""sgs"", ""sgw"", ""shi"", ""shk"", ""shn"", ""shp"", ""shu"", ""si"", ""sid"", ""sil"", ""sim"", ""sin"", ""sk"", ""skg"", ""skr"", ""sl"", ""slk"", ""sll"", ""slv"", ""sm"", ""sma"", ""sme"", ""smj"", ""smk"", ""sml"", ""smn"", ""smo"", ""sms"", ""sn"", ""sna"", ""snc"", ""snd"", ""snf"", ""snn"", ""snp"", ""snw"", ""sny"", ""so"", ""som"", ""sop"", ""soq"", ""sot"", ""spa"", ""spl"", ""spm"", ""spp"", ""sps"", ""sqi"", ""sr"", ""srd"", ""srm"", ""srn"", ""srp"", ""srr"", ""ss"", ""ssd"", ""ssg"", ""ssw"", ""ssx"", ""st"", ""stq"", ""su"", ""suc"", ""sue"", ""suk"", ""sun"", ""sus"", ""suz"", ""sv"", ""swa"", ""swb"", ""swc"", ""swe"", ""swg"", ""swh"", ""swp"", ""sxb"", ""sxn"", ""sxw"", ""syb"", ""syc"", ""syl"", ""syr"", ""szl"", ""szy"", ""ta"", ""tab"", ""tah"", ""taj"", ""tam"", ""tap"", ""taq"", ""tar"", ""tat"", ""taw"", ""tay"", ""tbg"", ""tbo"", ""tbw"", ""tby"", ""tbz"", ""tca"", ""tcs"", ""tcy"", ""tcz"", ""tdt"", ""tdx"", ""te"", ""tel"", ""teo"", ""tet"", ""tfr"", ""tg"", ""tgk"", ""tgo"", ""tgp"", ""th"", ""tha"", ""thl"", ""ti"", ""tif"", ""tig"", ""tih"", ""tir"", ""tiv"", ""tk"", ""tke"", ""tkl"", ""tkr"", ""tku"", ""tlb"", ""tlf"", ""tlh"", ""tll"", ""tly"", ""tmd"", ""tmh"", ""tn"", ""tnc"", ""tnk"", ""tnn"", ""tnp"", ""tnr"", ""to"", ""tob"", ""tod"", ""tog"", ""toi"", ""toj"", ""tok"", ""ton"", ""too"", ""top"", ""tos"", ""tpi"", ""tr"", ""trc"", ""trn"", ""trp"", ""trq"", ""trv"", ""ts"", ""tsg"", ""tsn"", ""tso"", ""tsz"", ""tt"", ""ttc"", ""tte"", ""ttq"", ""tuc"", ""tuf"", ""tui"", ""tuk"", ""tum"", ""tur"", ""tvk"", ""tvl"", ""tw"", ""twi"", ""twu"", ""txu"", ""ty"", ""tyv"", ""tzh"", ""tzj"", ""tzm"", ""tzo"", ""ubr"", ""ubu"", ""udm"", ""ug"", ""uig"", ""uk"", ""ukr"", ""umb"", ""und"", ""upv"", ""ur"", ""urd"", ""urh"", ""usa"", ""usp"", ""uvh"", ""uvl"", ""uzb"", ""uzn"", ""uzs"", ""vap"", ""ve"", ""vec"", ""ven"", ""vep"", ""vi"", ""vid"", ""vie"", ""vls"", ""vmw"", ""vmy"", ""vo"", ""vol"", ""vro"", ""vun"", ""wa"", ""waj"", ""wal"", ""war"", ""wat"", ""way"", ""wbm"", ""wbp"", ""wed"", ""wes"", ""wln"", ""wls"", ""wlv"", ""wlx"", ""wmw"", ""wnc"", ""wnu"", ""wo"", ""wol"", ""wos"", ""wrs"", ""wsg"", ""wsk"", ""wuu"", ""wuv"", ""xal"", ""xbi"", ""xh"", ""xho"", ""xla"", ""xmf"", ""xmm"", ""xmv"", ""xog"", ""xon"", ""xsm"", ""xsr"", ""xtd"", ""xtm"", ""xtn"", ""yaa"", ""yal"", ""yao"", ""yap"", ""yby"", ""ydd"", ""yid"", ""yka"", ""yle"", ""yli"", ""yml"", ""yo"", ""yom"", ""yon"", ""yor"", ""yrk"", ""yrl"", ""yss"", ""yua"", ""yue"", ""yuj"", ""yut"", ""yuw"", ""yva"", ""zac"", ""zai"", ""zam"", ""zao"", ""zap"", ""zas"", ""zat"", ""zdj"", ""zea"", ""zgh"", ""zha"", ""zho"", ""zia"", ""zom"", ""zos"", ""zpm"", ""zpo"", ""zpt"", ""zpu"", ""zsm"", ""zu"", ""zul"", ""zyb"", ""zyp"", ""zza""]}","## Dataset Description - **Repository:** [https://github.com/cisnlp/GlotCC](https://github.com/cisnlp/GlotCC) - **Paper:** [https://arxiv.org/abs/2410.23825](https://arxiv.org/abs/2410.23825) - **Point of Contact:** [amir@cis.lmu.de](mailto:amir@cis.lmu.de) ### Dataset Summary **GlotCC-V1.0** is a document-level, general domain dataset derived from CommonCrawl, covering more than **1000** languages. It is built using the [GlotLID](https://github.com/cisnlp/GlotLID) language identification and [Ungoliant](https://github.com/kargaranamir/ungoliant) pipeline from CommonCrawl. We release our pipeline as open-source at [https://github.com/cisnlp/GlotCC](https://github.com/cisnlp/GlotCC). **List of Languages:** See [https://datasets-server.huggingface.co/splits?dataset=cis-lmu/GlotCC-V1](https://datasets-server.huggingface.co/splits?dataset=cis-lmu/GlotCC-V1) to get the list of splits available. ### Usage (Huggingface Hub -- Recommended) Replace `bal-Arab` with your specific language. ```python from huggingface_hub import snapshot_download folder = snapshot_download( ""cis-lmu/glotcc-v1"", repo_type=""dataset"", local_dir=""./path/to/glotcc-v1/"", # Replace ""v1.0/bal-Arab/*"" with the path for any other language available in the dataset allow_patterns=""v1.0/bal-Arab/*"" ) ``` For faster downloads, make sure to `pip install huggingface_hub[hf_transfer]` and set the environment variable `HF_HUB_ENABLE_HF_TRANSFER`=1. Then you can load it with any library that supports Parquet files, such as Pandas: ```python import pandas as pd # Load the dataset from a Parquet file # Replace the file path with the path to the desired language's Parquet file dataset = pd.read_parquet('./path/to/glotcc-v1/v1.0/bal-Arab/bal-Arab_0.parquet') # Print the first 5 rows of the dataset print(dataset.head()) ``` ### Usage (Huggingface datasets) ```python from datasets import load_dataset # Replace ""bal-Arab"" with the name of any other language available in the dataset dataset = load_dataset(""cis-lmu/glotcc-v1"", name=""bal-Arab"", split=""train"") # Print the first row of data print(dataset[0]) ``` ### Usage (Huggingface datasets -- streaming=True) ```python from datasets import load_dataset # Replace ""bal-Arab"" with the name of any other language available in the dataset fw = load_dataset(""cis-lmu/glotcc-v1"", name=""bal-Arab"", split=""train"", streaming=True) # Create an iterator from the streaming dataset iterator = iter(fw) # Print the next item from the iterator print(next(iterator)) ``` ### Usage (direct download) If you prefer not to use the Hugging Face datasets or hub you can download it directly. For example, to download the first file of `bal-Arab`: ```python !wget https://huggingface.co/datasets/cis-lmu/GlotCC-V1/resolve/main/v1.0/bal-Arab/bal-Arab_0.parquet ``` ## Additional Information The dataset is currently heavily under audit and changes accordingly. ### Licensing Information GlotCC data is released under the following licensing scheme: We do not own any of the text from which this data has been extracted. The data is licensed under the terms of the CommonCrawl [Terms of Use](https://commoncrawl.org/terms-of-use). We license the actual packaging, metadata, and annotations of this data under the Creative Commons [CC0 license](https://github.com/cisnlp/GlotCC/blob/main/LICENSE). ### Citation Information If you find our data useful for your research, please cite: ``` @article{kargaran2024glotcc, title = {Glot{CC}: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages}, author = {Kargaran, Amir Hossein and Yvon, Fran{\c{c}}ois and Sch{\""u}tze, Hinrich}, journal = {Advances in Neural Information Processing Systems}, year = {2024}, url = {https://arxiv.org/abs/2410.23825} } ```" simon3000/genshin-voice,"{""language"": [""zh"", ""en"", ""ja"", ""ko""], ""task_categories"": [""audio-classification"", ""automatic-speech-recognition"", ""text-to-speech""], ""pretty_name"": ""Genshin Voice"", ""dataset_info"": {""features"": [{""name"": ""audio"", ""dtype"": ""audio""}, {""name"": ""transcription"", ""dtype"": ""string""}, {""name"": ""language"", ""dtype"": ""string""}, {""name"": ""speaker"", ""dtype"": ""string""}, {""name"": ""speaker_type"", ""dtype"": ""string""}, {""name"": ""type"", ""dtype"": ""string""}, {""name"": ""inGameFilename"", ""dtype"": ""string""}], ""splits"": [{""name"": ""train"", ""num_bytes"": 264598217401.752, ""num_examples"": 463383}], ""download_size"": 227704444125, ""dataset_size"": 264598217401.752}, ""configs"": [{""config_name"": ""default"", ""data_files"": [{""split"": ""train"", ""path"": ""data/train-*""}]}]}","# Genshin Voice Genshin Voice is a dataset of voice lines from the popular game [Genshin Impact](https://genshin.hoyoverse.com/). Hugging Face 🤗 [Genshin-Voice](https://huggingface.co/datasets/simon3000/genshin-voice) Last update at `2024-08-30` `463383` wavs `20231` without speaker (4%) `24819` without transcription (5%) `602` without inGameFilename (0%) ## Dataset Details ### Dataset Description The dataset contains voice lines from the game's characters in multiple languages, including Chinese, English, Japanese, and Korean. The voice lines are spoken by the characters in the game and cover a wide range of topics, including greetings, combat, and story dialogue. - **Language(s) (NLP):** Chinese, English, Japanese, Korean ## Uses To install Hugging Face's datasets library, follow the instructions from [this link](https://huggingface.co/docs/datasets/installation#audio). ### Example: Load the dataset and filter for Chinese voices of Ganyu with transcriptions ```python from datasets import load_dataset import soundfile as sf import os # Load the dataset dataset = load_dataset('simon3000/genshin-voice', split='train', streaming=True) # Filter the dataset for Chinese voices of Ganyu with transcriptions chinese_ganyu = dataset.filter(lambda voice: voice['language'] == 'Chinese' and voice['speaker'] == 'Ganyu' and voice['transcription'] != '') # Create a folder to store the audio and transcription files ganyu_folder = 'ganyu' os.makedirs(ganyu_folder, exist_ok=True) # Process each voice in the filtered dataset for i, voice in enumerate(chinese_ganyu): audio_path = os.path.join(ganyu_folder, f'{i}_audio.wav') # Path to save the audio file transcription_path = os.path.join(ganyu_folder, f'{i}_transcription.txt') # Path to save the transcription file # Save the audio file sf.write(audio_path, voice['audio']['array'], voice['audio']['sampling_rate']) # Save the transcription file with open(transcription_path, 'w') as transcription_file: transcription_file.write(voice['transcription']) print(f'{i} done') # Print the progress ``` ### You unpacked the game and just want to know what the wavs are about result.json format: (subject to change) ```json { ""9b5502fb1b83cb97.wav"": { ""inGameFilename"": ""VO_friendship\\VO_raidenShogun\\vo_raidenEi_dialog_pendant.wem"", ""filename"": ""9b5502fb1b83cb97.wav"", ""language"": ""English(US)"", ""transcription"": ""Really? So in all this time, no new Electro Visions have appeared in the outside world? Well, what I can say on this topic is subject to certain constraints, but... it is not by my will that Visions are granted or denied. The key is people's desire, and... well, there's another side to it too."", ""speaker"": ""Raiden Shogun"", ""talkRoleType"": """", ""talkRoleID"": """", ""guid"": ""f8e72b65-6c0a-4df1-a2f0-2bb08dbeab75"", ""voiceConfigs"": [ { ""gameTrigger"": ""Fetter"", ""gameTriggerArgs"": 3001, ""avatarName"": ""Switch_raidenShogun"" } ] } } ``` ## Dataset Creation ### Source Data The data was obtained by unpacking the [Genshin Impact](https://genshin.hoyoverse.com/) game. #### Data Collection and Processing Please refer to [Genshin-Voice](https://github.com/simon300000/genshin-voice) and [w4123/GenshinVoice](https://github.com/w4123/GenshinVoice) for more information on how the data was processed. #### Who are the source data producers? The source data producers are the developers of the game, miHoYo. ### Annotations The dataset contains official annotations from the game, including language, speaker name, and transcription. ## Bias, Risks, and Limitations Annotations are incomplete. Some voice lines are missing speaker names and transcriptions. Speakers and transcriptions may contain markups and placeholders: `#パイモン：{NICKNAME}、すごく怖い悪夢を見たことってあるか？\\n{NICKNAME}：...` ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. Speaker names can be partially inferred from the ingame filenames. ## Licensing Information Copyright © COGNOSPHERE. All Rights Reserved. ## More Information I can upload wav files on demand." WueNLP/sib-fleurs,"{""license"": ""cc-by-sa-4.0"", ""language"": [""ace"", ""acm"", ""acq"", ""aeb"", ""af"", ""ajp"", ""ak"", ""als"", ""am"", ""apc"", ""ar"", ""ars"", ""ary"", ""arz"", ""as"", ""ast"", ""awa"", ""ayr"", ""azb"", ""azj"", ""ba"", ""bm"", ""ban"", ""be"", ""bem"", ""bn"", ""bho"", ""bjn"", ""bo"", ""bs"", ""bug"", ""bg"", ""ca"", ""ceb"", ""cs"", ""cjk"", ""ckb"", ""crh"", ""cy"", ""da"", ""de"", ""dik"", ""dyu"", ""dz"", ""el"", ""en"", ""eo"", ""et"", ""eu"", ""ee"", ""fo"", ""fj"", ""fi"", ""fon"", ""fr"", ""fur"", ""fuv"", ""gaz"", ""gd"", ""ga"", ""gl"", ""gn"", ""gu"", ""ht"", ""ha"", ""he"", ""hi"", ""hne"", ""hr"", ""hu"", ""hy"", ""ig"", ""ilo"", ""id"", ""is"", ""it"", ""jv"", ""ja"", ""kab"", ""kac"", ""kam"", ""kn"", ""ks"", ""ka"", ""kk"", ""kbp"", ""kea"", ""khk"", ""km"", ""ki"", ""rw"", ""ky"", ""kmb"", ""kmr"", ""knc"", ""kg"", ""ko"", ""lo"", ""lij"", ""li"", ""ln"", ""lt"", ""lmo"", ""ltg"", ""lb"", ""lua"", ""lg"", ""luo"", ""lus"", ""lvs"", ""mag"", ""mai"", ""ml"", ""mar"", ""min"", ""mk"", ""mt"", ""mni"", ""mos"", ""mi"", ""my"", ""nl"", ""nn"", ""nb"", ""npi"", ""nqo"", ""nso"", ""nus"", ""ny"", ""oc"", ""ory"", ""pag"", ""pa"", ""pap"", ""pbt"", ""pes"", ""plt"", ""pl"", ""pt"", ""prs"", ""quy"", ""ro"", ""rn"", ""ru"", ""sg"", ""sa"", ""sat"", ""scn"", ""shn"", ""si"", ""sk"", ""sl"", ""sm"", ""sn"", ""sd"", ""so"", ""st"", ""es"", ""sc"", ""sr"", ""ss"", ""su"", ""sv"", ""swh"", ""szl"", ""ta"", ""taq"", ""tt"", ""te"", ""tg"", ""tl"", ""th"", ""ti"", ""tpi"", ""tn"", ""ts"", ""tk"", ""tum"", ""tr"", ""tw"", ""tzm"", ""ug"", ""uk"", ""umb"", ""ur"", ""uzn"", ""vec"", ""vi"", ""war"", ""wo"", ""xh"", ""ydd"", ""yo"", ""yue"", ""zh"", ""zsm"", ""zu"", ""multilingual""], ""annotations_creators"": [""found""], ""language_creators"": [""expert-generated""], ""multilinguality"": [""multilingual""], ""task_categories"": [""audio-classification"", ""automatic-speech-recognition"", ""audio-text-to-text"", 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{""split"": ""validation"", ""path"": ""data/por_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/por_Latn/test-*""}]}, {""config_name"": ""ron_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/ron_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/ron_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/ron_Latn/test-*""}]}, {""config_name"": ""rus_Cyrl"", ""data_files"": [{""split"": ""train"", ""path"": ""data/rus_Cyrl/train-*""}, {""split"": ""validation"", ""path"": ""data/rus_Cyrl/validation-*""}, {""split"": ""test"", ""path"": ""data/rus_Cyrl/test-*""}]}, {""config_name"": ""slk_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/slk_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/slk_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/slk_Latn/test-*""}]}, {""config_name"": ""slv_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/slv_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/slv_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/slv_Latn/test-*""}]}, {""config_name"": ""sna_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/sna_Latn/train-*""}, {""split"": ""test"", ""path"": ""data/sna_Latn/test-*""}, {""split"": ""validation"", ""path"": ""data/sna_Latn/validation-*""}]}, {""config_name"": ""snd_Arab"", ""data_files"": [{""split"": ""train"", ""path"": ""data/snd_Arab/train-*""}, {""split"": ""validation"", ""path"": ""data/snd_Arab/validation-*""}, {""split"": ""test"", ""path"": ""data/snd_Arab/test-*""}]}, {""config_name"": ""som_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/som_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/som_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/som_Latn/test-*""}]}, {""config_name"": ""spa_Latn"", ""data_files"": [{""split"": ""validation"", ""path"": ""data/spa_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/spa_Latn/test-*""}, {""split"": ""train"", ""path"": ""data/spa_Latn/train-*""}]}, {""config_name"": ""srp_Cyrl"", ""data_files"": [{""split"": ""train"", ""path"": ""data/srp_Cyrl/train-*""}, {""split"": ""validation"", ""path"": ""data/srp_Cyrl/validation-*""}, {""split"": ""test"", ""path"": ""data/srp_Cyrl/test-*""}]}, {""config_name"": ""swe_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/swe_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/swe_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/swe_Latn/test-*""}]}, {""config_name"": ""swh_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/swh_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/swh_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/swh_Latn/test-*""}]}, {""config_name"": ""tam_Taml"", ""data_files"": [{""split"": ""train"", ""path"": ""data/tam_Taml/train-*""}, {""split"": ""validation"", ""path"": ""data/tam_Taml/validation-*""}, {""split"": ""test"", ""path"": ""data/tam_Taml/test-*""}]}, {""config_name"": ""tel_Telu"", ""data_files"": [{""split"": ""train"", ""path"": ""data/tel_Telu/train-*""}, {""split"": ""validation"", ""path"": ""data/tel_Telu/validation-*""}, {""split"": ""test"", ""path"": ""data/tel_Telu/test-*""}]}, {""config_name"": ""tgk_Cyrl"", ""data_files"": [{""split"": ""train"", ""path"": ""data/tgk_Cyrl/train-*""}, {""split"": ""validation"", ""path"": ""data/tgk_Cyrl/validation-*""}, {""split"": ""test"", ""path"": ""data/tgk_Cyrl/test-*""}]}, {""config_name"": ""tgl_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/tgl_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/tgl_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/tgl_Latn/test-*""}]}, {""config_name"": ""tha_Thai"", ""data_files"": [{""split"": ""train"", ""path"": ""data/tha_Thai/train-*""}, {""split"": ""validation"", ""path"": ""data/tha_Thai/validation-*""}, {""split"": ""test"", ""path"": ""data/tha_Thai/test-*""}]}, {""config_name"": ""tur_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/tur_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/tur_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/tur_Latn/test-*""}]}, {""config_name"": ""ukr_Cyrl"", ""data_files"": [{""split"": ""train"", ""path"": ""data/ukr_Cyrl/train-*""}, {""split"": ""validation"", ""path"": ""data/ukr_Cyrl/validation-*""}, {""split"": ""test"", ""path"": ""data/ukr_Cyrl/test-*""}]}, {""config_name"": ""umb_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/umb_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/umb_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/umb_Latn/test-*""}]}, {""config_name"": ""urd_Arab"", ""data_files"": [{""split"": ""train"", ""path"": ""data/urd_Arab/train-*""}, {""split"": ""validation"", ""path"": ""data/urd_Arab/validation-*""}, {""split"": ""test"", ""path"": ""data/urd_Arab/test-*""}]}, {""config_name"": ""uzn_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/uzn_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/uzn_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/uzn_Latn/test-*""}]}, {""config_name"": ""vie_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/vie_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/vie_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/vie_Latn/test-*""}]}, {""config_name"": ""wol_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/wol_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/wol_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/wol_Latn/test-*""}]}, {""config_name"": ""xho_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/xho_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/xho_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/xho_Latn/test-*""}]}, {""config_name"": ""yor_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/yor_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/yor_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/yor_Latn/test-*""}]}, {""config_name"": ""zho_Hans"", ""data_files"": [{""split"": ""train"", ""path"": ""data/zho_Hans/train-*""}, {""split"": ""validation"", ""path"": ""data/zho_Hans/validation-*""}, {""split"": ""test"", ""path"": ""data/zho_Hans/test-*""}]}, {""config_name"": ""zho_Hant"", ""data_files"": [{""split"": ""train"", ""path"": ""data/zho_Hant/train-*""}, {""split"": ""validation"", ""path"": ""data/zho_Hant/validation-*""}, {""split"": ""test"", ""path"": ""data/zho_Hant/test-*""}]}, {""config_name"": ""zsm_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/zsm_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/zsm_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/zsm_Latn/test-*""}]}, {""config_name"": ""zul_Latn"", ""data_files"": [{""split"": ""train"", ""path"": ""data/zul_Latn/train-*""}, {""split"": ""validation"", ""path"": ""data/zul_Latn/validation-*""}, {""split"": ""test"", ""path"": ""data/zul_Latn/test-*""}]}]}","# SIB-Fleurs SIB-Fleurs is a dataset suitable to evaluate Multilingual Spoken Language Understanding. For each utterance in Fleurs, the task is to determine the topic the utterance belongs to. The topics are: - Science/Technology - Travel - Politics - Sports - Health - Entertainment - Geography **Preliminary evaluations can be found at the bottom of the README. The preliminary results in full detail are available in ./results.csv***. ## Dataset creation This dataset processes and merges all available multilingual data from the Fleurs, Flores, and [SIB-200](https://huggingface.co/datasets/Davlan/sib200) datasets. It aligns the data of SIB to the available instances from the merged Fleurs-Flores data. The processing pipeline involves the following steps: 1. Remove all silent and noisy files from Fleurs. 2. Match Fleurs into Flores 3. Merge SIB into available Fleurs-Flores sentences. **This dataset retains the training, validation, and test splits of Fleurs and not SIB.** Full details and scripts to compile this dataset are available at: [https://github.com/fdschmidt93/fleurs-slu](https://github.com/fdschmidt93/fleurs-slu) ## Usage ### Example Each sentence in Flores has ~2.3 utterances in Fleurs, on average. That is why each instance comprises the aligned Fleurs data as `list[str, audio, ...]`. We track all available meta data (gender, speaker id) and further provide the ASR, ASR translations, CER, and WER for [SeamlessM4Tv2-Large](https://huggingface.co/facebook/seamless-m4t-v2-large) and [WhisperV3-Large](https://huggingface.co/openai/whisper-large-v3). ```python from datasets import load_dataset eng_Latn = load_dataset(""wuenlp/sib-fleurs"", ""eng_Latn"", split=""test"") eng_Latn[0] # { # 'sentence': 'As knowledge of Greek declined, the West found itself cut off from its Greek philosophical and scientific roots.', # 'URL': 'https://en.wikibooks.org/wiki/Animal_Behavior/History', # 'id': 596, # 'domain': 'wikibooks', # 'topic': 'Science/Animal Behavior', # 'has_image': 0, # 'has_hyperlink': 0, # 'fleurs_id': 1895, # 'filename': ['5358875111503056320.wav', '11200231708585274851.wav'], # 'raw_transcription': 'As knowledge of Greek declined, the West found itself cut off from its Greek philosophical and scientific roots.', # 'transcription': 'as knowledge of greek declined the west found itself cut off from its greek philosophical and scientific roots', # 'num_samples': [120960, 162880], # 'speaker_id': [5, 1], # 'gender': ['FEMALE', 'MALE'], # 'whisper_asr': ['As knowledge of Greek declined, the West found itself cut off from its Greek philosophical and scientific roots.', # 'As knowledge of Greek declined, the West found itself cut off from its Greek philosophical and scientific roots.'], # 'whisper_asr_cer': [0.0, 0.0], # 'whisper_asr_wer': [0.0, 0.0], # 'whisper_asr_translation': ['As knowledge of Greek declined, the West found itself cut off from its Greek philosophical and scientific roots.', # 'As knowledge of Greek declined, the West found itself cut off from its Greek philosophical and scientific roots.'], # 'seamlessm4t_asr': ['As knowledge of Greek declined, the West found itself cut off from its Greek philosophical and scientific roots.', # 'As knowledge of Greek declined, the West found itself cut off from its Greek philosophical and scientific roots.'], # 'seamlessm4t_asr_cer': [0.0, 0.0], # 'seamlessm4t_asr_wer': [0.0, 0.0], # 'seamlessm4t_asr_translation': ['As knowledge of Greek declined, the West found itself cut off from its Greek philosophical and scientific roots.', # 'As knowledge of Greek declined, the West found itself cut off from its Greek philosophical and scientific roots.'], # 'index_id': 1592, # 'category': 0, # 'text': 'As knowledge of Greek declined, the West found itself cut off from its Greek philosophical and scientific roots.', # 'audio': [{'path': '5358875111503056320.wav', 'array': array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ..., 5.72204590e-06, 7.56978989e-06, 5.42402267e-06]), 'sampling_rate': 16000}, # {'path': '11200231708585274851.wav', 'array': array([0. , 0. , 0. , ..., 0.00011402, 0.00011003, 0.00012642]), 'sampling_rate': 16000}] # } ``` ### Preprocessing Below is an example of how to use the provided functions for selecting utterances from the Sib-Fleurs dataset according to different criteria (e.g. minimizing or maximizing CER, or random selection). You can adjust the selection strategy (`strategy`) as needed. The mapping unpacks the below columns according the selection criterion provided by `strategy`. - 'filename' - 'speaker_id' - 'gender' - 'whisper_asr' - 'whisper_asr_cer' - 'whisper_asr_wer' - 'whisper_asr_translation' - 'seamlessm4t_asr' - 'seamlessm4t_asr_cer' - 'seamlessm4t_asr_wer' - 'seamlessm4t_asr_translation' **Note:** The selection logic takes into account which models are supported for a given language. If a language is unsupported by one of the models, the function automatically adjusts to only consider CERs from the supported models. #### Selection Strategy: You can choose how you want to select utterances: - `strategy=""best""`: Selects utterances with the minimal Character Error Rate (CER). - `strategy=""worst""`: Selects utterances with the maximal CER. - `strategy=""random""`: Selects utterances at random. ```python import random from typing import Any, Callable import torch from datasets import load_dataset from transformers import PreTrainedTokenizerFast from datasets.arrow_dataset import Dataset def collect_cer( examples: dict[str, list[list[float]]], models: list[str] ) -> list[list[float]]: """""" Calculate the average CER (Character Error Rate) for each index of each example across specified models. Args: examples (dict[str, list[list[float]]]): Dictionary containing CER lists for different models. models (list[str]): List of models to include in the calculation. Returns: list[list[float]]: A list where each sublist contains the average CERs for each index of an example. Raises: ValueError: If models have inconsistent numbers of examples or mismatched CER lengths. """""" model_cer_lists = [examples[model] for model in models if model in examples] if not model_cer_lists or not all( len(cer_list) == len(model_cer_lists[0]) for cer_list in model_cer_lists ): raise ValueError(""All models must have the same number of examples."") averaged_cer = [] for example_group in zip(*model_cer_lists): if not all( len(cer_list) == len(example_group[0]) for cer_list in example_group ): raise ValueError(""All CER lists for an example must have the same length."") averaged_cer.append( [sum(values) / len(values) for values in zip(*example_group)] ) return averaged_cer def select_audio_mapper( language: str, strategy: str = ""best"", ) -> Callable[[dict[str, list[Any]]], dict[str, list[Any]]]: """""" Create a mapping function for selecting audio data based on CER. Args: language (str): Language code for filtering unsupported models. strategy (str, optional): Selection strategy ('best', 'worst', or 'random'). Defaults to 'best'. Returns: Callable[[dict[str, list[Any]]], dict[str, list[Any]]]: A function for mapping dataset examples. Raises: ValueError: If an invalid selection strategy is provided. """""" keys = { ""audio"", ""filename"", ""gender"", ""num_samples"", ""seamlessm4t_asr"", ""seamlessm4t_asr_cer"", ""seamlessm4t_asr_translation"", ""seamlessm4t_asr_wer"", ""speaker_id"", ""split"", ""whisper_asr"", ""whisper_asr_cer"", ""whisper_asr_translation"", ""whisper_asr_wer"", } # Define unsupported languages for each model seamless_unsupported = { ""ast_Latn"", ""hau_Latn"", ""kam_Latn"", ""kea_Latn"", ""lin_Latn"", ""mri_Latn"", ""nso_Latn"", ""oci_Latn"", ""tgl_Latn"", ""umb_Latn"", ""wol_Latn"", ""xho_Latn"", } whisper_unsupported = { ""ast_Latn"", ""ceb_Latn"", ""ckb_Arab"", ""fuv_Latn"", ""gle_Latn"", ""ibo_Latn"", ""kam_Latn"", ""kea_Latn"", ""kir_Cyrl"", ""lug_Latn"", ""luo_Latn"", ""nso_Latn"", ""tgl_Latn"", ""umb_Latn"", ""wol_Latn"", ""xho_Latn"", ""zul_Latn"", } # Define selection strategy if strategy == ""best"": select_func = lambda scores: min(range(len(scores)), key=lambda i: scores[i]) elif strategy == ""worst"": select_func = lambda scores: max(range(len(scores)), key=lambda i: scores[i]) elif strategy == ""random"": select_func = lambda scores: random.randint(0, len(scores) - 1) else: raise ValueError(""Invalid 'strategy'. Must be one of 'best', 'worst', or 'random'."") # Determine which models are supported for the given language if language not in whisper_unsupported and language not in seamless_unsupported: models = [""whisper_asr_cer"", ""seamlessm4t_asr_cer""] elif language in whisper_unsupported: models = [""seamlessm4t_asr_cer""] elif language in seamless_unsupported: models = [""whisper_asr_cer""] else: models = [""whisper_asr_cer"", ""seamlessm4t_asr_cer""] def map_fn(examples: dict[str, list[Any]]) -> dict[str, list[Any]]: """""" Map function to process dataset examples by selecting CER-based audio data. Args: examples (dict[str, list[Any]]): Dataset examples. Returns: dict[str, list[Any]]: Processed dataset examples. """""" cers = collect_cer(examples, models) indices = [select_func(cer) for cer in cers] for key, values in examples.items(): if key not in keys: examples[key] = values else: examples[key] = [values[idx] for idx, values in zip(indices, examples[key])] return examples return map_fn eng_Latn = load_dataset(""wuenlp/sib"", ""eng_Latn"", split=""test"") mapper = select_audio_mapper(""eng_Latn"") dataset = eng_Latn.map(mapper, batched=True, batch_size=50) ``` ## ASR results We evaluate both speech encoders and LMs in a cascaded pipeline. In the cascaded setup, we first run Automatic Speech Recognition (ASR) using WhisperV3-Large and SeamlessM4Tv2-Large, then process the transcribed text with a language model (currently roberta-large). We select the best checkpoint by maximizing performance on the English validation set. For evaluation, we test zero-shot cross-lingual transfer across all available languages. The avg column represents the average performance across all languages. For detailed per-language results, please refer to results.csv. | Model | Input | ASR Quality | Seed | LR | Batch Size | eng_Latn | avg | |:-------------------------------------|:--------------------------------|:--------------|-------:|------:|-------------:|:-----------|:------| | roberta-large | SeamlessM4Tv2 ASR Translation | best | 42 | 3e-5 | 32 | 92.7% | 81.5% | | roberta-large | SeamlessM4Tv2 ASR Translation | best | 43 | 3e-5 | 32 | 91.0% | 80.4% | | roberta-large | SeamlessM4Tv2 ASR Translation | worst | 44 | 2e-5 | 32 | 89.8% | 79.8% | | roberta-large | SeamlessM4Tv2 ASR Translation | best | 43 | 2e-5 | 32 | 87.6% | 79.3% | | roberta-large | SeamlessM4Tv2 ASR Translation | best | 42 | 2e-5 | 32 | 89.3% | 79.0% | | roberta-large | SeamlessM4Tv2 ASR Translation | worst | 43 | 3e-5 | 32 | 89.8% | 78.5% | | roberta-large | SeamlessM4Tv2 ASR Translation | best | 44 | 3e-5 | 32 | 88.1% | 78.5% | | roberta-large | SeamlessM4Tv2 ASR Translation | worst | 42 | 2e-5 | 32 | 89.3% | 78.4% | | roberta-large | SeamlessM4Tv2 ASR Translation | best | 44 | 2e-5 | 32 | 87.6% | 78.2% | | roberta-large | SeamlessM4Tv2 ASR Translation | worst | 43 | 2e-5 | 32 | 85.3% | 77.9% | | roberta-large | SeamlessM4Tv2 ASR Translation | worst | 44 | 3e-5 | 32 | 88.1% | 77.5% | | roberta-large | SeamlessM4Tv2 ASR Translation | worst | 42 | 3e-5 | 32 | 87.6% | 76.3% | | seamless-m4t-v2-large-speech-encoder | Speech | best | 42 | 3e-5 | 32 | 85.9% | 70.0% | | roberta-large | WhisperV3-Large ASR Translation | best | 43 | 3e-5 | 32 | 90.4% | 69.1% | | roberta-large | WhisperV3-Large ASR Translation | best | 42 | 3e-5 | 32 | 91.5% | 68.9% | | roberta-large | WhisperV3-Large ASR Translation | best | 43 | 2e-5 | 32 | 88.7% | 68.5% | | roberta-large | WhisperV3-Large ASR Translation | worst | 43 | 2e-5 | 32 | 91.0% | 68.1% | | seamless-m4t-v2-large-speech-encoder | Speech | worst | 42 | 3e-5 | 32 | 85.9% | 67.8% | | roberta-large | WhisperV3-Large ASR Translation | worst | 43 | 3e-5 | 32 | 90.4% | 67.6% | | roberta-large | WhisperV3-Large ASR Translation | best | 44 | 3e-5 | 32 | 89.3% | 67.3% | | roberta-large | WhisperV3-Large ASR Translation | best | 44 | 2e-5 | 32 | 86.4% | 67.1% | | roberta-large | WhisperV3-Large ASR Translation | worst | 44 | 2e-5 | 32 | 90.4% | 66.8% | | roberta-large | WhisperV3-Large ASR Translation | worst | 44 | 3e-5 | 32 | 89.3% | 66.8% | | roberta-large | WhisperV3-Large ASR Translation | best | 42 | 2e-5 | 32 | 87.6% | 66.8% | | roberta-large | WhisperV3-Large ASR Translation | worst | 42 | 3e-5 | 32 | 89.8% | 66.0% | | roberta-large | WhisperV3-Large ASR Translation | worst | 42 | 2e-5 | 32 | 89.3% | 65.9% | | roberta-large | SeamlessM4Tv2 ASR Translation | best | 42 | 1e-5 | 32 | 67.8% | 66.0% | | roberta-large | SeamlessM4Tv2 ASR Translation | best | 43 | 1e-5 | 32 | 66.7% | 64.7% | | roberta-large | SeamlessM4Tv2 ASR Translation | worst | 42 | 1e-5 | 32 | 65.0% | 64.5% | | roberta-large | SeamlessM4Tv2 ASR Translation | best | 44 | 1e-5 | 32 | 66.7% | 64.0% | | roberta-large | SeamlessM4Tv2 ASR Translation | worst | 43 | 1e-5 | 32 | 66.1% | 63.7% | | roberta-large | WhisperV3-Large ASR Translation | best | 42 | 1e-5 | 32 | 80.2% | 62.6% | | roberta-large | SeamlessM4Tv2 ASR Translation | worst | 44 | 1e-5 | 32 | 63.8% | 61.7% | | roberta-large | WhisperV3-Large ASR Translation | best | 44 | 1e-5 | 32 | 76.3% | 60.8% | | roberta-large | WhisperV3-Large ASR Translation | worst | 43 | 1e-5 | 32 | 78.0% | 60.7% | | roberta-large | WhisperV3-Large ASR Translation | worst | 42 | 1e-5 | 32 | 76.3% | 59.5% | | roberta-large | WhisperV3-Large ASR Translation | worst | 44 | 1e-5 | 32 | 74.0% | 58.2% | | seamless-m4t-v2-large-speech-encoder | Speech | worst | 43 | 3e-5 | 32 | 83.1% | 57.4% | | seamless-m4t-v2-large-speech-encoder | Speech | best | 43 | 3e-5 | 32 | 81.9% | 56.2% | | seamless-m4t-v2-large-speech-encoder | Speech | best | 44 | 3e-5 | 32 | 83.6% | 55.6% | | seamless-m4t-v2-large-speech-encoder | Speech | worst | 44 | 3e-5 | 32 | 81.4% | 55.5% | | seamless-m4t-v2-large-speech-encoder | Speech | best | 42 | 2e-5 | 32 | 74.6% | 50.8% | | whisper-large-v3-turbo | Speech | worst | 42 | 2e-5 | 32 | 81.4% | 50.4% | | whisper-large-v3-turbo | Speech | best | 42 | 1e-5 | 32 | 80.2% | 48.7% | | whisper-large-v3-turbo | Speech | worst | 42 | 1e-5 | 32 | 79.7% | 47.4% | | whisper-large-v3-turbo | Speech | best | 44 | 2e-5 | 32 | 83.6% | 46.9% | | whisper-large-v3-turbo | Speech | best | 42 | 2e-5 | 32 | 77.4% | 45.8% | | whisper-large-v3-turbo | Speech | best | 43 | 1e-5 | 32 | 75.7% | 45.3% | | seamless-m4t-v2-large-speech-encoder | Speech | best | 44 | 2e-5 | 32 | 78.5% | 44.0% | | seamless-m4t-v2-large-speech-encoder | Speech | worst | 42 | 2e-5 | 32 | 66.1% | 43.5% | | seamless-m4t-v2-large-speech-encoder | Speech | worst | 44 | 2e-5 | 32 | 74.0% | 43.1% | | whisper-large-v3-turbo | Speech | worst | 42 | 3e-5 | 32 | 76.8% | 42.4% | | seamless-m4t-v2-large-speech-encoder | Speech | best | 43 | 2e-5 | 32 | 76.3% | 41.9% | | whisper-large-v3-turbo | Speech | worst | 43 | 3e-5 | 32 | 78.0% | 41.8% | | whisper-large-v3-turbo | Speech | best | 43 | 2e-5 | 32 | 74.0% | 41.2% | | seamless-m4t-v2-large-speech-encoder | Speech | worst | 43 | 2e-5 | 32 | 76.3% | 41.0% | | whisper-large-v3-turbo | Speech | best | 42 | 3e-5 | 32 | 76.3% | 40.6% | | whisper-large-v3-turbo | Speech | best | 43 | 3e-5 | 32 | 78.5% | 39.3% | | whisper-large-v3-turbo | Speech | worst | 44 | 2e-5 | 32 | 80.8% | 39.3% | | whisper-large-v3-turbo | Speech | worst | 43 | 2e-5 | 32 | 76.3% | 39.2% | | whisper-large-v3-turbo | Speech | worst | 44 | 1e-5 | 32 | 75.7% | 38.8% | | whisper-large-v3-turbo | Speech | best | 44 | 3e-5 | 32 | 76.8% | 37.1% | | whisper-large-v3-turbo | Speech | worst | 44 | 3e-5 | 32 | 75.1% | 37.0% | | whisper-large-v3-turbo | Speech | worst | 43 | 1e-5 | 32 | 73.4% | 35.8% | | whisper-large-v3-turbo | Speech | best | 44 | 1e-5 | 32 | 76.8% | 34.5% | | seamless-m4t-v2-large-speech-encoder | Speech | worst | 42 | 1e-5 | 32 | 33.9% | 26.5% | | seamless-m4t-v2-large-speech-encoder | Speech | best | 42 | 1e-5 | 32 | 28.8% | 24.7% | | seamless-m4t-v2-large-speech-encoder | Speech | best | 43 | 1e-5 | 32 | 18.6% | 18.4% | | seamless-m4t-v2-large-speech-encoder | Speech | worst | 43 | 1e-5 | 32 | 18.6% | 18.1% | | seamless-m4t-v2-large-speech-encoder | Speech | worst | 44 | 1e-5 | 32 | 16.9% | 13.0% | | seamless-m4t-v2-large-speech-encoder | Speech | best | 44 | 1e-5 | 32 | 18.6% | 12.7% | # Statistics The table below denotes the number of available examples per split by language. The original SIB splits have been realigned to match the Fleurs splits. | Language | Train | Validation | Test | |:---------|--------:|-------------:|-------:| | `afr_Latn` | 406 | 86 | 95 | | `amh_Ethi` | 752 | 54 | 149 | | `arb_Arab` | 579 | 64 | 133 | | `asm_Beng` | 730 | 71 | 176 | | `ast_Latn` | 701 | 69 | 177 | | `azj_Latn` | 712 | 71 | 174 | | `bel_Cyrl` | 690 | 71 | 177 | | `bul_Cyrl` | 749 | 70 | 176 | | `ben_Beng` | 742 | 71 | 176 | | `bos_Latn` | 746 | 71 | 177 | | `cat_Latn` | 683 | 71 | 177 | | `ceb_Latn` | 741 | 61 | 149 | | `ckb_Arab` | 738 | 70 | 176 | | `zho_Hans` | 751 | 71 | 176 | | `ces_Latn` | 732 | 68 | 172 | | `cym_Latn` | 739 | 71 | 177 | | `dan_Latn` | 696 | 70 | 177 | | `deu_Latn` | 736 | 69 | 175 | | `ell_Grek` | 750 | 67 | 168 | | `eng_Latn` | 738 | 71 | 177 | | `spa_Latn` | 676 | 71 | 177 | | `est_Latn` | 700 | 71 | 176 | | `pes_Arab` | 692 | 66 | 165 | | `fin_Latn` | 735 | 71 | 175 | | `tgl_Latn` | 604 | 71 | 176 | | `fra_Latn` | 753 | 65 | 164 | | `gle_Latn` | 731 | 71 | 176 | | `glg_Latn` | 660 | 71 | 174 | | `guj_Gujr` | 752 | 71 | 177 | | `hau_Latn` | 753 | 70 | 166 | | `heb_Hebr` | 754 | 70 | 175 | | `hin_Deva` | 653 | 60 | 132 | | `hrv_Latn` | 756 | 71 | 176 | | `hun_Latn` | 750 | 71 | 177 | | `hye_Armn` | 741 | 71 | 177 | | `ind_Latn` | 728 | 69 | 167 | | `ibo_Latn` | 737 | 71 | 177 | | `isl_Latn` | 381 | 18 | 23 | | `ita_Latn` | 743 | 69 | 175 | | `jpn_Jpan` | 662 | 62 | 164 | | `jav_Latn` | 740 | 67 | 171 | | `kat_Geor` | 557 | 69 | 177 | | `kam_Latn` | 752 | 69 | 179 | | `kea_Latn` | 725 | 71 | 175 | | `kaz_Cyrl` | 749 | 70 | 176 | | `khm_Khmr` | 588 | 69 | 168 | | `kan_Knda` | 660 | 70 | 174 | | `kor_Hang` | 669 | 61 | 141 | | `kir_Cyrl` | 729 | 71 | 177 | | `ltz_Latn` | 703 | 71 | 176 | | `lug_Latn` | 691 | 70 | 173 | | `lin_Latn` | 755 | 59 | 139 | | `lao_Laoo` | 591 | 54 | 132 | | `lit_Latn` | 730 | 71 | 178 | | `luo_Latn` | 698 | 39 | 98 | | `lvs_Latn` | 634 | 69 | 174 | | `mri_Latn` | 749 | 71 | 176 | | `mkd_Cyrl` | 680 | 71 | 177 | | `mal_Mlym` | 723 | 68 | 174 | | `khk_Cyrl` | 743 | 71 | 177 | | `mar_Deva` | 749 | 71 | 177 | | `zsm_Latn` | 713 | 67 | 171 | | `mlt_Latn` | 731 | 71 | 176 | | `mya_Mymr` | 746 | 71 | 175 | | `nob_Latn` | 723 | 51 | 127 | | `npi_Deva` | 754 | 70 | 175 | | `nld_Latn` | 729 | 58 | 123 | | `nso_Latn` | 633 | 70 | 169 | | `nya_Latn` | 720 | 68 | 169 | | `oci_Latn` | 756 | 71 | 177 | | `ory_Orya` | 442 | 71 | 168 | | `pan_Guru` | 580 | 56 | 143 | | `pol_Latn` | 723 | 68 | 165 | | `pbt_Arab` | 701 | 55 | 144 | | `por_Latn` | 728 | 70 | 177 | | `ron_Latn` | 734 | 69 | 177 | | `rus_Cyrl` | 733 | 71 | 173 | | `snd_Arab` | 749 | 71 | 177 | | `slk_Latn` | 628 | 71 | 169 | | `slv_Latn` | 704 | 71 | 174 | | `sna_Latn` | 689 | 71 | 176 | | `som_Latn` | 746 | 70 | 177 | | `srp_Cyrl` | 730 | 63 | 164 | | `swe_Latn` | 686 | 71 | 168 | | `swh_Latn` | 745 | 65 | 154 | | `tam_Taml` | 693 | 71 | 169 | | `tel_Telu` | 658 | 66 | 153 | | `tgk_Cyrl` | 680 | 69 | 163 | | `tha_Thai` | 710 | 71 | 176 | | `tur_Latn` | 692 | 67 | 164 | | `ukr_Cyrl` | 732 | 67 | 164 | | `umb_Latn` | 473 | 39 | 108 | | `urd_Arab` | 636 | 65 | 120 | | `uzn_Latn` | 734 | 69 | 175 | | `vie_Latn` | 737 | 70 | 176 | | `wol_Latn` | 643 | 52 | 123 | | `xho_Latn` | 756 | 71 | 177 | | `yor_Latn` | 686 | 71 | 172 | | `zho_Hant` | 624 | 70 | 172 | | `zul_Latn` | 739 | 69 | 175 | | `fuv_Latn` | 752 | 68 | 166 | | `gaz_Latn` | 574 | 6 | 17 | # Citations If you are using this dataset, please cite the following papers. Our paper is forthcoming and will be added as soon as possible. ``` @misc{adelani2023sib200, title={SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects}, author={David Ifeoluwa Adelani and Hannah Liu and Xiaoyu Shen and Nikita Vassilyev and Jesujoba O. Alabi and Yanke Mao and Haonan Gao and Annie En-Shiun Lee}, year={2023}, eprint={2309.07445}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```" ontocord/CulturaY,"{""configs"": [{""config_name"": ""af"", ""data_files"": ""af/*.jsonl.zst""}, {""config_name"": ""ar"", ""data_files"": ""ar/*.jsonl.zst""}, {""config_name"": ""az"", ""data_files"": ""az/*.jsonl.zst""}, {""config_name"": ""be"", ""data_files"": ""be/*.jsonl.zst""}, {""config_name"": ""bg"", ""data_files"": ""bg/*.jsonl.zst""}, {""config_name"": ""bn"", ""data_files"": ""bn/*.jsonl.zst""}, {""config_name"": ""ca"", ""data_files"": ""ca/*.jsonl.zst""}, {""config_name"": ""cs"", ""data_files"": ""cs/*.jsonl.zst""}, {""config_name"": ""cy"", ""data_files"": ""cy/*.jsonl.zst""}, {""config_name"": ""da"", ""data_files"": ""da/*.jsonl.zst""}, {""config_name"": ""de"", ""data_files"": ""de/*.jsonl.zst""}, {""config_name"": ""el"", ""data_files"": ""el/*.jsonl.zst""}, {""config_name"": ""en"", ""data_files"": ""en/*.jsonl.zst""}, {""config_name"": ""eo"", ""data_files"": ""eo/*.jsonl.zst""}, {""config_name"": ""es"", ""data_files"": ""es/*.jsonl.zst""}, {""config_name"": ""et"", ""data_files"": ""et/*.jsonl.zst""}, {""config_name"": ""eu"", ""data_files"": ""eu/*.jsonl.zst""}, {""config_name"": ""fa"", ""data_files"": ""fa/*.jsonl.zst""}, {""config_name"": ""fi"", ""data_files"": ""fi/*.jsonl.zst""}, {""config_name"": ""fr"", ""data_files"": ""fr/*.jsonl.zst""}, {""config_name"": ""ga"", ""data_files"": ""ga/*.jsonl.zst""}, {""config_name"": ""gl"", ""data_files"": ""gl/*.jsonl.zst""}, {""config_name"": ""gu"", ""data_files"": ""gu/*.jsonl.zst""}, {""config_name"": ""hbs"", ""data_files"": ""hbs/*.jsonl.zst""}, {""config_name"": ""he"", ""data_files"": ""he/*.jsonl.zst""}, {""config_name"": ""hi"", ""data_files"": ""hi/*.jsonl.zst""}, {""config_name"": ""hu"", ""data_files"": ""hu/*.jsonl.zst""}, {""config_name"": ""hy"", ""data_files"": ""hy/*.jsonl.zst""}, {""config_name"": ""id"", ""data_files"": ""id/*.jsonl.zst""}, {""config_name"": ""is"", ""data_files"": ""is/*.jsonl.zst""}, {""config_name"": ""it"", ""data_files"": ""it/*.jsonl.zst""}, {""config_name"": ""ja"", ""data_files"": ""ja/*.jsonl.zst""}, {""config_name"": ""ka"", ""data_files"": ""ka/*.jsonl.zst""}, {""config_name"": ""kk"", ""data_files"": ""kk/*.jsonl.zst""}, {""config_name"": ""kn"", ""data_files"": ""kn/*.jsonl.zst""}, {""config_name"": ""ko"", ""data_files"": ""ko/*.jsonl.zst""}, {""config_name"": ""ky"", ""data_files"": ""ky/*.jsonl.zst""}, {""config_name"": ""la"", ""data_files"": ""la/*.jsonl.zst""}, {""config_name"": ""lt"", ""data_files"": ""lt/*.jsonl.zst""}, {""config_name"": ""lv"", ""data_files"": ""lv/*.jsonl.zst""}, {""config_name"": ""mk"", ""data_files"": ""mk/*.jsonl.zst""}, {""config_name"": ""ml"", ""data_files"": ""ml/*.jsonl.zst""}, {""config_name"": ""mn"", ""data_files"": ""mn/*.jsonl.zst""}, {""config_name"": ""mr"", ""data_files"": ""mr/*.jsonl.zst""}, {""config_name"": ""ms"", ""data_files"": ""ms/*.jsonl.zst""}, {""config_name"": ""mt"", ""data_files"": ""mt/*.jsonl.zst""}, {""config_name"": ""my"", ""data_files"": ""my/*.jsonl.zst""}, {""config_name"": ""nb"", ""data_files"": ""nb/*.jsonl.zst""}, {""config_name"": ""ne"", ""data_files"": ""ne/*.jsonl.zst""}, {""config_name"": ""nl"", ""data_files"": ""nl/*.jsonl.zst""}, {""config_name"": ""nn"", ""data_files"": ""nn/*.jsonl.zst""}, {""config_name"": ""pa"", ""data_files"": ""pa/*.jsonl.zst""}, {""config_name"": ""pl"", ""data_files"": ""pl/*.jsonl.zst""}, {""config_name"": ""ps"", ""data_files"": ""ps/*.jsonl.zst""}, {""config_name"": ""pt"", ""data_files"": ""pt/*.jsonl.zst""}, {""config_name"": ""ro"", ""data_files"": ""ro/*.jsonl.zst""}, {""config_name"": ""ru"", ""data_files"": ""ru/*.jsonl.zst""}, {""config_name"": ""si"", ""data_files"": ""si/*.jsonl.zst""}, {""config_name"": ""sk"", ""data_files"": ""sk/*.jsonl.zst""}, {""config_name"": ""sl"", ""data_files"": ""sl/*.jsonl.zst""}, {""config_name"": ""so"", ""data_files"": ""so/*.jsonl.zst""}, {""config_name"": ""sq"", ""data_files"": ""sq/*.jsonl.zst""}, {""config_name"": ""sv"", ""data_files"": ""sv/*.jsonl.zst""}, {""config_name"": ""sw"", ""data_files"": ""sw/*.jsonl.zst""}, {""config_name"": ""ta"", ""data_files"": ""ta/*.jsonl.zst""}, {""config_name"": ""te"", ""data_files"": ""te/*.jsonl.zst""}, {""config_name"": ""th"", ""data_files"": ""th/*.jsonl.zst""}, {""config_name"": ""tl"", ""data_files"": ""tl/*.jsonl.zst""}, {""config_name"": ""tr"", ""data_files"": ""tr/*.jsonl.zst""}, {""config_name"": ""tt"", ""data_files"": ""tt/*.jsonl.zst""}, {""config_name"": ""uk"", ""data_files"": ""uk/*.jsonl.zst""}, {""config_name"": ""ur"", ""data_files"": ""ur/*.jsonl.zst""}, {""config_name"": ""uz"", ""data_files"": ""uz/*.jsonl.zst""}, {""config_name"": ""vi"", ""data_files"": ""vi/*.jsonl.zst""}, {""config_name"": ""zh"", ""data_files"": ""zh/*.jsonl.zst""}], ""pretty_name"": ""CulturaY"", ""annotations_creators"": [""no-annotation""], ""language_creators"": [""found""], ""language"": [""af"", ""ar"", ""az"", ""be"", ""bg"", ""bn"", ""ca"", ""cs"", ""cy"", ""da"", ""de"", ""el"", ""en"", ""eo"", ""es"", ""et"", ""eu"", ""fa"", ""fi"", ""fr"", ""ga"", ""gl"", ""gu"", ""hbs"", ""he"", ""hi"", ""hu"", ""hy"", ""id"", ""is"", ""it"", ""ja"", ""ka"", ""kk"", ""kn"", ""ko"", ""ky"", ""la"", ""lt"", ""lv"", ""mk"", ""ml"", ""mn"", ""mr"", ""ms"", ""mt"", ""my"", ""nb"", ""ne"", ""nl"", ""nn"", ""pa"", ""pl"", ""ps"", ""pt"", ""ro"", ""ru"", ""si"", ""sk"", ""sl"", ""so"", ""sq"", ""sv"", ""sw"", ""ta"", ""te"", ""th"", ""tl"", ""tr"", ""tt"", ""uk"", ""ur"", ""uz"", ""vi"", ""zh""], ""multilinguality"": [""multilingual""], ""size_categories"": [""n<1K"", ""1K>> from datasets import load_dataset >>> ds = load_dataset(""nlpai-lab/kullm-v2"", split=""train"") >>> ds DatasetDict({ train: Dataset({ features: ['id', 'instruction', 'input', 'output'], num_rows: 152630 }) }) ``` ```python >>> ds[0] {'id': 'alpaca_{idx}', 'instruction': '3원색이란 무엇인가요?', 'input': '', 'output': '세 가지 기본 색은 빨강, 파랑, 노랑입니다. 이 색은 다른 색을 혼합하여 만들 수 없고 다른 모든 색은 다양한 비율로 조합하여 만들 수 있기 때문에 원색이라고 부릅니다. 빛에 사용되는 첨가제 색상 시스템에서 원색은 빨강, 녹색, 파랑(RGB)입니다.'} ```" adithya7/xlel_wd_dictionary,"{""annotations_creators"": [""found""], ""language_creators"": [""found""], ""language"": [""af"", ""ar"", ""be"", ""bg"", ""bn"", ""ca"", ""cs"", ""da"", ""de"", ""el"", ""en"", ""es"", ""fa"", ""fi"", ""fr"", ""he"", ""hi"", ""hu"", ""id"", ""it"", ""ja"", ""ko"", ""ml"", ""mr"", ""ms"", ""nl"", ""no"", ""pl"", ""pt"", ""ro"", ""ru"", ""si"", ""sk"", ""sl"", ""sr"", ""sv"", ""sw"", ""ta"", ""te"", ""th"", ""tr"", ""uk"", ""vi"", ""zh""], ""license"": [""cc-by-4.0""], ""multilinguality"": [""multilingual""], ""pretty_name"": ""XLEL-WD is a multilingual event linking dataset. This supplementary dataset contains a dictionary of event items from Wikidata. The descriptions for Wikidata event items are taken from the corresponding multilingual Wikipedia articles."", ""size_categories"": [""10K - **Repository:** - **Paper:** - **Leaderboard:** N/A - **Point of Contact:** Adithya Pratapa ### Dataset Summary XLEL-WD is a multilingual event linking dataset. This supplementary dataset contains a dictionary of event items from Wikidata. The descriptions for Wikidata event items are taken from the corresponding multilingual Wikipedia articles. ### Supported Tasks and Leaderboards This dictionary can be used as a part of the event linking task. ### Languages This dataset contains text from 44 languages. The language names and their ISO 639-1 codes are listed below. For details on the dataset distribution for each language, refer to the original paper. | Language | Code | Language | Code | Language | Code | Language | Code | | -------- | ---- | -------- | ---- | -------- | ---- | -------- | ---- | | Afrikaans | af | Arabic | ar | Belarusian | be | Bulgarian | bg | | Bengali | bn | Catalan | ca | Czech | cs | Danish | da | | German | de | Greek | el | English | en | Spanish | es | | Persian | fa | Finnish | fi | French | fr | Hebrew | he | | Hindi | hi | Hungarian | hu | Indonesian | id | Italian | it | | Japanese | ja | Korean | ko | Malayalam | ml | Marathi | mr | | Malay | ms | Dutch | nl | Norwegian | no | Polish | pl | | Portuguese | pt | Romanian | ro | Russian | ru | Sinhala | si | | Slovak | sk | Slovene | sl | Serbian | sr | Swedish | sv | | Swahili | sw | Tamil | ta | Telugu | te | Thai | th | | Turkish | tr | Ukrainian | uk | Vietnamese | vi | Chinese | zh | ## Dataset Structure ### Data Instances Each instance in the `label_dict.jsonl` file follows the below template, ```json { ""label_id"": ""830917"", ""label_title"": ""2010 European Aquatics Championships"", ""label_desc"": ""The 2010 European Aquatics Championships were held from 4–15 August 2010 in Budapest and Balatonfüred, Hungary. It was the fourth time that the city of Budapest hosts this event after 1926, 1958 and 2006. Events in swimming, diving, synchronised swimming (synchro) and open water swimming were scheduled."", ""label_lang"": ""en"" } ``` ### Data Fields | Field | Meaning | | ----- | ------- | | `label_id` | Wikidata ID | | `label_title` | Title for the event, as collected from the corresponding Wikipedia article | | `label_desc` | Description for the event, as collected from the corresponding Wikipedia article | | `label_lang` | language used for the title and description | ### Data Splits This dictionary has a single split, `dictionary`. It contains 10947 event items from Wikidata and a total of 114834 text descriptions collected from multilingual Wikipedia articles. ## Dataset Creation ### Curation Rationale This datasets helps address the task of event linking. KB linking is extensively studied for entities, but its unclear if the same methodologies can be extended for linking mentions to events from KB. Event items are collected from Wikidata. ### Source Data #### Initial Data Collection and Normalization A Wikidata item is considered a potential event if it has spatial and temporal properties. The final event set is collected after post-processing for quality control. #### Who are the source language producers? The titles and descriptions for the events are written by Wikipedia contributors. ### Annotations #### Annotation process This dataset was automatically compiled from Wikidata. It was post-processed to improve data quality. #### Who are the annotators? Wikidata and Wikipedia contributors. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations This dictionary primarily contains eventive nouns from Wikidata. It does not include other event items from Wikidata such as disease outbreak (Q3241045), military offensive (Q2001676), war (Q198), etc., ## Additional Information ### Dataset Curators The dataset was curated by Adithya Pratapa, Rishubh Gupta and Teruko Mitamura. The code for collecting the dataset is available at [Github:xlel-wd](https://github.com/adithya7/xlel-wd). ### Licensing Information XLEL-WD dataset is released under [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/). ### Citation Information ```bib @article{pratapa-etal-2022-multilingual, title = {Multilingual Event Linking to Wikidata}, author = {Pratapa, Adithya and Gupta, Rishubh and Mitamura, Teruko}, publisher = {arXiv}, year = {2022}, url = {https://arxiv.org/abs/2204.06535}, } ``` ### Contributions Thanks to [@adithya7](https://github.com/adithya7) for adding this dataset." stanford-oval/ccnews,"{""language"": [""multilingual"", ""af"", ""am"", ""ar"", ""as"", ""az"", ""be"", ""bg"", ""bn"", ""br"", ""bs"", ""ca"", ""cs"", ""cy"", ""da"", ""de"", ""el"", ""en"", ""eo"", ""es"", ""et"", ""eu"", ""fa"", ""fi"", ""fr"", ""fy"", ""ga"", ""gd"", ""gl"", ""gu"", ""ha"", ""he"", ""hi"", ""hr"", ""hu"", ""hy"", ""id"", ""is"", ""it"", ""ja"", ""jv"", ""ka"", ""kk"", ""km"", ""kn"", ""ko"", ""ku"", ""ky"", ""la"", ""lo"", ""lt"", ""lv"", ""mg"", ""mk"", ""ml"", ""mn"", ""mr"", ""ms"", ""my"", ""ne"", ""nl"", ""no"", ""om"", ""or"", ""pa"", ""pl"", ""ps"", ""pt"", ""ro"", ""ru"", ""sa"", ""sd"", ""si"", ""sk"", ""sl"", ""so"", ""sq"", ""sr"", ""su"", ""sv"", ""sw"", ""ta"", ""te"", ""th"", ""tl"", ""tr"", ""ug"", ""uk"", ""ur"", ""uz"", ""vi"", ""xh"", ""yi"", ""zh""], ""pretty_name"": ""All of Common Crawl News, 100+ languages, preprocessed and cleaned"", ""task_categories"": [""text-classification"", ""question-answering"", ""text-generation"", ""text2text-generation""], ""size_categories"": [""100M ## Dataset Descritpion Korean Hate Speech Evaluation Datasets : trained with [BEEP!](https://huggingface.co/datasets/kor_hate) and evaluate with [APEACH](https://github.com/jason9693/APEACH) - **Repository: [Korean HateSpeech Evaluation Dataset](https://github.com/jason9693/APEACH)** - **Paper: [APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets](https://arxiv.org/abs/2202.12459)** - **Point of Contact: [Kichang Yang](ykcha9@gmail.com)** ### Languages ko-KR ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json {'text': ['(현재 호텔주인 심정) 아18 난 마른하늘에 날벼락맞고 호텔망하게생겼는데 누군 계속 추모받네....', '....한국적인 미인의 대표적인 분...너무나 곱고아름다운모습...그모습뒤의 슬픔을 미처 알지못했네요ㅠ'], 'class': ['Spoiled', 'Default']} ``` ### Dataset Fields The dataset has the following fields (also called ""features""): ```json { ""text"": ""Value(dtype='string', id=None)"", ""class"": ""ClassLabel(num_classes=2, names=['Default', 'Spoiled'], id=None)"" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train (binarized BEEP!) | 7896 | | valid (APEACH) | 3770 | ## Citation ``` @article{yang2022apeach, title={APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets}, author={Yang, Kichang and Jang, Wonjun and Cho, Won Ik}, journal={arXiv preprint arXiv:2202.12459}, year={2022} } ```" xu-song/cc100-samples,"{""annotations_creators"": [""no-annotation""], ""language_creators"": [""found""], ""datasets"": [""cc100""], ""language"": [""af"", ""am"", ""ar"", ""as"", ""az"", ""be"", ""bg"", ""bn"", ""br"", ""bs"", ""ca"", ""cs"", ""cy"", ""da"", ""de"", ""el"", ""en"", ""eo"", ""es"", ""et"", ""eu"", ""fa"", ""ff"", ""fi"", ""fr"", ""fy"", ""ga"", ""gd"", ""gl"", ""gn"", ""gu"", ""ha"", ""he"", ""hi"", ""hr"", ""ht"", ""hu"", ""hy"", ""id"", ""ig"", ""is"", ""it"", ""ja"", ""jv"", ""ka"", ""kk"", ""km"", ""kn"", ""ko"", ""ku"", ""ky"", ""la"", ""lg"", ""li"", ""ln"", ""lo"", ""lt"", ""lv"", ""mg"", ""mk"", ""ml"", ""mn"", ""mr"", ""ms"", ""my"", ""ne"", ""nl"", ""no"", ""ns"", ""om"", ""or"", ""pa"", ""pl"", ""ps"", ""pt"", ""qu"", ""rm"", ""ro"", ""ru"", ""sa"", ""sc"", ""sd"", ""si"", ""sk"", ""sl"", ""so"", ""sq"", ""sr"", ""ss"", ""su"", ""sv"", ""sw"", ""ta"", ""te"", ""th"", ""tl"", ""tn"", ""tr"", ""ug"", ""uk"", ""ur"", ""uz"", ""vi"", ""wo"", ""xh"", ""yi"", ""yo"", ""zh"", ""zu""], ""language_bcp47"": [""bn-Latn"", ""hi-Latn"", ""my-x-zawgyi"", ""ta-Latn"", ""te-Latn"", ""ur-Latn"", ""zh-Hans"", ""zh-Hant""], ""license"": [""unknown""], ""multilinguality"": [""multilingual""], ""size_categories"": [""1K A dataset containing strings from projects hosted on [Weblate](https://hosted.weblate.org) and their translations into other languages. Please consider [donating](https://weblate.org/en/donate/) or [contributing](https://weblate.org/en/contribute/) to Weblate if you find this dataset useful. To avoid rows with values like ""None"" and ""N/A"" being interpreted as missing values, pass the keep_default_na parameter like this: ``` from datasets import load_dataset dataset = load_dataset(""ayymen/Weblate-Translations"", keep_default_na=False) ``` ## Dataset Details ### Dataset Description - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** Each sentence pair in the dataset has a corresponding license in the ""license"" column. This license is the one specified in the component or project containing the sentence. ### Dataset Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses - Machine Translation - Language Identification ### Direct Use [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Dataset Structure [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Data Collection and Processing - Sentence pairs with empty/missing elements were dropped. - Identical pairs were dropped. - Trailing whitespace was stripped. - Rows were deduplicated based on all 3 columns including ""license"", on a config/subset/tsv file basis. Which means that a single config might contain two identical sentence pairs with different licenses. Or a different config/subset might contain the exact same row (most likely a different variant/dialect of the same language(s)). #### Who are the source data producers? [More Information Needed] ### Annotations [optional] #### Annotation process [More Information Needed] #### Who are the annotators? Weblate users. #### Personal and Sensitive Information [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]" heegyu/namuwiki-extracted,"{""license"": ""cc-by-nc-sa-2.0"", ""language"": [""ko""], ""language_creators"": [""other""], ""multilinguality"": [""monolingual""], ""size_categories"": [""100K - 571308rows - download size: 2.19GB ## 주의사항 namu-wiki-extractor를 이용하여 전처리, 추가로 아래 전처리를 수행했습니다 1. 헤더 제거 `== 개요 ==` 1. 테이블 제거 1. `[age(1997-01-01)]` 는 전처리 시점 기준으로 적용(2022년 10월 2일) 1. `[math(a / b + c)]` 는 제거하지 않음. 1. math 마크다운이 각주 내에 있을 경우, 각주가 전처리되지 않은 문제 있음. ## Usage ```bash pip install datasets ``` ```python from datasets import load_dataset dataset = load_dataset(""heegyu/namuwiki-extracted"") print(dataset[""train""][0]) ``` ``` { 'title': '!!아앗!!', 'text': '！！ああっと！！ ▲신 세계수의 미궁 2에서 뜬 !!아앗!! 세계수의 미궁 시리즈에 전통으로 등장하는 대사. 2편부터 등장했으며 훌륭한 사망 플래그의 예시이다. 세계수의 모험가들이 탐험하는 던전인 수해의 구석구석에는 채취/벌채/채굴 포인트가 있으며, 이를 위한 채집 스킬에 ...', 'contributors': '110.46.34.123,kirby10,max0243,218.54.117.149,ruby3141,121.165.63.239,iviyuki,1.229.200.194,anatra95,kiri47,175.127.134.2,nickchaos71,chkong1998,kiwitree2,namubot,huwieblusnow', 'namespace': '' } ```" bongsoo/kowiki20220620,"{""language"": [""ko""], ""license"": ""apache-2.0""}",-kowiki202206 1줄 말뭉치 wikimedia/wikisource,"{""language"": [""ar"", ""as"", ""az"", ""ban"", ""be"", ""bg"", ""bn"", ""br"", ""bs"", ""ca"", ""cs"", ""cy"", ""da"", ""de"", ""el"", ""en"", ""eo"", ""es"", ""et"", ""eu"", ""fa"", ""fi"", ""fo"", ""fr"", ""gl"", ""gu"", ""he"", ""hi"", ""hr"", ""hu"", ""hy"", ""id"", ""is"", ""it"", ""ja"", ""jv"", ""kn"", ""ko"", ""la"", ""li"", ""lij"", ""lt"", ""mk"", ""ml"", ""mr"", ""nan"", ""nap"", ""nl"", ""no"", ""or"", ""pa"", ""pl"", ""pms"", ""pt"", ""ro"", ""ru"", ""sa"", ""sah"", ""sk"", ""sl"", ""sr"", ""su"", ""sv"", ""ta"", ""te"", ""th"", ""tr"", ""uk"", ""vec"", ""vi"", ""wa"", ""yi"", ""zh""], ""license"": [""cc-by-sa-3.0"", ""gfdl""], ""size_categories"": [""n<1K"", ""1K This is a dataset containing strings from various Mozilla projects on Mozilla's [Pontoon](https://pontoon.mozilla.org) localization platform and their translations into more than 200 languages. Source strings are in English. To avoid rows with values like ""None"" and ""N/A"" being interpreted as missing values, pass the keep_default_na parameter like this: ``` from datasets import load_dataset dataset = load_dataset(""ayymen/Pontoon-Translations"", keep_default_na=False) ``` ## Dataset Details ### Dataset Description - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** Per [Pontoons's terms](https://pontoon.mozilla.org/terms/) ""Translations are governed by the [Mozilla Public License 2.0](https://www.mozilla.org/en-US/MPL/2.0/), or another license or set of licenses acceptable to the Mozilla Foundation."" ### Dataset Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses - Machine Translation - Language Identification ### Direct Use [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Dataset Structure [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Data Collection and Processing - Sentence pairs with empty/missing elements were dropped. - Identical pairs were dropped. - Rows where the english string does not contain any letters were dropped. - Leading and trailing whitespace was stripped. - Rows were deduplicated. #### Who are the source data producers? [More Information Needed] ### Annotations [optional] #### Annotation process [More Information Needed] #### Who are the annotators? Pontoon users. #### Personal and Sensitive Information [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]" openai/MMMLU,"{""task_categories"": [""question-answering""], ""configs"": [{""config_name"": ""default"", ""data_files"": [{""split"": ""test"", ""path"": ""test/*.csv""}]}, {""config_name"": ""AR_XY"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_AR-XY.csv""}]}, {""config_name"": ""BN_BD"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_BN-BD.csv""}]}, {""config_name"": ""DE_DE"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_DE-DE.csv""}]}, {""config_name"": ""ES_LA"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_ES-LA.csv""}]}, {""config_name"": ""FR_FR"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_FR-FR.csv""}]}, {""config_name"": ""HI_IN"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_HI-IN.csv""}]}, {""config_name"": ""ID_ID"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_ID-ID.csv""}]}, {""config_name"": ""IT_IT"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_IT-IT.csv""}]}, {""config_name"": ""JA_JP"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_JA-JP.csv""}]}, {""config_name"": ""KO_KR"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_KO-KR.csv""}]}, {""config_name"": ""PT_BR"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_PT-BR.csv""}]}, {""config_name"": ""SW_KE"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_SW-KE.csv""}]}, {""config_name"": ""YO_NG"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_YO-NG.csv""}]}, {""config_name"": ""ZH_CN"", ""data_files"": [{""split"": ""test"", ""path"": ""test/mmlu_ZH-CN.csv""}]}], ""language"": [""ar"", ""bn"", ""de"", ""es"", ""fr"", ""hi"", ""id"", ""it"", ""ja"", ""ko"", ""pt"", ""sw"", ""yo"", ""zh""], ""license"": ""mit""}","# Multilingual Massive Multitask Language Understanding (MMMLU) The MMLU is a widely recognized benchmark of general knowledge attained by AI models. It covers a broad range of topics from 57 different categories, covering elementary-level knowledge up to advanced professional subjects like law, physics, history, and computer science. We translated the MMLU’s test set into 14 languages using professional human translators. Relying on human translators for this evaluation increases confidence in the accuracy of the translations, especially for low-resource languages like Yoruba. We are publishing the professional human translations and the code we use to run the evaluations. This effort reflects our commitment to improving the multilingual capabilities of AI models, ensuring they perform accurately across languages, particularly for underrepresented communities. By prioritizing high-quality translations, we aim to make AI technology more inclusive and effective for users worldwide. ## Locales MMMLU contains the MMLU test set translated into the following locales: * AR_XY (Arabic) * BN_BD (Bengali) * DE_DE (German) * ES_LA (Spanish) * FR_FR (French) * HI_IN (Hindi) * ID_ID (Indonesian) * IT_IT (Italian) * JA_JP (Japanese) * KO_KR (Korean) * PT_BR (Brazilian Portuguese) * SW_KE (Swahili) * YO_NG (Yoruba) * ZH_CN (Simplified Chinese) ## Sources Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., & Steinhardt, J. (2021). [*Measuring Massive Multitask Language Understanding*](https://arxiv.org/abs/2009.03300). [OpenAI Simple Evals GitHub Repository](https://github.com/openai/simple-evals)" sentence-transformers/parallel-sentences-opus-100,"{""annotations_creators"": [""no-annotation""], ""language_creators"": [""found""], ""language"": [""af"", ""am"", ""an"", ""ar"", ""as"", ""az"", ""be"", ""bg"", ""bn"", ""br"", ""bs"", ""ca"", ""cs"", ""cy"", ""da"", ""de"", ""dz"", ""el"", ""en"", ""eo"", ""es"", ""et"", ""eu"", ""fa"", ""fi"", ""fr"", ""fy"", ""ga"", ""gd"", ""gl"", ""gu"", ""ha"", ""he"", ""hi"", ""hr"", ""hu"", ""hy"", ""id"", ""ig"", ""is"", ""it"", ""ja"", ""ka"", ""kk"", ""km"", ""kn"", ""ko"", ""ku"", ""ky"", ""li"", ""lt"", ""lv"", ""mg"", ""mk"", ""ml"", ""mn"", ""mr"", ""ms"", ""mt"", ""my"", ""nb"", ""ne"", ""nl"", ""nn"", ""no"", ""oc"", ""or"", ""pa"", ""pl"", ""ps"", ""pt"", ""ro"", ""ru"", ""rw"", ""se"", ""sh"", ""si"", ""sk"", ""sl"", ""sq"", ""sr"", ""sv"", ""ta"", ""te"", ""tg"", ""th"", ""tk"", ""tr"", ""tt"", ""ug"", ""uk"", ""ur"", ""uz"", ""vi"", ""wa"", ""xh"", ""yi"", ""yo"", ""zh"", ""zu""], ""size_categories"": [""10M wc -l * 6206 sharegpt_gpt4.jsonl # 清洗后的高质量gpt4日常问答数据集，大小：6K，主要是知识问答、编程题、推理计算，包括简体中文、繁体中文、英文、日文、韩文等多国语言多轮对话数据集。 58674 sharegpt_V3_format.jsonl # 原V3版sharegpt规范格式后的数据集，大小：58K，主要是日常问答，提问偏口语化，多国语言，多轮对话。 38535 sharegpt_zh_38K_format.jsonl # 中文gpt4日常问答数据集，大小：38K，主要是知识问答、翻译任务、求助、编程推理任务等偏口语提问，中文，多轮对话。 103415 total ``` #### Who are the annotators? 原作者。 ### Licensing Information same to sharegpt. ### Contributions [shibing624](https://github.com/shibing624) add this dataset." GEM/surface_realisation_st_2020,"{""annotations_creators"": [""none""], ""language_creators"": [""unknown""], ""language"": [""ar"", ""zh"", ""en"", ""fr"", ""hi"", ""id"", ""ja"", ""ko"", ""pt"", ""ru"", ""es""], ""license"": [""cc-by-2.5""], ""multilinguality"": [""unknown""], ""size_categories"": [""unknown""], ""source_datasets"": [""original""], ""task_categories"": [""table-to-text""], ""task_ids"": [], ""pretty_name"": ""surface_realisation_st_2020"", ""tags"": [""data-to-text""]}","# Dataset Card for GEM/surface_realisation_st_2020 ## Dataset Description - **Homepage:** http://taln.upf.edu/pages/msr2020-ws/SRST.html#data - **Repository:** https://sites.google.com/site/genchalrepository/surface-realisation/sr-20-multilingual - **Paper:** https://aclanthology.org/2020.msr-1.1/ - **Leaderboard:** N/A - **Point of Contact:** Simon Mille ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/surface_realisation_st_2020). ### Dataset Summary This dataset was used as part of the multilingual surface realization shared task in which a model gets full or partial universal dependency structures and has to reconstruct the natural language. This dataset support 11 languages. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/surface_realisation_st_2020') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/surface_realisation_st_2020). #### website [Website](http://taln.upf.edu/pages/msr2020-ws/SRST.html#data) #### paper [ACL Anthology](https://aclanthology.org/2020.msr-1.1/) #### authors Simon Mille (Pompeu Fabra University); Leo Wanner (Pompeu Fabra University); Anya Belz (Brighton University); Bernd Bohnet (Google Inc.); Thiago Castro Ferreira (Federal University of Minas Gerais); Yvette Graham (ADAPT/Trinity College Dublin) ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage [Website](http://taln.upf.edu/pages/msr2020-ws/SRST.html#data) #### Download [Website](https://sites.google.com/site/genchalrepository/surface-realisation/sr-20-multilingual) #### Paper [ACL Anthology](https://aclanthology.org/2020.msr-1.1/) #### BibTex ``` @inproceedings{mille-etal-2020-third, title = ""The Third Multilingual Surface Realisation Shared Task ({SR}{'}20): Overview and Evaluation Results"", author = ""Mille, Simon and Belz, Anya and Bohnet, Bernd and Castro Ferreira, Thiago and Graham, Yvette and Wanner, Leo"", booktitle = ""Proceedings of the Third Workshop on Multilingual Surface Realisation"", month = dec, year = ""2020"", address = ""Barcelona, Spain (Online)"", publisher = ""Association for Computational Linguistics"", url = ""https://aclanthology.org/2020.msr-1.1"", pages = ""1--20"", abstract = ""This paper presents results from the Third Shared Task on Multilingual Surface Realisation (SR{'}20) which was organised as part of the COLING{'}20 Workshop on Multilingual Surface Realisation. As in SR{'}18 and SR{'}19, the shared task comprised two tracks: (1) a Shallow Track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (2) a Deep Track where additionally, functional words and morphological information were removed. Moreover, each track had two subtracks: (a) restricted-resource, where only the data provided or approved as part of a track could be used for training models, and (b) open-resource, where any data could be used. The Shallow Track was offered in 11 languages, whereas the Deep Track in 3 ones. Systems were evaluated using both automatic metrics and direct assessment by human evaluators in terms of Readability and Meaning Similarity to reference outputs. We present the evaluation results, along with descriptions of the SR{'}19 tracks, data and evaluation methods, as well as brief summaries of the participating systems. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume."", } ``` #### Contact Name Simon Mille #### Contact Email sfmille@gmail.com #### Has a Leaderboard? no ### Languages and Intended Use #### Multilingual? yes #### Covered Dialects No multiple dialects. #### Covered Languages `Arabic`, `Chinese`, `English`, `French`, `Hindi`, `Indonesian`, `Japanese`, `Korean`, `Portuguese`, `Russian`, `Spanish, Castilian` #### Whose Language? Unknown #### License cc-by-2.5: Creative Commons Attribution 2.5 Generic #### Intended Use The dataset is intended to be used for training models to solve several NLG subtasks, such as function word introduction, morphological agreement resolution, word order determination and inflection generation. Comment about the license: the dataset has multiple licences, since each original dataset has their own type of licence. All datasets but one are CC-BY and subclasses of it, the other one is GPL (French Sequoia). #### Primary Task Data-to-Text #### Communicative Goal The models are able to introduce surface features (syntax, morphology, topology) from more or less abstract inputs in different, the most abstract being predicate-argument structures. The datasets cover a large variety of domains (news, blogs, forums, wikipedia pages, etc.). ### Credit #### Curation Organization Type(s) `industry`, `academic` #### Curation Organization(s) Pompeu Fabra University, Google Inc., University of Brighton, Federal University of Minas Gerais, ADAPT/Trinity College Dublin #### Dataset Creators Simon Mille (Pompeu Fabra University); Leo Wanner (Pompeu Fabra University); Anya Belz (Brighton University); Bernd Bohnet (Google Inc.); Thiago Castro Ferreira (Federal University of Minas Gerais); Yvette Graham (ADAPT/Trinity College Dublin) #### Funding Mostly EU funds via H2020 projects #### Who added the Dataset to GEM? Simon Mille (Pompeu Fabra University) ### Dataset Structure #### Data Fields `input` (string): this field contains an input tree in CoNLL-U format; the CoNLL-U format is a one-word-per-line format with the following tab-separated 10 columns (see [here](http://universaldependencies.org/format.html)): [1] Position, [2] Lemma, [3] Wordform, [4] Part of Speech, [5] Fine-grained Part of Speech (if available), [6] Features (FEATS), [7] governor, [8] dependency relation, [9] additional dependency information, and [10] metadata. For the surface task, the input is a Universal Dependency tree of a given language in which the word order was scrambled and the surface forms removed (only lemmas are available); for the deep task, the input is a tree derived from the surface input, with predicate-argument relations between content words only (function words were removed) and without any morphological agreement information. `target_tokenized` (string): this field contains the target sentence to generate, in which every non-initial and non-final token is surrounded by two spaces. This output is usually used for automatic evaluations. `target` (string): this field contains the detokenised target sentence to generate. This output is usually used for human evaluations. `gem_id` (string): a unique ID. `sentence_id` (string): the original ID of a sentence in the UD dataset. #### Reason for Structure The structure of the input (CoNLL-U) was chosen according to the standards in parsing, and because the original UD datasets were provided in this format. #### How were labels chosen? The input labels for the surface track are the original labels in the UD treebanks; see [here](https://universaldependencies.org/u/dep/index.html) for the dependencies, [here](https://universaldependencies.org/u/feat/index.html) for the features, and [here](https://universaldependencies.org/u/pos/index.html) for the PoS tags. The input labels for the deep track are a subset of the PoS tags and features of the surface track, and for the relations, universal predicate-argument relations augmented with a few specific relations to capture coordinations and named entity relations for instance. #### Example Instance ``` {""input"": ""1\tGoogle\t_\tPROPN\tNNP\tNumber=Sing\t5\tnsubj\t_\t_\n2\t\t_\tPUNCT\t.\tlin=+1\t5\tpunct\t_\t_\n3\tinto\t_\tADP\tIN\t_\t6\tcase\t_\t_\n4\tif\t_\tSCONJ\tIN\t_\t5\tmark\t_\t_\n5\tmorph\t_\tVERB\tVBD\tMood=Ind|Tense=Past|VerbForm=Fin\t7\tadvcl\t_\t_\n6\tGoogleOS\t_\tPROPN\tNNP\tNumber=Sing\t5\tobl\t_\t_\n7\twhat\t_\tPRON\tWP\tPronType=Int\t0\troot\t_\t_"", ""target_tokenized"": ""What if Google Morphed Into GoogleOS ?"", ""target"": ""What if Google Morphed Into GoogleOS?"", ""gem_id"": ""GEM-surface_realisation_st_2020-T1-test-en_ewt-ud-test-0"", ""sentence_id"": """"} ``` #### Data Splits There are 119 splits in the dataset: - 29 training sets, which correspond to 20 UD datasets (11 languages), 9 of which have both surface and deep inputs (3 languages); - 29 development set which correspond to the 29 training sets above; - 29 test sets for the data described above; - 4 out-of-domain test sets, 3 surface inputs and 1 deep one (3 languages for which PUD out-of-domain datasets were available); - 9 automatically parsed in-domain test sets, 6 surface inputs and 3 deep inputs (6 languages for which good UD parsers were available); - 9 automatically parsed out-of-domain test sets, 6 surface inputs and 3 deep inputs (6 languages for which we were able to create clean Wikipedia text and that had a good UD parser). #### Splitting Criteria Described above for more clarity. #### An outlier would usually be an input that corresponds to a very long sentence (e.g. 159 words in English, when the average number of words per sentence is around 25). ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? The datset includes languages from different families and some languages not often used in NLG (e.g. Arabic, Indonesian, Korean, Hindi). It proposes two tasks, which can be tackled both separately and in one shot, with different levels of difficulty: the most superficial task (T1) consits in ordering and inflecting some trees, and the deeper task (T2) includes extra tasks such as defining the syntactic structure and introducing function words and morphological agreement information. Both tasks can allow for developing modules for pipeline NLG architectures. T1 is rather straightforward to evaluate: BLEU works quite well for some languages since all the words are present in the input and few word orders only can be possible for a syntactic tree. But T2 is more challenging to evaluate, since more outputs are correct given one particular input. There is a large variety of sizes in the datasets, both clean and noisy data, parallel data in different languages, and many already available system outputs to use as baselines. #### Similar Datasets yes #### Unique Language Coverage yes #### Difference from other GEM datasets This is possibly the only dataset that starts the generation process from predicate-argument structures and from syntactic structures. It also has parallel datasets in a few languages (coming from the PUD parallel annotations). #### Ability that the Dataset measures Syntacticisation, functional word introduction, word order resolution, agreement resolution, morphological inflection ### GEM-Specific Curation #### Modificatied for GEM? no #### Additional Splits? no ### Getting Started with the Task #### Pointers to Resources [Website](http://taln.upf.edu/pages/msr2020-ws/SRST.html) #### Technical Terms Syntacticisation: prediction of the syntactic ## Previous Results ### Previous Results #### Measured Model Abilities Syntacticisation, functional word introduction, word order resolution, morphological agreement resolution, morphological inflection #### Metrics `BLEU`, `BERT-Score`, `Other: Other Metrics` #### Other Metrics NIST: n-gram similarity metric weighted in favour of less frequent n-grams which are taken to be more informative. Normalised edit distance (DIST): inverse, normalised, character-based string-edit distance that starts by computing the minimum number of character inserts, deletes and substitutions (all at cost 1) required to turn the system output into the (single) reference text. #### Proposed Evaluation BLEU, NIST, BERTScore and DIST simply aim at calculating in different ways the similarity between a predicted and a reference sentence. Two additional criteria have been used for human evaluation, Readability and Meaning SImilarity. The statement to be assessed in the Readability evaluation was: ""The text reads well and is free from grammatical errors and awkward constructions."". The corresponding statement in the Meaning Similarity evaluation, in which system outputs (‘the black text’) were compared to reference sentences (‘the gray text’), was: ""The meaning of the gray text is adequately expressed by the black text."" #### Previous results available? yes #### Other Evaluation Approaches Same as above. #### Relevant Previous Results - [Fast and Accurate Non-Projective Dependency Tree Linearization](https://aclanthology.org/2020.acl-main.134/) - [Shape of Synth to Come: Why We Should Use Synthetic Data for English Surface Realization](https://aclanthology.org/2020.acl-main.665/) ## Dataset Curation ### Original Curation #### Original Curation Rationale The datasets were created in the context of the Surface Realisation Shared Task series. #### Communicative Goal The dataset's objective was to allow for training systems to perform tasks related to surface realisation (introduction of function words, syntacticisation, resolution of morphological agreements, word order resolution, inflection generation. #### Sourced from Different Sources yes #### Source Details Each of the 20 used UD datasets comes from various sources, all listed on the individual page of each UD treeebank (https://universaldependencies.org/). Additional test sets were created for the task, and were obtained from Wikipedia pages for 6 languages. ### Language Data #### How was Language Data Obtained? `Found` #### Where was it found? `Multiple websites` #### Language Producers There are numerous sources of language in the multiple datasets. #### Topics Covered There is a large variety of topics in the multiple datasets. #### Data Validation not validated #### Data Preprocessing The text data was detokenised so as to create references for automatic evaluations (several languages don't use spaces to separate words, and running metrics like BLEU would not make sense without separating all the tokens in a sentence). #### Was Data Filtered? hybrid #### Filter Criteria For the Wikipedia test created for the shared task, extensive filtering was applied to achieve reasonably good text quality. Sentences that include special characters, contain unusual tokens (e.g. ISBN), or have unbalanced quotation marks or brackets were skipped. Furthermore, only sentences with more than 5 tokens and shorter than 50 tokens were selected. After the initial filtering, quite a few malformed sentences remained. In order to remove those, the sentences were scored with BERT and only the top half scored sentences were kept. Finally, via manual inspection, patterns and expressions were identified to further reduce the number of malformed sentences. ### Structured Annotations #### Additional Annotations? none #### Annotation Service? no ### Consent #### Any Consent Policy? no #### Justification for Using the Data The Universal Dependency data had been previously used for shared tasks on parsing, so it made sense to reuse it for generation. ### Private Identifying Information (PII) #### Contains PII? unlikely #### Any PII Identification? no identification ### Maintenance #### Any Maintenance Plan? no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? yes #### Details on how Dataset Addresses the Needs Thanks to the original work of the UD dataset creators, the surface realisation dataset addresses a few languages which are possibly under-served in NLG: e.g. Arabic, Hindi, Indonesian, Korean. ### Discussion of Biases #### Any Documented Social Biases? no #### Are the Language Producers Representative of the Language? It is very likely that the distribution of language producers is not fully represented in the datasets of each language. ## Considerations for Using the Data ### PII Risks and Liability #### Potential PII Risk No risks foreseen. ### Licenses #### Copyright Restrictions on the Dataset `multiple licenses`, `open license - commercial use allowed` #### Copyright Restrictions on the Language Data `multiple licenses`, `open license - commercial use allowed` ### Known Technical Limitations #### Technical Limitations The deep track inputs (predicate-argument structures) are not of perfect quality, they were derived automatically from gold or predicted syntactic parses using handcrafted grammars. #### Unsuited Applications The datasets are probably not fitted to train tools to produce ""unusual"" languages (e.g. poetry, kid writing etc.). #### Discouraged Use Cases To be thought of :)" royboy0416/ko-alpaca,"{""license"": ""cc-by-4.0"", ""task_categories"": [""text-generation""], ""language"": [""ko""]}","Testing purpose only. Do not redistribute. Original contents: [url] https://huggingface.co/datasets/tatsu-lab/alpaca Ko-alpaca: [url] https://github.com/Beomi/KoAlpaca/blob/main/ko_alpaca_data.json" sean0042/KorMedMCQA,"{""language"": [""ko""], ""license"": ""cc-by-nc-2.0"", ""size_categories"": [""10K>> from datasets import load_dataset >>> dataset = load_dataset(""Bingsu/zeroth-korean"") >>> dataset DatasetDict({ train: Dataset({ features: ['audio', 'text'], num_rows: 22263 }) test: Dataset({ features: ['text', 'audio'], num_rows: 457 }) }) ``` ### Data Size download: 2.68 GiB
generated: 2.85 GiB
total: 5.52 GiB ### Data Fields - audio: `audio`, sampling rate = 16000 - A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - Note that when accessing the audio column: `dataset[0][""audio""]` the audio file is automatically decoded and resampled to `dataset.features[""audio""].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the ""audio"" column, i.e. `dataset[0][""audio""]` should always be preferred over `dataset[""audio""][0]`. - text: `string` ```pycon >>> dataset[""train""][0] {'audio': {'path': None, 'array': array([-3.0517578e-05, 0.0000000e+00, -3.0517578e-05, ..., 0.0000000e+00, 0.0000000e+00, -6.1035156e-05], dtype=float32), 'sampling_rate': 16000}, 'text': '인사를 결정하는 과정에서 당 지도부가 우 원내대표 및 원내지도부와 충분한 상의를 거치지 않은 채 일방적으로 인사를 했다는 불만도 원내지도부를 중심으로 흘러나왔다'} ``` ### Data Splits | | train | test | | ---------- | -------- | ----- | | # of data | 22263 | 457 |" MarkrAI/KoCommercial-Dataset,"{""language"": [""ko""], ""license"": ""mit"", ""configs"": [{""config_name"": ""default"", ""data_files"": [{""split"": ""train"", ""path"": ""data/train-*""}]}], ""dataset_info"": {""features"": [{""name"": ""input"", ""dtype"": ""string""}, {""name"": ""instruction"", ""dtype"": ""string""}, {""name"": ""output"", ""dtype"": ""string""}], ""splits"": [{""name"": ""train"", ""num_bytes"": 187990458, ""num_examples"": 175454}], ""download_size"": 110149618, ""dataset_size"": 187990458}}","# SSL 데이터 생성을 위한 코드 공개 **[SSL 데이터 생성용 Github Repo](https://github.com/DopeorNope-Lee/Ko-Fine-tuning_DataGen)** - NIA와 AI-Hub와의 저작권 협의 하에, 조금 혼선이 생긴것 죄송합니다. - 이에 기존에 저희가 code베이스로 SSL 데이터를 생성했던 코드를 그대로 공개드립니다. - 다만, 이 과정에서는 저희 이후 파이프라인인, 자체 로컬 모델을 가지고 필터링하거나 수정하는 과정이 없어, 어느정도 감안을 해주시면 감사하겠습니다. - 코드는 누구나 사용하실 수 있고 과제와 Task에 맞게 활용하시면 감사하겠습니다! -------------------- # Dataset: KoCommercial-Dataset ## Info **Dataset 개수:** 약 1.44M **License:** MIT **Dataset list(전부 상업적 용도로 이용가능)** 1. [kyujinpy/KOpen-platypus](kyujinpy/KOpen-platypus) (*Except non-commercial datasets) 2. [beomi/KoAlpaca-v1.1a](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a) 3. [HumanF-MarkrAI/WIKI_QA_Near_dedup](https://huggingface.co/datasets/HumanF-MarkrAI/WIKI_QA_Near_dedup) 4. [KorQuadv1.0](https://korquad.github.io/KorQuad%201.0/) 5. [AIHUB](https://www.aihub.or.kr/)(AIHUB데이터는, 위의 github주소를 통해, 데이터를 생성하셔 사용하시면 됩니다.) - [일반상식 문장 생성 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=713090) - [도서자료 요약](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=93) - [논문자료 요약](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=90) - [문서요약 텍스트](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=97) --- **Self-Supervised method(AIHUB 데이터셋 가공)** **0. (Default) Summary & Instruction-Answer** ``` 주어진 문장에 적절한 제목을 생성하고, 내용을 요약해주세요. 문장: 원고가 소속회사의 노동조합에서 분규가 발생하자 노조활동을 구실로 정상적인 근무를 해태하고, ... 제목: 부당노동행위구제재심판정취소 원고가 주동하여 회사업무능률을 저해하고 회사업무상의 지휘명령에 위반하였다면 이에 따른 징계해고는 사내질서를 유지하기 위한 사용자 고유의 정당한 징계권의 행사로 보아야 한다. ``` **1. Sentence order inference** ``` 임의의 순서로 나열된 문장들이 주어집니다. 주어진 문장들을 이용해 원본의 배열을 유추하고, 그 내용을 재구성하세요. 임의의 순서로 나열된 문장: ['나는', '천재다', '그러나', '바보다', '동시에'] 나는 천재다. 그러나 동시에 바보다. ``` **2. Original sentence inference** ``` 주어진 제목과 요약문에 대한 정보를 토대로, 요약되기 전 문장을 유추해서 생성해주세요. 제목: 수산물 수급 위기관리체계 구축을 위한 기초연구 요약문: 현대 사회에서 발생하는 다양하고... 지금의 국가가 직면하는 위기는 전통사회의 그것과 위기의 규모뿐만아니라... ``` **3. Last sentence prediction** ``` 주어진 문장 뒤에 자연스럽게 이어질 문장을 생성해주세요. 문장: ...최근에 방문한 조선예술영화촬영소 에 있는 ‘문화성혁명사적관’(김정일관)에는 1960년대 중반부터 2000년대까지 40년 동안 김정일의 문화예술 부문 지도가 11,890건이며, 그 중 문화예술기관을 직접 방문하여 지도한 이른바 ‘현지지도’가 1,770건이라는 안내판이 있었다. 북한 연극이 김정일과 주체사상이라는 키워드를 떠나 존재할 수 없다는 것을 단적으로 말해 준다 ``` **4. Multi question** ``` 주어진 정보를 기반으로 질문에 답하세요. 답을 모른다면 답을 지어내지 말고 그냥 모른다고 말하세요. 1839년 바그너는 괴테의 파우스트을 처음 읽고 그 내용에 마음이... 질문: 1. 바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가? 2. 바그너는 교향곡 작곡을 어디까지 쓴 뒤에 중단했는가? 3. 바그너가 파우스트 서곡을 쓸 때 어떤 곡의 영향을 받았는가? 4. 1839년 바그너가 교향곡의 소재로 쓰려고 했던 책은? 5. 파우스트 서곡의 라단조 조성이 영향을 받은 베토벤의 곡은? 6. 바그너가 파우스트를 처음으로 읽은 년도는? 7. 바그너가 처음 교향곡 작곡을 한 장소는? 8. 바그너의 1악장의 초연은 어디서 연주되었는가? 1. 교향곡 2. 1악장 3. 베토벤의 교향곡 9번 4. 파우스트 5. 합창교향곡 6. 1839 7. 파리 8. 드레스덴 ``` **5. Mask Prediction** ``` 주어진 문장에서 에 들어갈 적절한 단어를 생성해주세요. 독도는 이다. 우리땅 ``` --- # References 1.[The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning (Kim et al., 2023)](https://huggingface.co/papers/2305.14045) 2.[Adapting Large Language Models via Reading Comprehension (Cheng et al., 2023)](https://huggingface.co/papers/2309.09530) 3.[Deduplicating Training Data Makes Language Models Better(Lee et al., 2021)](https://huggingface.co/papers/2107.06499) --- # Acknowledgement - 이 모델은 과학기술정보통신부·광주광역시가 공동 지원한 '인공지능 중심 산업융합 집적단지 조성사업'으로 지원을 받아 수행된 연구 결과입니다. - This model was supported by Artificial intelligence industrial convergence cluster development project funded by the Ministry of Science and ICT(MSIT, Korea)&Gwangju Metropolitan City. - 또한 수많은 오픈소스 개발자들과 연구자들에게 감사드리고, 최근 한국어 LLM 오픈생태계에 많은 공헌을 해주신, [Beomi](https://huggingface.co/beomi) 님과 [maywell](https://huggingface.co/maywell) 님에게도 감사의 인사 드립니다." jeanlee/kmhas_korean_hate_speech,"{""annotations_creators"": [""crowdsourced""], ""language"": [""ko""], ""language_creators"": [""found""], ""license"": [""cc-by-sa-4.0""], ""multilinguality"": [""monolingual""], ""pretty_name"": ""K-MHaS"", ""size_categories"": [""100K ## Dataset Description - **Homepage:** [K-MHaS](https://github.com/adlnlp/K-MHaS) - **Repository:** [Korean Multi-label Hate Speech Dataset](https://github.com/adlnlp/K-MHaS) - **Paper:** [K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment](https://arxiv.org/abs/2208.10684) - **Point of Contact:** [Caren Han](caren.han@sydney.edu.au) - **Sample code:** [Colab](https://colab.research.google.com/drive/171KhS1_LVBtpAFd_kaT8lcrZmhcz5ehY?usp=sharing) ### Dataset Summary The Korean Multi-label Hate Speech Dataset, **K-MHaS**, consists of 109,692 utterances from Korean online news comments, labelled with 8 fine-grained hate speech classes (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`) or `Not Hate Speech` class. Each utterance provides from a single to four labels that can handles Korean language patterns effectively. For more details, please refer to our paper about [**K-MHaS**](https://aclanthology.org/2022.coling-1.311), published at COLING 2022. ### Supported Tasks and Leaderboards Hate Speech Detection * `binary classification` (labels: `Hate Speech`, `Not Hate Speech`) * `multi-label classification`: (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`, `Not Hate Speech`) For the multi-label classification, a `Hate Speech` class from the binary classification, is broken down into eight classes, associated with the hate speech category. In order to reflect the social and historical context, we select the eight hate speech classes. For example, the `Politics` class is chosen, due to a significant influence on the style of Korean hate speech. ### Languages Korean ## Dataset Structure ### Data Instances The dataset is provided with train/validation/test set in the txt format. Each instance is a news comment with a corresponding one or more hate speech classes (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`) or `Not Hate Speech` class. The label numbers matching in both English and Korean is in the data fields section. ```python {'text':'수꼴틀딱시키들이 다 디져야 나라가 똑바로 될것같다..답이 없는 종자들ㅠ' 'label': [2, 3, 4] } ``` ### Data Fields * `text`: utterance from Korean online news comment. * `label`: the label numbers matching with 8 fine-grained hate speech classes and `not hate speech` class are follows. * `0`: `Origin`(`출신차별`) hate speech based on place of origin or identity; * `1`: `Physical`(`외모차별`) hate speech based on physical appearance (e.g. body, face) or disability; * `2`: `Politics`(`정치성향차별`) hate speech based on political stance; * `3`: `Profanity`(`혐오욕설`) hate speech in the form of swearing, cursing, cussing, obscene words, or expletives; or an unspecified hate speech category; * `4`: `Age`(`연령차별`) hate speech based on age; * `5`: `Gender`(`성차별`) hate speech based on gender or sexual orientation (e.g. woman, homosexual); * `6`: `Race`(`인종차별`) hate speech based on ethnicity; * `7`: `Religion`(`종교차별`) hate speech based on religion; * `8`: `Not Hate Speech`(`해당사항없음`). ### Data Splits In our repository, we provide splitted datasets that have 78,977(train) / 8,776 (validation) / 21,939 (test) samples, preserving the class proportion. ## Dataset Creation ### Curation Rationale We propose K-MHaS, a large size Korean multi-label hate speech detection dataset that represents Korean language patterns effectively. Most datasets in hate speech research are annotated using a single label classification of particular aspects, even though the subjectivity of hate speech cannot be explained with a mutually exclusive annotation scheme. We propose a multi-label hate speech annotation scheme that allows overlapping labels associated with the subjectivity and the intersectionality of hate speech. ### Source Data #### Initial Data Collection and Normalization Our dataset is based on the Korean online news comments available on Kaggle and Github. The unlabeled raw data was collected between January 2018 and June 2020. Please see the details in our paper [K-MHaS](https://aclanthology.org/2022.coling-1.311) published at COLING2020. #### Who are the source language producers? The language producers are users who left the comments on the Korean online news platform between 2018 and 2020. ### Annotations #### Annotation process We begin with the common categories of hate speech found in literature and match the keywords for each category. After the preliminary round, we investigate the results to merge or remove labels in order to provide the most representative subtype labels of hate speech contextual to the cultural background. Our annotation instructions explain a twolayered annotation to (a) distinguish hate and not hate speech, and (b) the categories of hate speech. Annotators are requested to consider given keywords or alternatives of each category within social, cultural, and historical circumstances. For more details, please refer to the paper [K-MHaS](https://aclanthology.org/2022.coling-1.311). #### Who are the annotators? Five native speakers were recruited for manual annotation in both the preliminary and main rounds. ### Personal and Sensitive Information This datasets contains examples of hateful language, however, has no personal information. ## Considerations for Using the Data ### Social Impact of Dataset We propose K-MHaS, a new large-sized dataset for Korean hate speech detection with a multi-label annotation scheme. We provided extensive baseline experiment results, presenting the usability of a dataset to detect Korean language patterns in hate speech. ### Discussion of Biases All annotators were recruited from a crowdsourcing platform. They were informed about hate speech before handling the data. Our instructions allowed them to feel free to leave if they were uncomfortable with the content. With respect to the potential risks, we note that the subjectivity of human annotation would impact on the quality of the dataset. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset is curated by Taejun Lim, Heejun Lee and Bogeun Jo. ### Licensing Information Creative Commons Attribution-ShareAlike 4.0 International (cc-by-sa-4.0). ### Citation Information ``` @inproceedings{lee-etal-2022-k, title = ""K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment"", author = ""Lee, Jean and Lim, Taejun and Lee, Heejun and Jo, Bogeun and Kim, Yangsok and Yoon, Heegeun and Han, Soyeon Caren"", booktitle = ""Proceedings of the 29th International Conference on Computational Linguistics"", month = oct, year = ""2022"", address = ""Gyeongju, Republic of Korea"", publisher = ""International Committee on Computational Linguistics"", url = ""https://aclanthology.org/2022.coling-1.311"", pages = ""3530--3538"", abstract = ""Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baselines on K-MHaS. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class."", } ``` ### Contributions The contributors of the work are: - [Jean Lee](https://jeanlee-ai.github.io/) (The University of Sydney) - [Taejun Lim](https://github.com/taezun) (The University of Sydney) - [Heejun Lee](https://bigwaveai.com/) (BigWave AI) - [Bogeun Jo](https://bigwaveai.com/) (BigWave AI) - Yangsok Kim (Keimyung University) - Heegeun Yoon (National Information Society Agency) - [Soyeon Caren Han](https://drcarenhan.github.io/) (The University of Western Australia and The University of Sydney)" djstrong/oscar-small,"{""annotations_creators"": [""no-annotation""], ""language_creators"": [""found""], ""language"": [""af"", ""am"", ""ar"", ""arz"", ""as"", ""az"", ""azb"", ""ba"", ""be"", ""bg"", ""bn"", ""bo"", ""br"", ""ca"", ""ce"", ""ceb"", ""ckb"", ""cs"", ""cv"", ""cy"", ""da"", ""de"", ""dv"", ""el"", ""en"", ""eo"", ""es"", ""et"", ""eu"", ""fa"", ""fi"", ""fr"", ""fy"", ""ga"", ""gl"", ""gu"", ""he"", ""hi"", ""hr"", ""hu"", ""hy"", ""id"", ""is"", ""it"", ""ja"", ""ka"", ""kk"", ""km"", ""kn"", ""ko"", ""ku"", ""ky"", ""la"", ""lb"", ""lo"", ""lt"", ""lv"", ""mg"", ""mhr"", ""mk"", ""ml"", ""mn"", ""mr"", ""ms"", ""mt"", ""my"", ""nds"", ""ne"", ""nl"", ""nn"", ""no"", ""or"", ""os"", ""pa"", ""pl"", ""pnb"", ""ps"", ""pt"", ""ro"", ""ru"", ""sa"", ""sah"", ""sd"", ""sh"", ""si"", ""sk"", ""sl"", ""sq"", ""sr"", ""sv"", ""sw"", ""ta"", ""te"", ""tg"", ""th"", ""tk"", ""tl"", ""tr"", ""tt"", ""ug"", ""uk"", ""ur"", ""uz"", ""vi"", ""yi"", ""zh""], ""license"": [""cc0-1.0""], ""multilinguality"": [""multilingual""], ""source_datasets"": [""oscar""], ""task_categories"": [""text-generation""], ""task_ids"": [""language-modeling""], ""paperswithcode_id"": ""oscar"", ""pretty_name"": ""OSCAR""}","## WARNING: this dataset is an extract of the OSCAR dataset published here to simulate the use of the full dataset in low-resource contexts. Using this dataset is equivalent to using a processed version of OSCAR legally speaking. I take no credit for the gathering of the original data and hence refer entirely to the original dataset in the card below. # Dataset Card for ""oscar"" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://oscar-corpus.com](https://oscar-corpus.com) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary OSCAR or **O**pen **S**uper-large **C**rawled [**A**LMAnaCH](https://team.inria.fr/almanach/) co**R**pus is a huge multilingual corpus obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) corpus using the [goclassy](https://github.com/pjox/goclassy) architecture. Data is distributed by language in both original and deduplicated form. ### Supported Tasks and Leaderboards OSCAR is mainly inteded to pretrain language models and word represantations. ### Languages All the data is distributed by language, both the original and the deduplicated versions of the data are available. 166 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR. ## Dataset Structure We show detailed information for all the configurations of the dataset. ## Dataset Creation ### Curation Rationale OSCAR was constructed new pipeline derived from the [fastText's one](https://github.com/facebookresearch/fastText), called [_goclassy_](https://github.com/pjox/goclassy). Goclassy reuses the [fastText linear classifier](https://fasttext.cc) and the pre-trained fastText model for language recognition, but it completely rewrites and parallelises their pipeline in an asynchronous manner. The order of operations is more or less the same as in the fastText pre-processing pipeline but instead of clustering multiple operations into a single blocking process, a worker is launched for each operation but bounding the number of possible parallel operations at a given time by the number of available threads instead of the number of CPUs. Goclassy is implemented in the [Go programming language](https://golang.org/) so it lets the [Go runtime](https://golang.org/src/runtime/mprof.go) handle the scheduling of the processes. Thus the goclassy's pipeline one does not have to wait for a whole WET file to download, decompress and classify in order to start downloading and processing the next one, a new file will start downloading and processing as soon as the scheduler is able to allocate a new process. Filtering and cleaning processes at line level are done before feeding each line to the classifier. Lines shorter than 100 UTF-8 characters and lines containing invalid UTF-8 characters are discarted and are not classified. After all files are proccesed the deduplicated versions are constructed and everything is then splitted in shards and compressed. ### Source Data #### Initial Data Collection and Normalization [Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies. Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics. To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR, the **November 2018** snapshot was used. It surpasses 20TB of uncompressed data and contains more than 50 thousand plain text files where each file consists of the plain text from multiple websites along its metadata header. #### Who are the source language producers? The data comes from multiple web pages in a large variety of languages. ### Annotations The dataset does not contain any additional annotations. #### Annotation process N/A #### Who are the annotators? N/A ### Personal and Sensitive Information Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models. ## Considerations for Using the Data ### Social Impact of Dataset OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures. ### Discussion of Biases OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models. ### Other Known Limitations The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571). ## Additional Information ### Dataset Curators The corpus was put together by [Pedro J. Ortiz](https://pjortiz.eu/), [BenoÃ®t Sagot](http://pauillac.inria.fr/~sagot/), and [Laurent Romary](https://cv.archives-ouvertes.fr/laurentromary), during work done at [Inria](https://www.inria.fr/en), particularly at the [ALMAnaCH team](https://team.inria.fr/almanach/). ### Licensing Information These data are released under this licensing scheme We do not own any of the text from which these data has been extracted. We license the actual packaging of these data under the Creative Commons CC0 license (""no rights reserved"") http://creativecommons.org/publicdomain/zero/1.0/ To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR This work is published from: France. Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: * Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. * Clearly identify the copyrighted work claimed to be infringed. * Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material. We will comply to legitimate requests by removing the affected sources from the next release of the corpus. ### Citation Information ``` @inproceedings{ortiz-suarez-etal-2020-monolingual, title = ""A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages"", author = ""Ortiz Su{'a}rez, Pedro Javier and Romary, Laurent and Sagot, Benoit"", booktitle = ""Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"", month = jul, year = ""2020"", address = ""Online"", publisher = ""Association for Computational Linguistics"", url = ""https://www.aclweb.org/anthology/2020.acl-main.156"", pages = ""1703--1714"", abstract = ""We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures."", } @inproceedings{OrtizSuarezSagotRomary2019, author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary}, title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures}, series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019}, editor = {Piotr BaÅ„ski and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{""u}ngen and Caroline Iliadi}, publisher = {Leibniz-Institut f{""u}r Deutsche Sprache}, address = {Mannheim}, doi = {10.14618/ids-pub-9021}, url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215}, pages = {9 -- 16}, year = {2019}, abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.}, language = {en} } ``` ### Contributions Thanks to [@pjox](https://github.com/pjox) and [@lhoestq](https://github.com/lhoestq) for adding this dataset." lcw99/wikipedia-korean-20221001,"{""language"": [""ko""], ""license"": ""apache-2.0""}",[20240501 update](https://huggingface.co/datasets/lcw99/wikipedia-korean-20240501) kyujinpy/KOpen-platypus,"{""language"": [""en"", ""ko""], ""license"": ""cc-by-4.0"", ""size_categories"": [""10K Post-processing 작업 내용 - Add post-processing (v2) +) 단답형 Task 삭제. ## OpenOrca-Ko-v2 1. NIV // 약 1500개 2. FLAN // 약 9000개 3. T0 // 약 6000개 4. CoT // 약 2000개 > Dataset 구성 - 수작업으로 고친 내용(v2) 1. 영어로 된 답변 수정. (Ex. Nick -> 닉, Lucky -> 운이 좋음, ...) 2. KoCoT 데이터셋 제거. 3. Yes, True, False 등등 일부 답변 수정 > Post-processing 작업 내용 ## Translation Using DeepL Pro API. Thanks. --- >Below is original dataset card ## Table of Contents - [Dataset Summary](#dataset-summary) - [Dataset Attribution](#dataset-attribution) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Dataset Use](#dataset-use) - [Use Cases](#use-cases) - [Usage Caveats](#usage-caveats) - [Getting Started](#getting-started)

🐋 The OpenOrca Dataset! 🐋

![OpenOrca Logo](https://huggingface.co/datasets/Open-Orca/OpenOrca/resolve/main/OpenOrcaLogo.png ""OpenOrca Logo"") We are thrilled to announce the release of the OpenOrca dataset! This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707). It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers! # Official Models ## OpenOrca-Platypus2-13B Our [latest release](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard! Released in partnership with Platypus. ## LlongOrca 7B & 13B * Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance. * [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance. ## OpenOrcaxOpenChat-Preview2-13B Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper. Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B. Released in partnership with OpenChat. ## OpenOrca-Preview1-13B [OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B) This model was trained in less than a day, for <$200, with <10% of our data. At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper. # Dataset Summary The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688). Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions. It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope. The data is primarily used for training and evaluation in the field of natural language processing. # Dataset Attribution We would like to give special recognition to the following contributors for their significant efforts and dedication: Teknium WingLian/Caseus Eric Hartford NanoBit Pankaj Winddude Rohan http://AlignmentLab.ai: Autometa Entropi AtlasUnified NeverendingToast NanoBit WingLian/Caseus Also of course, as always, TheBloke, for being the backbone of the whole community. Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others! We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials: http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2). [](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2) # Supported Tasks and Leaderboards This dataset supports a range of tasks including language modeling, text generation, and text augmentation. It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing. Further information on leaderboards will be updated as they become available. # Languages The language of the data is primarily English. # Dataset Structure ## Data Instances A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5. The response is then entered into the response field. ## Data Fields The fields are: 1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from. 2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint 3) 'question', representing a question entry as provided by the FLAN Collection 4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4. ## Data Splits The data is unsplit. # Dataset Creation ## Curation Rationale The dataset was created to provide a source of augmented text data for researchers and developers. The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4. This ""reasoning trace"" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on. ## Source Data The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below: 1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use. We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available. 2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original). These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source. However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively. Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work. # Dataset Use ## Use Cases The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation. ## Usage Caveats Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements. Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper. ## Getting Started This dataset is organized such that it can be naively loaded via Hugging Face datasets library. We recommend using streaming due to the large size of the files. Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face. # Citation ```bibtex @misc{OpenOrca, title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces}, author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and ""Teknium""}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca}, } ``` ```bibtex @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` ```bibtex @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint= arXiv 2307.09288 } @software{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```" cyanic-selkie/wikianc,"{""license"": 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Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) ## Dataset Description - **Repository:** [WikiAnc repository](https://github.com/cyanic-selkie/wikianc) ### Dataset Summary The WikiAnc dataset is an automatically generated dataset from Wikipedia (all languages) and Wikidata dumps (August, 2023). The code for generating the dataset can be found [here](https://github.com/cyanic-selkie/wikianc). ### Supported Tasks - `wikificiation`: The dataset can be used to train a model for Wikification. - `named-entity-linking`: The dataset can be used to train a model for Named Entity Linking. ### Languages The text in the dataset is in all 320 Wikipedia languages. The full list can be found in the table below. ## Dataset Structure ### Data Instances A typical data point represents a paragraph in a Wikipedia article. The `paragraph_text` field contains the original text in an NFC normalized, UTF-8 encoded string. The `paragraph_anchors` field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point `start` field, exclusive ending UTF-8 code point `end` field, a nullable `qid` field, a nullable `pageid` field, and an NFC normalized, UTF-8 encoded `title` (Wikipedia) field. Additionally, each paragraph has `article_title`, `article_pageid`, and (nullable) `article_qid` fields referring to the article the paragraph came from. There is also a nullable, NFC normalized, UTF-8 encoded `section_heading` field, and an integer `section_level` field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from. The `qid` fields refers to Wikidata's QID identifiers, while the `pageid` and `title` fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles). **NOTE:** An anchor will always have a `title`, but that doesn't mean it has to have a `pageid`. This is because Wikipedia allows defining anchors to nonexistent articles. An example from the WikiAnc EN test set looks as follows: ``` { ""uuid"": ""5f74e678-944f-4761-a5e0-b6426f6f61b8"", ""article_title"": ""Climatius"", ""article_pageid"": 5394373, ""article_qid"": 867987, ""section_heading"": null, ""section_level"": 0, ""paragraph_text"": ""It was a small fish, at 7.5 cm, and to discourage predators, Climatius sported fifteen sharp spines. There was one spine each on the paired pelvic and pectoral fins, and on the aingle anal and two dorsal fins, and a four pairs without fins on the fish's underside."", ""paragraph_anchors"": [ { ""start"": 140, ""end"": 146, ""qid"": 3335089, ""pageid"": 56849833, ""title"": ""Pelvic_fin"" }, { ""start"": 151, ""end"": 159, ""qid"": 4162555, ""pageid"": 331956, ""title"": ""Pectoral_fin"" }, { ""start"": 184, ""end"": 188, ""qid"": 4162555, ""pageid"": 331958, ""title"": ""Anal_fin"" }, { ""start"": 197, ""end"": 208, ""qid"": 1568355, ""pageid"": 294244, ""title"": ""Dorsal_fin"" } ] } ``` ### Data Fields - `uuid`: a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example - `article_title`: an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores - `article_pageid`: an integer representing the Wikipedia pageID of the article - `article_qid`: an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset - `section_heading`: a nullable, NFC normalized, UTF-8 encoded string representing the section heading - `section_level`: an integer representing the level of the section in the section hierarchy - `paragraph_text`: an NFC normalized, UTF-8 encoded string representing the paragraph - `paragraph_anchors`: a list of structs representing anchors, each anchor has: - `start`: an integer representing the inclusive starting UTF-8 code point of the anchors - `end`: an integer representing the exclusive ending UTF-8 code point of the anchor - `qid`: a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset - `pageid`: a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset - `title`: an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article ### Data Splits The data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows: #### Train | | Articles | Paragraphs | Anchors | Anchors with QIDs | Anchors with PageIDs | | :-- | --: | --: | --: | --: | --: | | ab | 2378 | 5678 | 10515 | 3649 | 3650 | | ace | 12591 | 23969 | 48638 | 25150 | 25175 | | ady | 596 | 1662 | 2694 | 1593 | 1606 | | af | 104470 | 399038 | 985640 | 900596 | 900967 | | als | 27999 | 165085 | 402049 | 294742 | 294744 | | alt | 1043 | 7468 | 9158 | 5446 | 5452 | | am | 13576 | 46318 | 90051 | 51915 | 52173 | | ami | 1582 | 12428 | 6080 | 1505 | 2579 | | an | 40179 | 121367 | 669830 | 516248 | 516822 | | ang | 3833 | 9664 | 24297 | 10189 | 10229 | | anp | 2506 | 6865 | 14560 | 3825 | 5061 | | ar | 1132271 | 3617491 | 11657228 | 11240112 | 11244160 | | arc | 1844 | 3766 | 9232 | 5460 | 5545 | | ary | 6736 | 17049 | 50185 | 34193 | 34227 | | arz | 1579782 | 3693549 | 7879303 | 6906799 | 6917393 | | as | 11947 | 77835 | 122760 | 67594 | 67720 | | ast | 126992 | 877278 | 2952000 | 1775764 | 1777383 | | atj | 1872 | 3820 | 6544 | 3247 | 3365 | | av | 3048 | 8542 | 16115 | 8895 | 9000 | | avk | 27577 | 85219 | 106100 | 32260 | 33491 | | awa | 3396 | 5802 | 6617 | 1679 | 2370 | | ay | 5102 | 15125 | 22802 | 13930 | 13933 | | az | 180810 | 789902 | 1570889 | 1377797 | 1380325 | | azb | 240990 | 585386 | 1241661 | 749575 | 753318 | | ba | 62269 | 391926 | 625645 | 562730 | 563181 | | ban | 18955 | 44138 | 86239 | 66213 | 66412 | | bar | 26057 | 83298 | 185158 | 109082 | 109091 | | bat_smg | 17013 | 41951 | 77417 | 51701 | 51733 | | bcl | 13783 | 45457 | 78963 | 47819 | 47861 | | be | 222883 | 821135 | 2499258 | 2204062 | 2204117 | | bg | 285156 | 1336530 | 3967713 | 3618800 | 3627798 | | bh | 7658 | 17052 | 29110 | 22157 | 22217 | | bi | 1403 | 1712 | 3172 | 1991 | 1995 | | bjn | 9672 | 19007 | 58660 | 32538 | 33071 | | blk | 2786 | 11825 | 11341 | 5979 | 6129 | | bm | 1111 | 2421 | 2451 | 1217 | 1218 | | bn | 136921 | 736388 | 1530942 | 1161967 | 1162761 | | bo | 11843 | 37121 | 8241 | 6265 | 6359 | | bpy | 24742 | 115606 | 166906 | 86166 | 86170 | | br | 78524 | 214128 | 657375 | 527295 | 527606 | | bs | 86407 | 382114 | 1246030 | 965782 | 966511 | | bug | 14231 | 14484 | 53879 | 14787 | 15146 | | bxr | 2730 | 9571 | 27853 | 11560 | 11567 | | ca | 691444 | 3596667 | 11359870 | 10236358 | 10237666 | | cbk_zam | 2989 | 8322 | 9939 | 2790 | 2847 | | cdo | 15922 | 30059 | 63474 | 29659 | 29705 | | ce | 597137 | 2121587 | 3097393 | 1507129 | 1507806 | | ceb | 5888811 | 11920613 | 37969424 | 33678489 | 33962205 | | ch | 574 | 1166 | 2290 | 492 | 601 | | chr | 980 | 1110 | 1311 | 779 | 790 | | chy | 711 | 753 | 494 | 428 | 428 | | ckb | 48903 | 163599 | 435662 | 224749 | 226749 | | co | 6719 | 22954 | 46391 | 24149 | 24229 | | cr | 158 | 216 | 209 | 94 | 94 | | crh | 24117 | 29781 | 98534 | 70231 | 70235 | | cs | 516037 | 2679537 | 9917806 | 8763103 | 8763291 | | csb | 5315 | 14009 | 31294 | 16820 | 16820 | | cu | 1171 | 2796 | 5283 | 2346 | 2349 | | cv | 50525 | 157542 | 375399 | 166889 | 167497 | | cy | 276031 | 992900 | 2011030 | 1613064 | 1620632 | | da | 284765 | 1167917 | 4352733 | 3854239 | 3854549 | | dag | 9248 | 29213 | 46084 | 10981 | 14213 | | de | 2780056 | 16093948 | 52497421 | 50480495 | 50480548 | | din | 485 | 1551 | 1096 | 197 | 197 | | diq | 37565 | 70969 | 155656 | 141636 | 141695 | | dsb | 3083 | 8760 | 19397 | 9652 | 9652 | | dty | 3339 | 6219 | 7505 | 4417 | 4447 | | dv | 4190 | 16809 | 7906 | 3612 | 3620 | | dz | 652 | 2623 | 272 | 94 | 100 | | ee | 1075 | 2326 | 1823 | 861 | 926 | | el | 224207 | 1527561 | 4181433 | 3119952 | 3121967 | | eml | 12169 | 53861 | 115729 | 65775 | 65940 | | en | 6514924 | 40656507 | 109681826 | 107761324 | 107768438 | | eo | 330486 | 1116191 | 4257655 | 3975927 | 3979379 | | es | 1792062 | 10890435 | 33729712 | 31581851 | 31648945 | | et | 233078 | 1110906 | 3558448 | 2879595 | 2886824 | | eu | 386029 | 1405747 | 3398477 | 3025183 | 3030635 | | ext | 3472 | 9626 | 20554 | 11966 | 11978 | | fa | 901254 | 2357271 | 6189352 | 5862106 | 5870803 | | fat | 1044 | 6092 | 1717 | 120 | 857 | | ff | 1763 | 4103 | 3483 | 2304 | 2413 | | fi | 373226 | 1667296 | 5221239 | 4658292 | 4663471 | | fiu_vro | 6417 | 19897 | 40418 | 23563 | 23609 | | fj | 1157 | 1782 | 4852 | 1910 | 1911 | | fo | 11809 | 30828 | 119267 | 95117 | 95259 | | fr | 2432972 | 15252697 | 43564517 | 42573624 | 42589064 | | frp | 5341 | 10574 | 36358 | 24905 | 24926 | | frr | 16038 | 30821 | 80265 | 68184 | 68315 | | fur | 3665 | 10651 | 29516 | 16249 | 16278 | | fy | 46011 | 206153 | 1271339 | 985227 | 985511 | | ga | 52168 | 130535 | 347037 | 288261 | 288309 | | gag | 2408 | 4844 | 8551 | 4520 | 4520 | | gan | 4219 | 9689 | 18994 | 14119 | 14128 | | gcr | 2227 | 5163 | 2763 | 1186 | 1186 | | gd | 15850 | 48217 | 141290 | 95557 | 95562 | | gl | 190419 | 910543 | 3674404 | 2937660 | 2938634 | | glk | 6484 | 15344 | 32631 | 21395 | 21447 | | gn | 5064 | 15481 | 40641 | 30389 | 30440 | | gom | 4192 | 37508 | 14192 | 2369 | 2382 | | gor | 14388 | 28133 | 107341 | 66191 | 67016 | | got | 960 | 2186 | 4093 | 1404 | 1415 | | gpe | 899 | 3383 | 1199 | 796 | 815 | | gu | 30025 | 114805 | 459063 | 348651 | 348731 | | guc | 546 | 2545 | 2300 | 1025 | 1138 | | gur | 1010 | 5043 | 1761 | 227 | 244 | | guw | 1263 | 3719 | 7474 | 3116 | 5375 | | gv | 5036 | 12213 | 48801 | 19659 | 19663 | | ha | 31977 | 149096 | 115029 | 97167 | 98184 | | hak | 8694 | 11505 | 39744 | 28150 | 28152 | | haw | 2470 | 5810 | 11169 | 5700 | 5705 | | he | 323472 | 2648617 | 10904148 | 10367532 | 10379886 | | hi | 150121 | 538451 | 964251 | 795726 | 798254 | | hif | 10534 | 21169 | 43463 | 23970 | 24316 | | hr | 189415 | 876107 | 3210326 | 2752205 | 2758602 | | hsb | 13183 | 40760 | 91863 | 66632 | 66633 | | ht | 64850 | 154160 | 201547 | 166206 | 167961 | | hu | 346711 | 1859683 | 5267990 | 4707580 | 4710525 | | hy | 298066 | 1542920 | 3767938 | 2689014 | 2690466 | | hyw | 11358 | 83640 | 161227 | 82218 | 84817 | | ia | 24581 | 43289 | 129914 | 96517 | 96595 | | id | 620895 | 2138237 | 6589957 | 5629372 | 5644832 | | ie | 11020 | 22342 | 60890 | 46054 | 46122 | | ig | 19448 | 110907 | 57963 | 31022 | 31298 | | ik | 737 | 1016 | 848 | 551 | 580 | | ilo | 14135 | 74304 | 126533 | 75701 | 75705 | | inh | 1754 | 4640 | 13284 | 5770 | 6011 | | io | 36312 | 101555 | 303765 | 258933 | 259001 | | is | 54348 | 170321 | 574897 | 436767 | 437784 | | it | 1610989 | 8718610 | 27447754 | 26116131 | 26126157 | | iu | 502 | 757 | 536 | 414 | 418 | | ja | 1355269 | 9276459 | 29002111 | 27752954 | 27801000 | | jam | 1571 | 2260 | 5887 | 3588 | 3590 | | jbo | 1287 | 3088 | 5831 | 546 | 546 | | jv | 66323 | 148710 | 547010 | 381682 | 382052 | | ka | 167161 | 695865 | 2275552 | 422090 | 422095 | | kaa | 3540 | 9814 | 12930 | 5312 | 5752 | | kab | 5346 | 14709 | 36889 | 22000 | 22050 | | kbd | 1549 | 6348 | 14594 | 5277 | 5280 | | kbp | 1846 | 6005 | 7119 | 6875 | 6880 | | kcg | 871 | 1839 | 2953 | 1857 | 1871 | | kg | 1187 | 1933 | 3835 | 2292 | 2295 | | ki | 1482 | 2899 | 2035 | 1386 | 1649 | | kk | 235740 | 889990 | 1840304 | 1143049 | 1151399 | | kl | 282 | 1024 | 1337 | 302 | 302 | | km | 11422 | 84697 | 111378 | 40954 | 41529 | | kn | 30729 | 261724 | 432994 | 188536 | 188807 | | ko | 606386 | 2159706 | 6217786 | 5715559 | 5725614 | | koi | 3260 | 9065 | 17068 | 10628 | 10628 | | krc | 1465 | 6234 | 18092 | 7294 | 7311 | | ks | 4176 | 9446 | 15252 | 5917 | 6226 | | ksh | 2836 | 11043 | 26577 | 9484 | 9496 | | ku | 55166 | 112840 | 269080 | 208679 | 210304 | | kv | 5236 | 13396 | 32141 | 26727 | 26744 | | kw | 6884 | 18901 | 49462 | 28074 | 28194 | | ky | 75426 | 191772 | 271376 | 189656 | 190133 | | la | 124150 | 240343 | 1456464 | 1283285 | 1283728 | | lad | 3538 | 11910 | 37456 | 19124 | 19124 | | lb | 57747 | 178507 | 573528 | 443583 | 444601 | | lbe | 1205 | 2249 | 4470 | 2543 | 2543 | | lez | 4067 | 16675 | 36970 | 25834 | 25842 | | lfn | 4506 | 21746 | 29785 | 14554 | 14560 | | lg | 3814 | 23386 | 15539 | 2088 | 2724 | | li | 14134 | 58711 | 212772 | 137110 | 137367 | | lij | 8092 | 23366 | 61410 | 34939 | 34940 | | lld | 152613 | 158049 | 578033 | 443976 | 458150 | | lmo | 67387 | 136650 | 373890 | 274174 | 274612 | | ln | 3132 | 6066 | 11086 | 7838 | 7874 | | lo | 4734 | 15005 | 27132 | 8562 | 8799 | | lt | 204135 | 775863 | 2687983 | 2406710 | 2414909 | | ltg | 1018 | 2979 | 5815 | 2190 | 2193 | | lv | 118530 | 437086 | 1458341 | 1244609 | 1247181 | | mad | 1113 | 3500 | 3762 | 1149 | 1157 | | mai | 13285 | 22572 | 53246 | 38119 | 38128 | | map_bms | 10875 | 16411 | 67964 | 51125 | 51137 | | mdf | 4002 | 11043 | 21658 | 9178 | 9183 | | mg | 92227 | 213580 | 328751 | 265931 | 267633 | | mhr | 11010 | 33013 | 60771 | 38153 | 38220 | | mi | 7274 | 10154 | 29052 | 24854 | 25216 | | min | 223075 | 422381 | 1315030 | 513108 | 515548 | | mk | 131522 | 695456 | 1984109 | 1639280 | 1640744 | | ml | 84334 | 415940 | 797903 | 485482 | 486324 | | mn | 23434 | 124485 | 295548 | 142014 | 142984 | | mni | 10354 | 18872 | 29474 | 18810 | 19876 | | mnw | 3136 | 34165 | 9342 | 1908 | 2387 | | mr | 92464 | 326662 | 633452 | 383501 | 392709 | | mrj | 10156 | 20132 | 48416 | 24098 | 24098 | | ms | 344459 | 988647 | 2424535 | 1932685 | 1937647 | | mt | 5381 | 49856 | 104636 | 51251 | 51278 | | mwl | 4402 | 37271 | 127176 | 25729 | 26366 | | my | 103938 | 334243 | 445026 | 300567 | 303288 | | myv | 7515 | 21592 | 36762 | 26570 | 26591 | | mzn | 17364 | 39937 | 89805 | 46962 | 47020 | | nah | 5934 | 12478 | 30805 | 13093 | 14364 | | nap | 11235 | 22336 | 41891 | 20798 | 20804 | | nds | 79228 | 242004 | 583941 | 305374 | 305422 | | nds_nl | 6484 | 28252 | 94875 | 51767 | 51785 | | ne | 30359 | 91033 | 153937 | 124841 | 125078 | | new | 71653 | 245033 | 454251 | 289444 | 289912 | | nia | 1496 | 4047 | 4524 | 2258 | 2812 | | nl | 1948842 | 5867108 | 17953497 | 16886996 | 16893078 | | nn | 160106 | 549454 | 1751481 | 1375622 | 1376155 | | no | 591000 | 2213493 | 7050421 | 6471776 | 6476157 | | nov | 1341 | 3711 | 7466 | 3948 | 3955 | | nqo | 1489 | 9858 | 23633 | 6056 | 6981 | | nrm | 4571 | 14279 | 38935 | 33295 | 33321 | | nso | 7618 | 9505 | 36826 | 35621 | 35623 | | nv | 21911 | 57663 | 123762 | 107139 | 107139 | | ny | 1060 | 3164 | 4750 | 1455 | 1490 | | oc | 85099 | 303185 | 1035051 | 791403 | 792043 | | olo | 4348 | 14334 | 18704 | 8634 | 8647 | | om | 1710 | 7496 | 8222 | 4333 | 4416 | | or | 17027 | 76677 | 137274 | 57023 | 57064 | | os | 17468 | 40488 | 80943 | 48124 | 48414 | | pa | 50421 | 226354 | 344239 | 197594 | 198080 | | pag | 2533 | 41416 | 4150 | 2907 | 2907 | | pam | 7816 | 16493 | 53785 | 29375 | 29715 | | pap | 3153 | 12086 | 22157 | 18161 | 18233 | | pcd | 5272 | 12203 | 15602 | 12319 | 12360 | | pcm | 1019 | 4631 | 4161 | 1160 | 1261 | | pdc | 2009 | 5406 | 8151 | 4122 | 4144 | | pfl | 2717 | 14024 | 26150 | 10291 | 10294 | | pi | 2972 | 5959 | 7773 | 201 | 201 | | pih | 829 | 1065 | 2857 | 2016 | 2018 | | pl | 1468194 | 5599437 | 19364191 | 18389560 | 18405120 | | pms | 66552 | 170133 | 369956 | 308593 | 314917 | | pnb | 67534 | 402101 | 937247 | 525105 | 533265 | | pnt | 497 | 1467 | 3553 | 1715 | 1716 | | ps | 19254 | 134868 | 72493 | 36348 | 36899 | | pt | 1048823 | 5226543 | 16811382 | 15714686 | 15714890 | | pwn | 328 | 1825 | 990 | 428 | 430 | | qu | 22365 | 47078 | 133032 | 106686 | 106708 | | rm | 3569 | 27345 | 47169 | 20460 | 20490 | | rmy | 911 | 2221 | 4235 | 1854 | 1965 | | rn | 726 | 1641 | 1436 | 594 | 601 | | ro | 417630 | 1518438 | 4282072 | 3764830 | 3765626 | | roa_rup | 1270 | 2751 | 4641 | 2527 | 2537 | | roa_tara | 8407 | 18031 | 42040 | 14330 | 14331 | | ru | 1889271 | 12344758 | 30796034 | 29268121 | 29288089 | | rue | 7369 | 21429 | 61022 | 43241 | 43256 | | rw | 7793 | 35619 | 38066 | 19821 | 20967 | | sa | 12069 | 78188 | 104193 | 40307 | 41518 | | sah | 16007 | 76450 | 82154 | 61041 | 61412 | | sat | 8655 | 43624 | 57493 | 28497 | 28820 | | sc | 6919 | 24434 | 66719 | 44707 | 44733 | | scn | 21990 | 49686 | 132583 | 102735 | 102774 | | sco | 34097 | 86464 | 301450 | 148184 | 148406 | | sd | 16228 | 48679 | 79392 | 34572 | 35729 | | se | 6101 | 10531 | 25844 | 17978 | 18010 | | sg | 473 | 537 | 318 | 184 | 184 | | sh | 445218 | 1213741 | 4337559 | 3858400 | 3860253 | | shi | 1650 | 6036 | 10364 | 4715 | 4926 | | shn | 10653 | 51542 | 46976 | 29925 | 29993 | | si | 21959 | 132932 | 146935 | 55158 | 56422 | | simple | 224811 | 618711 | 2014692 | 1689101 | 1689185 | | sk | 230073 | 845501 | 2867955 | 2468707 | 2469129 | | skr | 5505 | 62742 | 38412 | 15004 | 21015 | | sl | 175804 | 810714 | 2597824 | 2067682 | 2068522 | | sm | 995 | 1591 | 3838 | 2515 | 2523 | | smn | 5004 | 12483 | 37008 | 22440 | 22492 | | sn | 10159 | 19527 | 40437 | 31573 | 32763 | | so | 8540 | 36173 | 53012 | 42913 | 43548 | | sq | 94941 | 371562 | 699210 | 520709 | 522241 | | sr | 657766 | 2331205 | 6562651 | 5257496 | 5264077 | | srn | 1171 | 3050 | 6637 | 1752 | 1941 | | ss | 783 | 2124 | 2382 | 1127 | 1139 | | st | 982 | 1971 | 2510 | 1689 | 1701 | | stq | 3648 | 10972 | 29713 | 15919 | 15920 | | su | 57552 | 122590 | 496201 | 384518 | 384891 | | sv | 2418380 | 5019466 | 22263222 | 21445193 | 21445441 | | sw | 75109 | 218219 | 798980 | 688743 | 692052 | | szl | 56229 | 109496 | 473528 | 129434 | 129479 | | szy | 4628 | 49166 | 18867 | 2419 | 3187 | | ta | 157642 | 780711 | 1642095 | 1141032 | 1142372 | | tay | 2643 | 15831 | 10104 | 1496 | 5312 | | tcy | 2135 | 9932 | 11073 | 4680 | 4745 | | te | 83866 | 719826 | 822054 | 619184 | 622092 | | tet | 1323 | 3797 | 8047 | 4093 | 4095 | | tg | 108598 | 279635 | 761826 | 330974 | 331423 | | th | 153075 | 715083 | 1723394 | 1395935 | 1398891 | | ti | 388 | 987 | 1191 | 325 | 326 | | tk | 4739 | 23629 | 18964 | 9717 | 9760 | | tl | 43388 | 150141 | 447293 | 296084 | 296634 | | tn | 1090 | 3960 | 3976 | 2008 | 2010 | | to | 1512 | 2754 | 3542 | 2029 | 2080 | | tpi | 1278 | 2055 | 3897 | 2193 | 2198 | | tr | 500435 | 1806253 | 4476004 | 3964449 | 3965589 | | trv | 1770 | 16650 | 3814 | 504 | 969 | | ts | 674 | 1798 | 1557 | 903 | 909 | | tt | 484761 | 1196573 | 2064576 | 1675637 | 1676579 | | tum | 16778 | 31383 | 57382 | 28399 | 37107 | | tw | 3568 | 16807 | 15312 | 10912 | 11495 | | ty | 1175 | 1364 | 1563 | 1095 | 1095 | | tyv | 3399 | 21968 | 21004 | 5535 | 5557 | | udm | 5066 | 11432 | 24875 | 17709 | 17715 | | ug | 8102 | 58982 | 23654 | 12671 | 12874 | | uk | 522709 | 2867475 | 6800045 | 6445628 | 6451294 | | ur | 194948 | 676227 | 1870488 | 910419 | 914840 | | uz | 232879 | 859793 | 1344790 | 1073065 | 1084092 | | ve | 764 | 1359 | 2524 | 2366 | 2366 | | vec | 62729 | 98987 | 275972 | 194424 | 194447 | | vep | 6853 | 43014 | 93864 | 39225 | 39228 | | vi | 1300753 | 4103594 | 10852870 | 6884928 | 6892519 | | vls | 7272 | 26374 | 61885 | 49639 | 49653 | | vo | 32133 | 78015 | 125495 | 101612 | 101629 | | wa | 11104 | 56305 | 116752 | 79686 | 80037 | | war | 1158901 | 1342594 | 6654010 | 6009636 | 6009641 | | wo | 1659 | 7693 | 10828 | 4057 | 4103 | | wuu | 37170 | 58227 | 121928 | 82184 | 82237 | | xal | 2008 | 4309 | 4582 | 2112 | 2113 | | xh | 1502 | 4448 | 6733 | 2128 | 2186 | | xmf | 19201 | 49944 | 179291 | 21189 | 22041 | | yi | 14164 | 68937 | 172645 | 116102 | 116325 | | yo | 29938 | 52231 | 85171 | 46928 | 47346 | | za | 2388 | 3917 | 7463 | 4613 | 4665 | | zea | 5445 | 16648 | 36161 | 23532 | 23578 | | zh | 1310818 | 5501834 | 16397675 | 14380752 | 14421795 | | zh_classical | 11775 | 44053 | 140340 | 71576 | 71692 | | zh_min_nan | 425676 | 853753 | 2627115 | 2053956 | 2054838 | | zh_yue | 121401 | 273459 | 844047 | 683130 | 683226 | | zu | 10387 | 18211 | 22569 | 20193 | 20238 | #### Validation | | Articles | Paragraphs | Anchors | Anchors with QIDs | Anchors with PageIDs | | :-- | --: | --: | --: | --: | --: | | ab | 475 | 601 | 1061 | 399 | 399 | | ace | 2443 | 2668 | 5197 | 2583 | 2587 | | ady | 142 | 183 | 248 | 150 | 151 | | af | 27383 | 44157 | 109108 | 100078 | 100123 | | als | 11998 | 18277 | 44634 | 32874 | 32874 | | alt | 481 | 827 | 1020 | 621 | 621 | | am | 3746 | 5234 | 10111 | 5731 | 5756 | | ami | 749 | 1431 | 744 | 179 | 304 | | an | 10526 | 13588 | 74808 | 58195 | 58259 | | ang | 826 | 1099 | 2647 | 1099 | 1102 | | anp | 504 | 751 | 1698 | 437 | 581 | | ar | 265368 | 401215 | 1295968 | 1249666 | 1250103 | | arc | 377 | 418 | 1061 | 610 | 617 | | ary | 1447 | 1870 | 5702 | 3885 | 3887 | | arz | 367206 | 410487 | 876531 | 767742 | 768942 | | as | 5463 | 8589 | 13953 | 7719 | 7732 | | ast | 48345 | 97904 | 329690 | 197832 | 198042 | | atj | 399 | 440 | 774 | 406 | 416 | | av | 719 | 961 | 1918 | 1043 | 1053 | | avk | 8056 | 9538 | 11816 | 3633 | 3772 | | awa | 515 | 645 | 721 | 213 | 287 | | ay | 1391 | 1653 | 2616 | 1481 | 1483 | | az | 57070 | 88136 | 177151 | 155596 | 155858 | | azb | 57642 | 64997 | 137053 | 83336 | 83778 | | ba | 25690 | 43460 | 69052 | 61624 | 61666 | | ban | 4053 | 4840 | 9581 | 7374 | 7385 | | bar | 6905 | 9377 | 20546 | 12164 | 12164 | | bat_smg | 4149 | 4706 | 8787 | 5820 | 5823 | | bcl | 3355 | 5058 | 8759 | 5080 | 5083 | | be | 64203 | 91174 | 276525 | 244114 | 244122 | | bg | 98148 | 148234 | 438687 | 400356 | 401330 | | bh | 1535 | 1891 | 3464 | 2630 | 2635 | | bi | 154 | 159 | 251 | 151 | 151 | | bjn | 1764 | 2166 | 6458 | 3694 | 3775 | | blk | 887 | 1374 | 1538 | 821 | 839 | | bm | 196 | 272 | 317 | 146 | 146 | | bn | 50495 | 81841 | 169097 | 128508 | 128609 | | bo | 2198 | 4079 | 934 | 746 | 752 | | bpy | 10057 | 12879 | 18710 | 9693 | 9693 | | br | 18687 | 23734 | 73278 | 59024 | 59056 | | bs | 28533 | 42574 | 138483 | 107760 | 107846 | | bug | 1636 | 1655 | 6141 | 1682 | 1731 | | bxr | 754 | 1003 | 2930 | 1211 | 1211 | | ca | 251952 | 399403 | 1265187 | 1140208 | 1140359 | | cbk_zam | 460 | 932 | 1040 | 268 | 272 | | cdo | 2953 | 3237 | 6938 | 3273 | 3281 | | ce | 197899 | 234617 | 341843 | 166126 | 166206 | | ceb | 1221405 | 1324624 | 4218179 | 3742385 | 3773844 | | ch | 123 | 131 | 239 | 64 | 73 | | chr | 124 | 134 | 175 | 100 | 100 | | chy | 67 | 67 | 47 | 42 | 42 | | ckb | 13511 | 18279 | 48490 | 25365 | 25540 | | co | 1723 | 2587 | 5286 | 2729 | 2737 | | cr | 22 | 23 | 22 | 13 | 13 | | crh | 2978 | 3246 | 11005 | 7899 | 7899 | | cs | 189136 | 297000 | 1101343 | 974485 | 974505 | | csb | 1307 | 1533 | 3341 | 1851 | 1851 | | cu | 250 | 275 | 540 | 229 | 229 | | cv | 14374 | 17462 | 42486 | 19049 | 19114 | | cy | 89897 | 110225 | 222476 | 177842 | 178698 | | da | 87765 | 129990 | 482701 | 427333 | 427374 | | dag | 2215 | 3237 | 4935 | 1169 | 1498 | | de | 1120553 | 1788057 | 5831103 | 5607963 | 5607963 | | din | 149 | 177 | 128 | 15 | 15 | | diq | 6660 | 7883 | 17684 | 15853 | 15861 | | dsb | 781 | 1032 | 2476 | 1301 | 1301 | | dty | 554 | 659 | 861 | 480 | 483 | | dv | 1227 | 1898 | 870 | 406 | 406 | | dz | 215 | 303 | 21 | 8 | 8 | | ee | 203 | 242 | 183 | 66 | 74 | | el | 99725 | 169395 | 461747 | 344216 | 344456 | | eml | 4387 | 6114 | 13938 | 8193 | 8214 | | en | 2503257 | 4516442 | 12185882 | 11974436 | 11975194 | | eo | 90949 | 123848 | 474727 | 442357 | 442772 | | es | 701171 | 1209944 | 3752765 | 3514968 | 3522213 | | et | 80911 | 123354 | 395877 | 319773 | 320587 | | eu | 104388 | 156552 | 378553 | 337331 | 337944 | | ext | 804 | 1045 | 2269 | 1344 | 1345 | | fa | 191532 | 262121 | 688824 | 652200 | 653219 | | fat | 446 | 709 | 214 | 3 | 97 | | ff | 361 | 459 | 378 | 222 | 234 | | fi | 123327 | 184244 | 576163 | 514419 | 514915 | | fiu_vro | 1738 | 2263 | 4622 | 2623 | 2628 | | fj | 168 | 213 | 604 | 214 | 214 | | fo | 2625 | 3398 | 13383 | 10599 | 10617 | | fr | 954388 | 1695419 | 4847588 | 4738268 | 4740047 | | frp | 1018 | 1181 | 4089 | 2862 | 2862 | | frr | 2968 | 3419 | 9609 | 7996 | 8011 | | fur | 884 | 1168 | 3225 | 1833 | 1839 | | fy | 15980 | 22974 | 139530 | 108300 | 108337 | | ga | 10781 | 14493 | 38848 | 32343 | 32352 | | gag | 440 | 551 | 961 | 465 | 465 | | gan | 731 | 1045 | 2071 | 1536 | 1537 | | gcr | 480 | 567 | 297 | 122 | 122 | | gd | 4393 | 5296 | 15544 | 10458 | 10458 | | gl | 62030 | 101112 | 407821 | 325854 | 325960 | | glk | 1383 | 1747 | 3723 | 2435 | 2443 | | gn | 1164 | 1728 | 4751 | 3521 | 3528 | | gom | 2106 | 4116 | 1511 | 251 | 251 | | gor | 2844 | 3082 | 11826 | 7315 | 7411 | | got | 216 | 245 | 514 | 190 | 190 | | gpe | 265 | 355 | 93 | 71 | 73 | | gu | 8437 | 13008 | 50956 | 38242 | 38251 | | guc | 198 | 279 | 312 | 141 | 162 | | gur | 369 | 565 | 145 | 25 | 27 | | guw | 332 | 393 | 827 | 313 | 616 | | gv | 957 | 1324 | 5652 | 2252 | 2253 | | ha | 10666 | 16571 | 12853 | 10862 | 10993 | | hak | 1179 | 1302 | 4628 | 3155 | 3155 | | haw | 541 | 650 | 1238 | 616 | 618 | | he | 165541 | 295188 | 1213939 | 1153986 | 1155384 | | hi | 36229 | 60184 | 108382 | 89102 | 89340 | | hif | 2107 | 2369 | 5015 | 2648 | 2680 | | hr | 62673 | 97103 | 354392 | 304964 | 305664 | | hsb | 3599 | 4379 | 10001 | 7239 | 7240 | | ht | 14693 | 17294 | 23011 | 18721 | 18928 | | hu | 125438 | 206546 | 586091 | 523501 | 523814 | | hy | 113060 | 171415 | 418503 | 298111 | 298292 | | hyw | 5310 | 9207 | 17616 | 8842 | 9168 | | ia | 4021 | 4850 | 14972 | 11257 | 11263 | | id | 158648 | 237793 | 734148 | 627764 | 629525 | | ie | 2213 | 2523 | 6750 | 5036 | 5046 | | ig | 7944 | 12354 | 6464 | 3466 | 3493 | | ik | 100 | 118 | 120 | 64 | 71 | | ilo | 4096 | 8297 | 14183 | 8609 | 8609 | | inh | 399 | 494 | 1298 | 626 | 645 | | io | 8868 | 11368 | 33682 | 28744 | 28748 | | is | 13573 | 18566 | 62576 | 47263 | 47360 | | it | 584902 | 968880 | 3050620 | 2902006 | 2903047 | | iu | 61 | 62 | 48 | 29 | 29 | | ja | 573457 | 1032568 | 3222875 | 3083301 | 3088604 | | jam | 249 | 274 | 623 | 399 | 399 | | jbo | 270 | 321 | 562 | 56 | 56 | | jv | 13108 | 16457 | 60143 | 42112 | 42148 | | ka | 53071 | 76961 | 252383 | 46974 | 46975 | | kaa | 775 | 1071 | 1476 | 669 | 717 | | kab | 1269 | 1685 | 4050 | 2397 | 2403 | | kbd | 474 | 663 | 1482 | 537 | 537 | | kbp | 535 | 656 | 835 | 810 | 811 | | kcg | 190 | 223 | 311 | 196 | 197 | | kg | 187 | 213 | 420 | 260 | 260 | | ki | 273 | 333 | 248 | 169 | 206 | | kk | 76635 | 99268 | 204324 | 126732 | 127677 | | kl | 97 | 129 | 162 | 43 | 43 | | km | 3844 | 9340 | 12192 | 4524 | 4583 | | kn | 14217 | 29387 | 48402 | 20992 | 21022 | | ko | 154713 | 239887 | 689906 | 633527 | 634725 | | koi | 682 | 1010 | 1815 | 1144 | 1144 | | krc | 423 | 698 | 2022 | 841 | 846 | | ks | 888 | 1006 | 1692 | 645 | 670 | | ksh | 918 | 1156 | 2951 | 1053 | 1055 | | ku | 10060 | 12771 | 29766 | 23050 | 23232 | | kv | 1105 | 1456 | 3365 | 2787 | 2787 | | kw | 1820 | 2171 | 5570 | 3076 | 3082 | | ky | 16655 | 21571 | 31213 | 21712 | 21757 | | la | 22397 | 26732 | 161732 | 142447 | 142486 | | lad | 961 | 1286 | 3984 | 2056 | 2056 | | lb | 15385 | 19667 | 60568 | 46664 | 46730 | | lbe | 207 | 232 | 488 | 290 | 290 | | lez | 1184 | 1764 | 3829 | 2760 | 2760 | | lfn | 1455 | 2435 | 3328 | 1602 | 1604 | | lg | 1272 | 2650 | 1795 | 239 | 305 | | li | 4501 | 6650 | 24213 | 15790 | 15826 | | lij | 1781 | 2607 | 6658 | 3933 | 3933 | | lld | 17293 | 17539 | 64059 | 49327 | 50864 | | lmo | 12641 | 14976 | 40217 | 29874 | 29946 | | ln | 585 | 692 | 1321 | 996 | 997 | | lo | 1144 | 1680 | 3023 | 991 | 1013 | | lt | 62652 | 85962 | 300456 | 269264 | 270227 | | ltg | 289 | 341 | 686 | 285 | 285 | | lv | 34742 | 48371 | 160433 | 136594 | 136873 | | mad | 284 | 381 | 439 | 135 | 136 | | mai | 2184 | 2499 | 5878 | 4209 | 4212 | | map_bms | 1539 | 1847 | 7486 | 5705 | 5705 | | mdf | 1086 | 1244 | 2512 | 1077 | 1077 | | mg | 20361 | 23650 | 36313 | 29821 | 29974 | | mhr | 2863 | 3594 | 6538 | 4114 | 4122 | | mi | 1078 | 1154 | 3214 | 2743 | 2776 | | min | 42987 | 46277 | 143692 | 55809 | 56077 | | mk | 46235 | 76890 | 219310 | 180884 | 181042 | | ml | 31116 | 46345 | 88976 | 53726 | 53818 | | mn | 8485 | 13887 | 32271 | 15330 | 15455 | | mni | 1843 | 2102 | 3418 | 2183 | 2325 | | mnw | 1284 | 3750 | 897 | 202 | 224 | | mr | 26803 | 36202 | 70510 | 43103 | 44352 | | mrj | 2062 | 2297 | 5627 | 2888 | 2888 | | ms | 75473 | 110077 | 270064 | 215280 | 215811 | | mt | 2516 | 5510 | 11680 | 5760 | 5761 | | mwl | 1828 | 4316 | 15365 | 3216 | 3287 | | my | 24005 | 37165 | 49321 | 33223 | 33518 | | myv | 1732 | 2327 | 4094 | 2923 | 2925 | | mzn | 3784 | 4409 | 9938 | 5199 | 5205 | | nah | 1128 | 1314 | 3316 | 1418 | 1556 | | nap | 2047 | 2473 | 4579 | 2249 | 2249 | | nds | 20646 | 26845 | 65355 | 34090 | 34094 | | nds_nl | 2127 | 3063 | 10188 | 5585 | 5587 | | ne | 6956 | 10087 | 16847 | 13502 | 13536 | | new | 22645 | 27233 | 50860 | 32165 | 32217 | | nia | 312 | 430 | 512 | 277 | 329 | | nl | 490380 | 651743 | 1994062 | 1874588 | 1875259 | | nn | 44180 | 60918 | 194747 | 153072 | 153140 | | no | 172653 | 245377 | 779775 | 715618 | 716153 | | nov | 339 | 410 | 861 | 452 | 452 | | nqo | 583 | 1037 | 2598 | 704 | 813 | | nrm | 1318 | 1600 | 4276 | 3734 | 3736 | | nso | 960 | 1038 | 4242 | 4119 | 4119 | | nv | 5649 | 6281 | 13652 | 11768 | 11768 | | ny | 236 | 318 | 392 | 126 | 126 | | oc | 23067 | 33775 | 115155 | 87980 | 88063 | | olo | 1273 | 1598 | 2162 | 997 | 998 | | om | 401 | 830 | 891 | 401 | 412 | | or | 6261 | 8669 | 16120 | 6752 | 6757 | | os | 3923 | 4535 | 9130 | 5470 | 5524 | | pa | 17242 | 24844 | 37813 | 21759 | 21812 | | pag | 1602 | 4519 | 404 | 300 | 300 | | pam | 1509 | 1831 | 6019 | 3230 | 3272 | | pap | 773 | 1376 | 2526 | 2042 | 2056 | | pcd | 1089 | 1361 | 1803 | 1334 | 1338 | | pcm | 353 | 542 | 409 | 128 | 139 | | pdc | 370 | 565 | 839 | 424 | 429 | | pfl | 1113 | 1500 | 2861 | 1070 | 1070 | | pi | 578 | 682 | 881 | 26 | 26 | | pih | 118 | 125 | 317 | 217 | 218 | | pl | 444095 | 621669 | 2149058 | 2041686 | 2043400 | | pms | 16530 | 19186 | 41547 | 34783 | 35474 | | pnb | 21586 | 44654 | 103992 | 58461 | 59380 | | pnt | 147 | 172 | 389 | 177 | 178 | | ps | 7566 | 14922 | 8427 | 4108 | 4187 | | pt | 349931 | 580790 | 1868210 | 1745832 | 1745858 | | pwn | 103 | 166 | 85 | 31 | 31 | | qu | 4540 | 5211 | 14781 | 11746 | 11750 | | rm | 1076 | 3100 | 5539 | 2293 | 2298 | | rmy | 214 | 235 | 446 | 176 | 184 | | rn | 125 | 172 | 124 | 53 | 53 | | ro | 106169 | 168972 | 473512 | 416263 | 416347 | | roa_rup | 214 | 290 | 458 | 254 | 254 | | roa_tara | 1278 | 1979 | 4455 | 1534 | 1534 | | ru | 806592 | 1369860 | 3416036 | 3245837 | 3247963 | | rue | 2022 | 2513 | 7023 | 5064 | 5066 | | rw | 2577 | 3925 | 4139 | 2223 | 2349 | | sa | 4344 | 8607 | 11313 | 4249 | 4391 | | sah | 4729 | 8472 | 9040 | 6623 | 6660 | | sat | 3485 | 4960 | 6473 | 3225 | 3278 | | sc | 1900 | 2807 | 7641 | 5096 | 5098 | | scn | 4263 | 5604 | 14333 | 11167 | 11171 | | sco | 7382 | 9639 | 33771 | 16432 | 16453 | | sd | 3970 | 5499 | 8879 | 3804 | 3925 | | se | 982 | 1149 | 2841 | 1958 | 1958 | | sg | 67 | 72 | 36 | 24 | 24 | | sh | 103283 | 135121 | 484459 | 429555 | 429770 | | shi | 477 | 679 | 1144 | 545 | 570 | | shn | 3633 | 5630 | 5456 | 3627 | 3639 | | si | 7672 | 14760 | 16443 | 6215 | 6346 | | simple | 52503 | 68765 | 224811 | 187586 | 187598 | | sk | 67520 | 93957 | 317232 | 272711 | 272779 | | skr | 2090 | 6926 | 4136 | 1683 | 2359 | | sl | 55621 | 89740 | 285769 | 228421 | 228530 | | sm | 153 | 171 | 485 | 297 | 297 | | smn | 1163 | 1420 | 4517 | 2681 | 2688 | | sn | 1896 | 2139 | 4351 | 3384 | 3529 | | so | 2358 | 4032 | 6064 | 5027 | 5083 | | sq | 25223 | 41621 | 79295 | 59156 | 59350 | | sr | 177997 | 258455 | 728755 | 584663 | 585394 | | srn | 281 | 342 | 796 | 205 | 225 | | ss | 188 | 259 | 265 | 125 | 125 | | st | 157 | 198 | 248 | 164 | 166 | | stq | 804 | 1162 | 3150 | 1816 | 1816 | | su | 10348 | 13687 | 55055 | 42915 | 42944 | | sv | 467467 | 558522 | 2473790 | 2382576 | 2382608 | | sw | 18014 | 24348 | 90302 | 77817 | 78145 | | szl | 11292 | 12173 | 52459 | 14419 | 14424 | | szy | 2391 | 5418 | 2042 | 235 | 285 | | ta | 59923 | 87114 | 183399 | 126977 | 127148 | | tay | 1192 | 1757 | 1101 | 175 | 591 | | tcy | 769 | 1077 | 1089 | 464 | 465 | | te | 43790 | 79667 | 91327 | 69148 | 69484 | | tet | 294 | 412 | 871 | 471 | 471 | | tg | 27060 | 31599 | 86180 | 37522 | 37561 | | th | 49169 | 78814 | 189768 | 154097 | 154453 | | ti | 87 | 99 | 89 | 22 | 22 | | tk | 1328 | 2612 | 2116 | 1056 | 1062 | | tl | 11731 | 16623 | 49726 | 32858 | 32914 | | tn | 296 | 424 | 477 | 278 | 278 | | to | 254 | 277 | 393 | 230 | 233 | | tpi | 180 | 207 | 394 | 216 | 217 | | tr | 134938 | 200972 | 496960 | 440639 | 440790 | | trv | 807 | 1814 | 400 | 53 | 98 | | ts | 155 | 203 | 219 | 132 | 132 | | tt | 113689 | 132676 | 228544 | 185563 | 185662 | | tum | 2188 | 3516 | 6442 | 3105 | 4083 | | tw | 1249 | 1885 | 1729 | 1217 | 1291 | | ty | 162 | 167 | 215 | 143 | 143 | | tyv | 1494 | 2486 | 2342 | 611 | 617 | | udm | 1036 | 1240 | 2781 | 1957 | 1957 | | ug | 2629 | 6556 | 2657 | 1479 | 1493 | | uk | 203057 | 318240 | 758049 | 718278 | 718908 | | ur | 54784 | 75152 | 206169 | 99493 | 100041 | | uz | 65767 | 95465 | 149763 | 119192 | 120519 | | ve | 128 | 148 | 256 | 229 | 229 | | vec | 9463 | 11242 | 32188 | 22525 | 22531 | | vep | 3225 | 4804 | 10375 | 4295 | 4295 | | vi | 330763 | 455933 | 1211343 | 768936 | 769829 | | vls | 2189 | 2904 | 7133 | 5776 | 5777 | | vo | 7308 | 8647 | 13902 | 11270 | 11273 | | wa | 4457 | 6269 | 12736 | 8751 | 8794 | | war | 146537 | 149236 | 738087 | 666983 | 666983 | | wo | 516 | 864 | 1083 | 404 | 414 | | wuu | 5530 | 6448 | 13732 | 9168 | 9171 | | xal | 407 | 449 | 549 | 308 | 308 | | xh | 399 | 550 | 804 | 284 | 293 | | xmf | 4516 | 5414 | 19437 | 2342 | 2447 | | yi | 5260 | 7563 | 18821 | 12493 | 12510 | | yo | 4431 | 5855 | 9761 | 5361 | 5410 | | za | 335 | 414 | 777 | 457 | 458 | | zea | 1470 | 1847 | 3682 | 2569 | 2574 | | zh | 389361 | 611537 | 1817382 | 1592929 | 1597686 | | zh_classical | 3601 | 4995 | 15834 | 8157 | 8170 | | zh_min_nan | 87849 | 94529 | 291330 | 227978 | 228083 | | zh_yue | 23579 | 30146 | 92720 | 75081 | 75096 | | zu | 1646 | 2050 | 2518 | 2228 | 2234 | **NOTE:** The number of articles in the tables above refers to the number of articles that have at least one paragraph belonging to the article appear in the split. ## Additional Information ### Licensing Information The WikiAnc dataset is given under the [Creative Commons Attribution ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/) license." KETI-AIR/kor_duorc,"{""language"": [""ko""], ""license"": [""mit""], ""multilinguality"": [""monolingual""], ""size_categories"": [""10K(Male/Female/Unidentified) | |:---:|:---:|:---:|:---:|:---:| | ar-SA | validation | 2033 | 2.12 | 36 (22/14/0) | | | test | 2974 | 3.23 | 37 (15/17/5) | | | train_115 | 115 | 0.14 | 8 (4/4/0) | | de-DE | validation | 2033 | 2.33 | 68 (35/32/1) | | | test | 2974 | 3.41 | 82 (36/36/10) | | | train | 11514 | 12.61 | 117 (50/63/4) | | | train_115 | 115 | 0.15 | 7 (3/4/0) | | es-ES | validation | 2033 | 2.53 | 109 (51/53/5) | | | test | 2974 | 3.61 | 85 (37/33/15) | | | train_115 | 115 | 0.13 | 7 (3/4/0) | | fr-FR | validation | 2033 | 2.20 | 55 (26/26/3) | | | test | 2974 | 2.65 | 75 (31/35/9) | | | train | 11514 | 12.42 | 103 (50/52/1) | | | train_115 | 115 | 0.12 | 103 (50/52/1) | | hu-HU | validation | 2033 | 2.27 | 69 (33/33/3) | | | test | 2974 | 3.30 | 55 (25/24/6) | | | train_115 | 115 | 0.12 | 8 (3/4/1) | | ko-KR | validation | 2033 | 2.12 | 21 (8/13/0) | | | test | 2974 | 2.66 | 31 (10/18/3) | | | train_115 | 115 | 0.14 | 8 (4/4/0) | | nl-NL | validation | 2033 | 2.14 | 37 (17/19/1) | | | test | 2974 | 3.30 | 100 (48/49/3) | | | train_115 | 115 | 0.12 | 7 (3/4/0) | | pl-PL | validation | 2033 | 2.24 | 105 (50/52/3) | | | test | 2974 | 3.21 | 151 (73/71/7) | | | train_115 | 115 | 0.10 | 7 (3/4/0) | | pt-PT | validation | 2033 | 2.20 | 107 (51/53/3) | | | test | 2974 | 3.25 | 102 (48/50/4) | | | train_115 | 115 | 0.12 | 8 (4/4/0) | | ru-RU | validation | 2033 | 2.25 | 40 (7/31/2) | | | test | 2974 | 3.44 | 51 (25/23/3) | | | train_115 | 115 | 0.12 | 7 (3/4/0) | | tr-TR | validation | 2033 | 2.17 | 71 (36/34/1) | | | test | 2974 | 3.00 | 42 (17/18/7) | | | train_115 | 115 | 0.11 | 6 (3/3/0) | | vi-VN | validation | 2033 | 2.10 | 28 (13/14/1) | | | test | 2974 | 3.23 | 30 (11/14/5) | || train_115 | 115 | 0.11 | 7 (2/4/1) | ## How to use ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the French config, simply specify the corresponding language config name (i.e., ""fr-FR"" for French): ```python from datasets import load_dataset speech_massive_fr_train = load_dataset(""FBK-MT/Speech-MASSIVE"", ""fr-FR"", split=""train"") ``` In case you don't have enough space in the machine, you can stream dataset by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset speech_massive_de_train = load_dataset(""FBK-MT/Speech-MASSIVE"", ""de-DE"", split=""train"", streaming=True) list(speech_massive_de_train.take(2)) ``` You can also load all the available languages and splits at once. And then access each split. ```python from datasets import load_dataset speech_massive = load_dataset(""FBK-MT/Speech-MASSIVE"", ""all"") multilingual_validation = speech_massive['validation'] ``` Or you can load dataset's all the splits per language to separate languages more easily. ```python from datasets import load_dataset, interleave_datasets, concatenate_datasets # creating full train set by interleaving between German and French speech_massive_de = load_dataset(""FBK-MT/Speech-MASSIVE"", ""de-DE"") speech_massive_fr = load_dataset(""FBK-MT/Speech-MASSIVE"", ""fr-FR"") speech_massive_train_de_fr = interleave_datasets([speech_massive_de['train'], speech_massive_fr['train']]) # creating train_115 few-shot set by concatenating Korean and Russian speech_massive_ko = load_dataset(""FBK-MT/Speech-MASSIVE"", ""ko-KR"") speech_massive_ru = load_dataset(""FBK-MT/Speech-MASSIVE"", ""ru-RU"") speech_massive_train_115_ko_ru = concatenate_datasets([speech_massive_ko['train_115'], speech_massive_ru['train_115']]) ``` ## Dataset Structure ### Data configs - `all`: load all the 12 languages in one single dataset instance - `lang`: load only `lang` in the dataset instance, by specifying one of below languages - ```ar-SA, de-DE, es-ES, fr-FR, hu-HU, ko-KR, nl-NL, pl-PL, pt-PT, ru-RU, tr-TR, vi-VN``` ### Data Splits - `validation`: validation(dev) split available for all the 12 languages - `train_115`: few-shot (115 samples) split available for all the 12 languages - `train`: train split available for French (fr-FR) and German (de-DE) > [!WARNING] > `test` split is uploaded as a separate dataset on HF to prevent possible data contamination - ⚠️ `test`: available **_only_** in the separate HF dataset repository. ⚠️ - [https://huggingface.co/datasets/FBK-MT/Speech-MASSIVE-test](https://huggingface.co/datasets/FBK-MT/Speech-MASSIVE-test) ### Data Instances ```json { // Start of the data collected in Speech-MASSIVE 'audio': { 'path': 'train/2b12a21ca64a729ccdabbde76a8f8d56.wav', 'array': array([-7.80913979e-...7259e-03]), 'sampling_rate': 16000}, 'path': '/path/to/wav/file.wav', 'is_transcript_reported': False, 'is_validated': True, 'speaker_id': '60fcc09cb546eee814672f44', 'speaker_sex': 'Female', 'speaker_age': '25', 'speaker_ethnicity_simple': 'White', 'speaker_country_of_birth': 'France', 'speaker_country_of_residence': 'Ireland', 'speaker_nationality': 'France', 'speaker_first_language': 'French', // End of the data collected in Speech-MASSIVE // Start of the data extracted from MASSIVE // (https://huggingface.co/datasets/AmazonScience/massive/blob/main/README.md#data-instances) 'id': '7509', 'locale': 'fr-FR', 'partition': 'train', 'scenario': 2, 'scenario_str': 'calendar', 'intent_idx': 32, 'intent_str': 'calendar_query', 'utt': 'après les cours de natation quoi d autre sur mon calendrier mardi', 'annot_utt': 'après les cours de natation quoi d autre sur mon calendrier [date : mardi]', 'worker_id': '22', 'slot_method': {'slot': ['date'], 'method': ['translation']}, 'judgments': { 'worker_id': ['22', '19', '0'], 'intent_score': [1, 2, 1], 'slots_score': [1, 1, 1], 'grammar_score': [4, 4, 4], 'spelling_score': [2, 1, 2], 'language_identification': ['target', 'target', 'target'] }, 'tokens': ['après', 'les', 'cours', 'de', 'natation', 'quoi', 'd', 'autre', 'sur', 'mon', 'calendrier', 'mardi'], 'labels': ['Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'Other', 'date'], // End of the data extracted from MASSIVE } ``` ### Data Fields `audio.path`: Original audio file name `audio.array`: Read audio file with the sampling rate of 16,000 `audio.sampling_rate`: Sampling rate `path`: Original audio file full path `is_transcript_reported`: Whether the transcript is reported as 'syntatically wrong' by crowd-source worker `is_validated`: Whether the recorded audio has been validated to check if the audio matches transcript exactly by crowd-source worker `speaker_id`: Unique hash id of the crowd source speaker `speaker_sex`: Speaker's sex information provided by the crowd-source platform ([Prolific](http://prolific.com)) - Male - Female - Unidentified : Information not available from Prolific `speaker_age`: Speaker's age information provided by Prolific - age value (`str`) - Unidentified : Information not available from Prolific `speaker_ethnicity_simple`: Speaker's ethnicity information provided by Prolific - ethnicity value (`str`) - Unidentified : Information not available from Prolific `speaker_country_of_birth`: Speaker's country of birth information provided by Prolific - country value (`str`) - Unidentified : Information not available from Prolific `speaker_country_of_residence`: Speaker's country of residence information provided by Prolific - country value (`str`) - Unidentified : Information not available from Prolific `speaker_nationality`: Speaker's nationality information provided by Prolific - nationality value (`str`) - Unidentified : Information not available from Prolific `speaker_first_language`: Speaker's first language information provided by Prolific - language value (`str`) - Unidentified : Information not available from Prolific ### Limitations As Speech-MASSIVE is constructed based on the MASSIVE dataset, it inherently retains certain grammatical errors present in the original MASSIVE text. Correcting these errors was outside the scope of our project. However, by providing the `is_transcripted_reported` attribute in Speech-MASSIVE, we enable users of the dataset to be aware of these errors. ## License All datasets are licensed under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en). ### Citation Information Speech-MASSIVE is accepted at INTERSPEECH 2024 (Kos, Greece). You can access the [Speech-MASSIVE paper on arXiv](https://arxiv.org/abs/2408.03900). Please cite the paper when referencing the Speech-MASSIVE corpus as: ``` @misc{lee2024speechmassivemultilingualspeechdataset, title={Speech-MASSIVE: A Multilingual Speech Dataset for SLU and Beyond}, author={Beomseok Lee and Ioan Calapodescu and Marco Gaido and Matteo Negri and Laurent Besacier}, year={2024}, eprint={2408.03900}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2408.03900}, } ```" facebook/2M-Belebele,"{""license"": ""cc-by-sa-4.0"", ""task_categories"": [""question-answering"", ""automatic-speech-recognition""], ""language"": [""bg"", ""pa"", ""en"", ""hu"", ""sv"", ""af"", ""ca"", ""ka"", ""sk"", ""jv"", ""bn"", ""tr"", ""sr"", ""ro"", ""tg"", ""fa"", ""wo"", ""fi"", ""hy"", ""vi"", ""kea"", ""as"", ""ja"", ""nl"", ""ne"", ""lg"", ""hi"", ""xh"", ""kk"", ""mn"", ""yo"", ""km"", ""ha"", ""ru"", ""sw"", ""ps"", ""ko"", ""cs"", ""lv"", ""ig"", ""ar"", ""es"", ""nb"", ""lt"", ""fil"", ""it"", ""he"", ""da"", ""ml"", ""my"", ""el"", ""et"", ""pl"", ""sn"", ""sd"", ""or"", ""th"", ""luo"", ""sl"", ""fr"", ""id"", ""ta"", ""gu"", ""mk"", ""am"", ""pt"", ""cmn"", ""de"", ""ceb"", ""is"", ""ur"", ""az"", ""te""], ""tags"": [""speech-recognition"", ""multilingual"", ""flores200"", ""translation"", ""audio"", ""speech""], ""pretty_name"": ""2M Belebele Speech"", ""size_categories"": [""1Kin multiple languages** to be the **Vript_Multilingual**. **New in Vript_Multilingual**: 1. Multilingual: zh (60%), en (17%), de (15%), ja (6%), ko (2%), ru (<1%), es (<1%), pt (<1%), jv (<1%), fr (<1%), id (<1%), vi (<1%) 2. More diverse and fine-grained categories: 113 categories (please check [vript_CN-V2_meta.json](https://huggingface.co/datasets/Mutonix/Vript_Multilingual/blob/main/vript_CN-V2_meta.jsonl)) 3. Wider range: from 2011-01 to 2024-06 4. Higher resolution: 1080p 5. Longer duration: > 10 minutes in average 6. More clips: ~677k clips ## Getting Started **By downloading these datasets, you agree to the terms of the [License](#License).** The captions of the videos in the Vript_Multilingual dataset are structured as follows: ``` { ""meta"": { ""video_id"": ""xxx"", ""video_title"": ""..."", ""num_clips"": ..., ""integrity"": true, }, ""data"": { ""xxx-Scene-001"": { ""video_id"": ""xxx"", ""clip_id"": ""xxx-Scene-001"", ""video_title"": ""..."", ""caption"":{ ""shot_type"": ""..."", ""camera_movement"": ""..."", ""content"": ""..."", ""scene_title"": ""..."", }, ""voiceover"": [""...""], }, ""xxx-Scene-002"": { ... } } } ``` - `video_id`: The ID of the video from YouTube. - `video_title`: The title of the video. - `num_clips`: The number of clips in the video. If the `integrity` is `false`, some clips may not be captioned. - `integrity`: Whether all clips of the video are captioned. - `clip_id`: The ID of the clip in the video, which is the concatenation of the `video_id` and the scene number. - `caption`: The caption of the scene, including the shot type, camera movement, content, and scene title. - `voiceover`: The transcription of the voice-over in the scene. The data is organized as follows: ``` Vript_Multilingual/ | ├── vript_CN-V2_meta.json │ ├── vript_CN-V2_captions/ │ ├── vript_CN-V2_captions.zip │ └── vript_CN-V2_captions.jsonl │ ├── vript_CN-V2_videos/ │ ├── CN-V2_video_1_of_224.zip │ │ ├── xxx.mp4 │ │ └── ... │ ├── CN-V2_video_2_of_224.zip │ └── ... │ └── vript_CN-V2_clips/ ├── CN-V2_clips_1_of_224.zip │ ├── xxx/ │ │ ├── xxx_cut_meta.json │ │ ├── xxx_asr.jsonl │ │ ├── xxx-Scene-001.mp4 │ │ └── ... │ └── ... ├── CN-V2_clips_2_of_224.zip └── ... ``` - `vript_CN-V2_meta.json`: The meta information of the videos in the Vript_Multilingual dataset, including the video id, title, url, description, category, etc. - `vript_CN-V2_captions/`: The video captions of the videos in the Vript_Multilingual dataset, which are structured as described above. - `vript_CN-V2_videos/` (711 GB): The untrimmed videos in the Vript_Multilingual dataset. We divide the whole data into multiple zip files, each containing 200 videos. - `vript_CN-V2_clips/` (890 GB): The trimmed video clips in the Vript_Multilingual dataset, which correspond to scenes in the `video_CN-V2_captions`. - `xxx_cut_meta.json`: The meta information about how the video is trimmed, including the start time, end time, and the duration of the scene. - `xxx_asr.jsonl`: The transcription of the voice-over in the scene. ## License By downloading or using the data or model, you understand, acknowledge, and agree to all the terms in the following agreement. - ACADEMIC USE ONLY Any content from the Vript-related dataset and Vriptor model is available for academic research purposes only. You agree not to reproduce, duplicate, copy, trade, or exploit for any commercial purposes - NO DISTRIBUTION Respect the privacy of personal information of the original source. Without the permission of the copyright owner, you are not allowed to perform any form of broadcasting, modification or any other similar behavior to the data set content. - RESTRICTION AND LIMITATION OF LIABILITY In no event shall we be liable for any other damages whatsoever arising out of the use of, or inability to use this dataset and its associated software, even if we have been advised of the possibility of such damages. - DISCLAIMER You are solely responsible for legal liability arising from your improper use of the dataset content. We reserve the right to terminate your access to the dataset at any time. You should delete the Vript-related dataset or Vriptor model if required. This license is modified from the [HD-VG-100M](https://github.com/daooshee/HD-VG-130M) license. ## Citation ``` @misc{yang2024vript, title={Vript: A Video Is Worth Thousands of Words}, author={Dongjie Yang and Suyuan Huang and Chengqiang Lu and Xiaodong Han and Haoxin Zhang and Yan Gao and Yao Hu and Hai Zhao}, year={2024}, eprint={2406.06040}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Contact **Dongjie Yang**: [djyang.tony@sjtu.edu.cn](djyang.tony@sjtu.edu.cn) Paper: arxiv.org/abs/2406.06040" sentence-transformers/mldr,"{""multilinguality"": [""monolingual""], ""size_categories"": [""100K 1M | 9 | images > 1M | 6 total > 500K | 10 | images > 500K | 12 total > 100K | 36 | images > 100K | 35 total > 50K | 15 | images > 50K | 17 total > 14K | 38 | images > 13K | 38 ## Dataset Structure ### Data Instances ``` { 'language': 'en', 'page_url': 'https://en.wikipedia.org/wiki/Oxydactylus', 'image_url': 'https://upload.wikimedia.org/wikipedia/commons/5/5f/Oxydactylus_longipes_fm.jpg', 'page_title': 'Oxydactylus', 'section_title': None, 'hierarchical_section_title': 'Oxydactylus', 'caption_reference_description': None, 'caption_attribution_description': 'English: Mounted skeleton of Oxydactylus longipes in the Field Museum of Natural History.', 'caption_alt_text_description': None, 'mime_type': 'image/jpeg', 'original_height': 3564, 'original_width': 2748, 'is_main_image': True, 'attribution_passes_lang_id': True, 'page_changed_recently': True, 'context_page_description': 'Oxydactylus is an extinct genus of camelid endemic to North America. It lived from the Late Oligocene to the Middle Miocene, existing for approximately 14 million years. The name is from the Ancient Greek οξύς and δάκτυλος.\nThey had very long legs and necks, and were probably adapted to eating high vegetation, much like modern giraffes. Unlike modern camelids, they had hooves, rather than tough sole-pads, and splayed toes.', 'context_section_description': 'Oxydactylus is an extinct genus of camelid endemic to North America. It lived from the Late Oligocene to the Middle Miocene (28.4–13.7 mya), existing for approximately 14 million years. The name is from the Ancient Greek οξύς (oxys, ""sharp"")and δάκτυλος (daktylos, ""finger"").\n \nThey had very long legs and necks, and were probably adapted to eating high vegetation, much like modern giraffes. Unlike modern camelids, they had hooves, rather than tough sole-pads, and splayed toes.' } ``` ### Data Fields - `language`: Language code depicting wikipedia language of the page - `page_url`: URL to wikipedia page - `image_url`: URL to wikipedia image - `page_title`: Wikipedia page's title - `section_title`: Section's title - `hierarchical_section_title`: Hierarchical section's title - `caption_reference_description`: This is the caption that is visible on the wiki page directly below the image. - `caption_attribution_description`: This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias and thus can be in a language different to the original page article. - `caption_alt_text_description`: This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers - `mime_type`: Mime type associated to the image. - `original_height`: Image height - `original_width`: Image width - `is_main_image`: Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers. - `attribution_passes_lang_id`: Compared `language` field with the attribution language (written in the prefix of the attribution description). - `page_changed_recently`: [More Information Needed] - `context_page_description`: Page description corresponds to the short description of the page. It provides a concise explanation of the scope of the page. - `context_section_description`: Text within the image's section.

Figure: WIT annotation example.

Details on the field content can be found directly in the [paper, figure 5 and table 12.](https://arxiv.org/abs/2103.01913) ### Data Splits All data is held in `train` split, with a total of 37046386 rows. ## Dataset Creation ### Curation Rationale From the [repository](https://github.com/google-research-datasets/wit#motivation): > Multimodal visio-linguistic models rely on a rich dataset to help them learn to model the relationship between images and texts. Having large image-text datasets can significantly improve performance, as shown by recent works. Furthermore the lack of language coverage in existing datasets (which are mostly only in English) also impedes research in the multilingual multimodal space – we consider this a lost opportunity given the potential shown in leveraging images (as a language-agnostic medium) to help improve our multilingual textual understanding. > > To address these challenges and advance research on multilingual, multimodal learning we created the Wikipedia-based Image Text (WIT) Dataset. WIT is created by extracting multiple different texts associated with an image (e.g., as shown in the above image) from Wikipedia articles and Wikimedia image links. This was accompanied by rigorous filtering to only retain high quality image-text sets. > > The resulting dataset contains over 37.6 million image-text sets – making WIT the largest multimodal dataset (publicly available at the time of this writing) with unparalleled multilingual coverage – with 12K+ examples in each of 108 languages (53 languages have 100K+ image-text pairs). ### Source Data #### Initial Data Collection and Normalization From the [paper, section 3.1](https://arxiv.org/abs/2103.01913): > We started with all Wikipedia content pages (i.e., ignoring other pages that have discussions, comments and such). These number about ∼124M pages across 279 languages. #### Who are the source language producers? Text was extracted from Wikipedia. ### Annotations #### Annotation process WIT was constructed using an automatic process. However it was human-validated. From the [paper, section 3.7](https://arxiv.org/abs/2103.01913): > To further verify the quality of the WIT dataset we performed a study using (crowd-sourced) human annotators. As seen in Fig. 3, we asked raters to answer 3 questions. Given an image and the page title, raters first evaluate the quality of the attribution description and reference description in the first two questions (order randomized). The third question understands the contextual quality of these text descriptions given the page description and caption. Each response is on a 3-point scale: ""Yes"" if the text perfectly describes the image, ""Maybe"" if it is sufficiently explanatory and ""No"" if it is irrelevant or the image is inappropriate. #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases From the [paper, section 3.4](https://arxiv.org/abs/2103.01913): > Lastly we found that certain image-text pairs occurred very frequently. These were often generic images that did not have much to do with the main article page. Common examples included flags, logos, maps, insignia and such. To prevent biasing the data, we heavily under-sampled all such images ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @article{srinivasan2021wit, title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning}, author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc}, journal={arXiv preprint arXiv:2103.01913}, year={2021} } ``` ### Contributions Thanks to [@thomasw21](https://github.com/thomasw21), [@nateraw](https://github.com/nateraw) and [hassiahk](https://github.com/hassiahk) for adding this dataset." msarmi9/korean-english-multitarget-ted-talks-task,"{""annotations_creators"": [""expert-generated""], ""language_creators"": [""other""], ""language"": [""en"", ""ko""], ""language_bcp47"": [""en-US"", ""ko-KR""], ""license"": [""cc-by-nc-nd-4.0""], ""multilinguality"": [""translation"", ""multilingual""], ""pretty_name"": ""English-Korean Multitarget Ted Talks Task (MTTT)"", ""task_categories"": [""conditional-text-generation""], ""task_ids"": [""machine-translation""]}","# Dataset Card for english-korean-multitarget-ted-talks-task ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.cs.jhu.edu/~kevinduh/a/multitarget-tedtalks/ ### Dataset Summary - Parallel English-Korean Text Corpus - Text was originally transcribed to English from various Ted Talks, then translated to Korean by TED translators - Approximately 166k train, 2k validation, and 2k test sentence pairs. ### Supported Tasks and Leaderboards - Machine Translation ### Languages - English - Korean ## Additional Information ### Dataset Curators Kevin Duh, ""The Multitarget TED Talks Task"", http://www.cs.jhu.edu/~kevinduh/a/multitarget-tedtalks/, 2018 ### Licensing Information TED makes its collection available under the Creative Commons BY-NC-ND license. Please acknowledge TED when using this data. We acknowledge the authorship of TED Talks (BY condition). We are not redistributing the transcripts for commercial purposes (NC condition) nor making derivative works of the original contents (ND condition). ### Citation Information @misc{duh18multitarget, author = {Kevin Duh}, title = {The Multitarget TED Talks Task}, howpublished = {\url{http://www.cs.jhu.edu/~kevinduh/a/multitarget-tedtalks/}}, year = {2018}, }" DAMO-NLP-SG/MultiJail,"{""license"": ""mit"", ""task_categories"": [""conversational""], ""language"": [""en"", ""zh"", ""it"", ""vi"", ""ar"", ""ko"", ""th"", ""bn"", ""sw"", ""jv""], ""size_categories"": [""n<1K""]}","# Multilingual Jailbreak Challenges in Large Language Models This repo contains the data for our paper [""Multilingual Jailbreak Challenges in Large Language Models""](https://arxiv.org/abs/2310.06474). [[Github repo]](https://github.com/DAMO-NLP-SG/multilingual-safety-for-LLMs/) ## Annotation Statistics We collected a total of 315 English unsafe prompts and annotated them into nine non-English languages. The languages were categorized based on resource availability, as shown below: **High-resource languages:** Chinese (zh), Italian (it), Vietnamese (vi) **Medium-resource languages:** Arabic (ar), Korean (ko), Thai (th) **Low-resource languages:** Bengali (bn), Swahili (sw), Javanese (jv) ## Ethics Statement Our research investigates the safety challenges of LLMs in multilingual settings. We are aware of the potential misuse of our findings and emphasize that our research is solely for academic purposes and ethical use. Misuse or harm resulting from the information in this paper is strongly discouraged. To address the identified risks and vulnerabilities, we commit to open-sourcing the data used in our study. This openness aims to facilitate vulnerability identification, encourage discussions, and foster collaborative efforts to enhance LLM safety in multilingual contexts. Furthermore, we have developed the SELF-DEFENSE framework to address multilingual jailbreak challenges in LLMs. This framework automatically generates multilingual safety training data to mitigate risks associated with unintentional and intentional jailbreak scenarios. Overall, our work not only highlights multilingual jailbreak challenges in LLMs but also paves the way for future research, collaboration, and innovation to enhance their safety. ## Citation ``` @misc{deng2023multilingual, title={Multilingual Jailbreak Challenges in Large Language Models}, author={Yue Deng and Wenxuan Zhang and Sinno Jialin Pan and Lidong Bing}, year={2023}, eprint={2310.06474}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```" sentence-transformers/parallel-sentences-opensubtitles,"{""language"": [""en"", ""multilingual"", ""ar"", ""bg"", ""ca"", ""cs"", ""da"", ""de"", ""el"", ""es"", ""et"", ""fa"", ""fi"", ""fr"", ""gl"", ""he"", ""hi"", ""hr"", ""hu"", ""hy"", ""id"", ""it"", ""ja"", ""ka"", ""ko"", ""lt"", ""lv"", ""mk"", ""ms"", ""nl"", ""pl"", ""pt"", ""ro"", ""ru"", ""sk"", ""sl"", ""sq"", ""sr"", ""sv"", ""th"", ""tr"", ""uk"", ""ur"", ""vi"", ""zh""], ""size_categories"": [""100MBelow is original dataset card ## Table of Contents - [Dataset Summary](#dataset-summary) - [Dataset Attribution](#dataset-attribution) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Dataset Use](#dataset-use) - [Use Cases](#use-cases) - [Usage Caveats](#usage-caveats) - [Getting Started](#getting-started)

🐋 The OpenOrca Dataset! 🐋

additional details

The columns corresponding to annotations collected from our cultural bias study (i.e. 'required_knowledge', 'time_sensitive', 'reference', 'culture', 'region', 'country') contain a list of values representing annotations from different annotators. However, to avoid conversion issues to HF dataset, these columns are provided as string in the final dataset. You can convert these columns back to list of values for easier manipulation as follows: ```python import ast # convert string values to list gmmlu_lite_test['required_knowledge'] = gmmlu_lite_test['required_knowledge'].apply(lamda x: ast.literal_eval(x)) ```
## Data Fields The data fields are the same among all splits. Brief description of each field is provided below.

data field description

## Data Splits The following are the splits of the data: | Split | No. of instances | Language Coverage | |-------|------------------|-------------------| | test | 6,000 | 15 | | dev | 4,275 | 15 | ## Data Instances An example from `test` set looks as follows: ```json {'sample_id': 'astronomy/test/58', 'subject': 'astronomy', 'subject_category': 'STEM', 'question': 'When traveling north from the United States into Canada you’ll see the North Star (Polaris) getting _________.', 'option_a': 'Brighter', 'option_b': 'Dimmer', 'option_c': 'Higher in the sky', 'option_d': 'Lower in the sky', 'answer': 'C', 'required_knowledge': ""['regional', 'regional', 'regional', 'regional']"", 'time_sensitive': ""['No', 'No', 'No', 'No']"", 'reference': ""[{'end': 55, 'label': 'Geographic', 'score': None, 'start': 5}, {'end': 43, 'label': 'Geographic', 'score': None, 'start': 30}, {'end': 55, 'label': 'Geographic', 'score': None, 'start': 5}, {'end': 43, 'label': 'Geographic', 'score': None, 'start': 30}]"", 'culture': '[]', 'region': ""['North America', 'North America', 'North America', 'North America']"", 'country': ""['United States of America (USA)', 'United States of America (USA)', 'United States of America (USA)', 'United States of America (USA)']"", 'cultural_sensitivity_label': 'CS', 'is_annotated': True } ``` ## Statistics ### Annotation Types The following is the breakdown of CS🗽, CA⚖️ and MA📝 samples in the final dataset. | Type of Annotation | Instances per language | No. of languages | Total instances |--------------------|------------------------|------------------|----------------| | Culturally Sensitive 🗽 | 200 | 15 | 3,000 | | Culturally Agnostic ⚖️ | 200 |15 | 3,000 | | MMLU Annotated 📝| 400 |15 | 6,000 | ### Languages The dataset covers 15 languages. The following is details about the languages included in the dataset.

Languages Info

| ISO Code | Language | Resources | |----------|----------|-----------| | `ar` | Arabic (Standard)| High | | `bn` | Bengali | Mid | | `de` | German | High | | `en` | English | High | | `fr` | French | High | | `hi` | Hindi | High | | `id` | Indonesian | Mid | | `it` | Italian | High | | `ja` | Japanese | High | | `ko` | Korean | Mid | | `pt` | Portuguese | High | | `es` | Spanish | High | | `sw` | Swahili | Low | | `yo` | Yorùbá | Low | | `zh` | Chinese (Simplified) | High |

# Known Limitations A brief overview of limitations of this dataset is provided below.

show limitations

# Additional Information ## Provenance - **Methods Used:** Professional annotations as well as crowd-sourced through volunteer annotations. - **Methodology Details:** We collected cultural bias annotations as well as post-edits of translations for different mmlu questions. - [Cultural Sensitivity Annotation Platform](https://huggingface.co/spaces/CohereForAI/MMLU-evaluation) - [Translation Quality Annotation Platform](https://huggingface.co/spaces/CohereForAI/review-mmlu-translations) - Dates of Collection: May 2024 - Aug 2024 ## Dataset Version and Maintenance - **Maintenance Status:** Actively Maintained - **Version Details:** - *Current version:* 1.0 - *Last Update:* 12/2024 - *First Release:* 12/2024 ## Authorship - **Publishing Organization:** [Cohere For AI](https://cohere.com/research) - **Industry Type:** Not-for-profit - Tech ## Licensing Information This dataset can be used for any purpose, under the terms of the [Apache 2.0](https://opensource.org/license/apache-2-0) License. ## Continuous Improvement: If you want to contribute to improving the quality of translations in Global-MMLU-Lite then please contribute using our [annotation UI](https://huggingface.co/spaces/CohereForAI/review-global-mmlu-lite). You can also help review and edit machine translations in additional languages using our annotation interface to help improve language coverage of Global-MMLU-Lite. ## Additional Details For any additional details, please check our paper, [Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://arxiv.org/abs/2412.03304). ## Citation Information ```bibtex @misc{singh2024globalmmluunderstandingaddressing, title={Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation}, author={Shivalika Singh and Angelika Romanou and Clémentine Fourrier and David I. Adelani and Jian Gang Ngui and Daniel Vila-Suero and Peerat Limkonchotiwat and Kelly Marchisio and Wei Qi Leong and Yosephine Susanto and Raymond Ng and Shayne Longpre and Wei-Yin Ko and Madeline Smith and Antoine Bosselut and Alice Oh and Andre F. T. Martins and Leshem Choshen and Daphne Ippolito and Enzo Ferrante and Marzieh Fadaee and Beyza Ermis and Sara Hooker}, year={2024}, eprint={2412.03304}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.03304}, } ```" gsarti/iwslt2017_context,"{""annotations_creators"": [""crowdsourced""], ""language"": [""ar"", ""de"", ""en"", ""fr"", ""it"", ""ja"", ""ko"", ""nl"", ""ro"", ""zh""], ""language_creators"": [""expert-generated""], ""license"": [""cc-by-nc-nd-4.0""], ""multilinguality"": [""translation""], ""pretty_name"": ""IWSLT 2017"", ""size_categories"": [""1M>> from datasets import load_dataset >>> ko_lima = load_dataset('taeshahn/ko-lima', 'plain') # or load_dataset('taeshahn/ko-lima') >>> ko_lima_vicuna = load_dataset('taeshahn/ko-lima', 'vicuna') ``` ```python >>> ko_lima['train'][1025] { 'conversations': [ '저는 케냐 출신입니다. 망명을 신청하고 싶은데 비자없이 네덜란드로 망명을 신청하기 위해 여행할 수 있나요? 케냐항공에서 여권을 소지한 경우 스키폴 공항으로 가는 비자없이 비행기에 탑승할 수 있나요?', '항공사가 탑승을 허용할 가능성은 극히 낮습니다. 네덜란드에 입국하려는 케냐 시민은 비자, 체류 기간에 필요한 충분한 자금 증명, 다음 목적지 입국에 필요한 서류를 소지해야 합니다. 또한 항공사는 케냐에서 출발하는 승객에 대해 특별 조사를 실시해야 합니다:\n\n> 다음 공항에서 네덜란드로 운항하는 항공사:\n\n아부다비(AUH), 아크라(ACC), 바레인(BAH), 베이징 캐피탈(PEK), 보리스필(KBP), 카이로(CAI), 담만(DMM), 다르에스살람(DAR), 두바이(DXB), 엔테베(EBB), 광저우(CAN), 홍콩(HKG), 하마드(DOH), 이스탄불 아타튀르크(IST), 이스탄불 사비하곡첸(SAW), 요하네스버그(JNB), 키갈리(KGL), 킬리만자로(JRO), 쿠알라룸푸르(KUL), 쿠웨이트(KWI), 라고스(LOS), 모스크바 셰레메티예보(SVO), 무스카트(MCT), 나이로비(NB ), 뉴델리(DEL), 상파울루(GRU), 싱가포르(SIN) 및 테헤란(IKA)은 네덜란드 도착 시 적절한 여행 서류가 없어 입국할 수 없는 경우 해당 항공편의 모든 승객의 여행 서류 사본을 제출하셔야 합니다. 사본에는 여권/여행 서류의 데이터 페이지, 비자가 있는 페이지, 출국/통관 스탬프가 있는 페이지(해당되는 경우)가 포함되어야 합니다. 이러한 입국 규정을 준수하지 않을 경우 항공사에 승객 1인당 최대 11,250유로의 벌금이 부과될 수 있습니다.\n\n마지막 문장에서 볼 수 있듯이 항공사는 탑승 전에 여행 서류를 확인해야 하는 강력한 인센티브를 갖게 됩니다.', '인용된 자료의 출처는 무엇인가요?', '출처는 항공을 통해 해외로 여행하는 승객의 여권 및 비자 요건과 같은 서류 요건이 포함된 데이터베이스인 Timatic(여행 정보 매뉴얼 자동)에서 가져온 것입니다. 위의 답변은 목적지가 네덜란드이고 귀국/귀국 여행 증빙이 있으며 5일간 체류할 계획이며 케냐 여권의 유효 기간이 약 3년 남은 케냐 시민이 케냐에서 직접 여행하는 시나리오를 기반으로 한 것입니다.' ], 'source': 'multi_turn' } ``` ```python >>> ko_lima_vicuna['train'][1025] { 'id': '1026-multi_turn', 'conversations': [ { 'from': '사람', 'value': '저는 케냐 출신입니다. 망명을 신청하고 싶은데 비자없이 네덜란드로 망명을 신청하기 위해 여행할 수 있나요? 케냐항공에서 여권을 소지한 경우 스키폴 공항으로 가는 비자없이 비행기에 탑승할 수 있나요?' }, { 'from': '어시스턴트', 'value': '항공사가 탑승을 허용할 가능성은 극히 낮습니다. 네덜란드에 입국하려는 케냐 시민은 비자, 체류 기간에 필요한 충분한 자금 증명, 다음 목적지 입국에 필요한 서류를 소지해야 합니다. 또한 항공사는 케냐에서 출발하는 승객에 대해 특별 조사를 실시해야 합니다:\n\n> 다음 공항에서 네덜란드로 운항하는 항공사:\n\n아부다비(AUH), 아크라(ACC), 바레인(BAH), 베이징 캐피탈(PEK), 보리스필(KBP), 카이로(CAI), 담만(DMM), 다르에스살람(DAR), 두바이(DXB), 엔테베(EBB), 광저우(CAN), 홍콩(HKG), 하마드(DOH), 이스탄불 아타튀르크(IST), 이스탄불 사비하곡첸(SAW), 요하네스버그(JNB), 키갈리(KGL), 킬리만자로(JRO), 쿠알라룸푸르(KUL), 쿠웨이트(KWI), 라고스(LOS), 모스크바 셰레메티예보(SVO), 무스카트(MCT), 나이로비(NB ), 뉴델리(DEL), 상파울루(GRU), 싱가포르(SIN) 및 테헤란(IKA)은 네덜란드 도착 시 적절한 여행 서류가 없어 입국할 수 없는 경우 해당 항공편의 모든 승객의 여행 서류 사본을 제출하셔야 합니다. 사본에는 여권/여행 서류의 데이터 페이지, 비자가 있는 페이지, 출국/통관 스탬프가 있는 페이지(해당되는 경우)가 포함되어야 합니다. 이러한 입국 규정을 준수하지 않을 경우 항공사에 승객 1인당 최대 11,250유로의 벌금이 부과될 수 있습니다.\n\n마지막 문장에서 볼 수 있듯이 항공사는 탑승 전에 여행 서류를 확인해야 하는 강력한 인센티브를 갖게 됩니다.' }, { 'from': '사람', 'value': '인용된 자료의 출처는 무엇인가요?' }, { 'from': '어시스턴트', 'value': '출처는 항공을 통해 해외로 여행하는 승객의 여권 및 비자 요건과 같은 서류 요건이 포함된 데이터베이스인 Timatic(여행 정보 매뉴얼 자동)에서 가져온 것입니다. 위의 답변은 목적지가 네덜란드이고 귀국/귀국 여행 증빙이 있으며 5일간 체류할 계획이며 케냐 여권의 유효 기간이 약 3년 남은 케냐 시민이 케냐에서 직접 여행하는 시나리오를 기반으로 한 것입니다.' } ] } ``` ### Citation Information ``` @InProceedings{kolimadataset, title = {KoLIMA: Korean LIMA Dataset for Efficient Instruction-tuning}, author = {Hahn, Taeseung}, year = {2023} } ```" kuotient/gsm8k-ko,"{""dataset_info"": {""features"": [{""name"": ""question"", ""dtype"": ""string""}, {""name"": ""answer"", ""dtype"": ""string""}, {""name"": ""question_en"", ""dtype"": ""string""}, {""name"": ""answer_en"", ""dtype"": ""string""}], ""splits"": [{""name"": ""train"", ""num_bytes"": 8792462, ""num_examples"": 7473}, {""name"": ""test"", ""num_bytes"": 1585126, ""num_examples"": 1319}], ""download_size"": 6575639, ""dataset_size"": 10377588}, ""configs"": [{""config_name"": ""default"", ""data_files"": [{""split"": ""train"", ""path"": ""data/train-*""}, {""split"": ""test"", ""path"": ""data/test-*""}]}], ""language"": [""ko""], ""pretty_name"": ""g""}","번역 모델 `kuotient/Seagull-13B-translate` 사용. ## How to evaluate ``` git clone https://github.com/kuotient/lm-evaluation-harness.git cd lm-evaluation-harness pip install -e . ``` ``` lm_eval --model hf \ --model_args pretrained=yanolja/EEVE-Korean-Instruct-2.8B-v1.0 \ --tasks gsm8k-ko \ --device cuda:0 \ --batch_size auto:4 ``` 혹은 원본 lm-evaluation-harness에서 데이터셋 내의 `gsm8k-ko.yaml` 파일을 `lm-evaluation-harness/tasks/gsm8k-ko` 내에 생성해 사용." leey4n/KR3,"{""annotations_creators"": [], ""language_creators"": [], ""language"": [""ko""], ""license"": [""cc-by-nc-sa-4.0""], ""multilinguality"": [""monolingual""], ""pretty_name"": ""KR3"", ""size_categories"": [""100K ""num_docs"": ""title"": ""intro"": ""section_name"": ""previous_text"": ""question"": ""gold_section_text"": ""en_gold_section_text"": ""citations"": } ``` ## Licensing and Takedown MegaWika 1.0 consists in part of documents scraped from across the web (based on citations linked in Wikipedia articles.) We do not own any of the scraped text nor do we claim copyright: text drawn from Wikipedia citations are meant for research use in algorithmic design and model training. We release this dataset and all its contents under CC-BY-SA-4.0. ### Notice and Takedown Policy: *NB*: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: - Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. - Clearly identify the copyrighted work claimed to be infringed. - Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material. And contact the authors. *Take down*: We will comply to legitimate requests by removing the affected sources from the next release of the dataset. ## Usage ``` # all of the dataset (not recommended) dataset = load_dataset(""hltcoe/megawika-report-generation"") # just the `all`` section data (all splits) dataset = load_dataset(""hltcoe/megawika-report-generation"", data_dir=""all"") # just the `all` English test set (can replace with ""validation"" or ""train"", or other langs) dataset = load_dataset(""hltcoe/megawika-report-generation"", data_dir=""all/en"", split=""test"") ``` ### Dataset Curators Released and maintained by the Johns Hopkins University Human Language Technology Center of Excellence (JHU/HLTCOE). You can contact one the MegaWika authors, including [Samuel Barham](mailto:samuel.barham@jhuapl.edu), [Orion Weller](mailto:oweller2@jhu.edu), and [Ben van Durme](mailto:vandurme@jhu.edu) with questions. ### Licensing Information Released under the [Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/) license. ### Citation Information ``` @misc{barham2023megawika, title={MegaWika: Millions of reports and their sources across 50 diverse languages}, author={Samuel Barham and and Weller and Michelle Yuan and Kenton Murray and Mahsa Yarmohammadi and Zhengping Jiang and Siddharth Vashishtha and Alexander Martin and Anqi Liu and Aaron Steven White and Jordan Boyd-Graber and Benjamin Van Durme}, year={2023}, eprint={2307.07049}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```" joonhok-exo-ai/korean_law_open_data_precedents,"{""language"": [""ko""], ""tags"": [""legal""], ""size_categories"": [""10K, # text containing w words (one per language) separated by underscores 'text': 'σπιτάκι πουλιών_ドーム_प्रयोगशाला कोट_мавпа-павук_gown', # target word class name in English (key in translations.json) 'cls': 'dome', # class ID from translations.json (0 to 999) 'cls_id': 538, # target word (class name in the language of the audio) 'target_text': 'ドーム' } ``` The dataset includes a `translations.json` file that maps ImageNet class names across all supported languages. Each entry contains: - The English class name as the key - Translations for all supported languages (`ar`, `el`, `en`, `hi`, `ja`, `ko`, `te`, `th`, `uk`, `zh-CN`) - The ImageNet synset ID - A unique class ID (0-999) Example structure: ```json { ""tench"": { ""synset_id"": ""n01440764"", ""cls_id"": 0, ""ar"": ""سمك البنش"", ""el"": ""είδος κυπρίνου"", ""en"": ""tench"", ""hi"": ""टेंच"", ""ja"": ""テンチ"", ""ko"": ""텐치"", ""te"": ""టెంచ్"", ""th"": ""ปลาเทนช์"", ""uk"": ""линь"", ""zh-CN"": ""丁鱥"" } } ``` ## Dataset Variants We release three variants of the dataset: - Symile-M3-2 with 2 languages: English (`en`) and Greek (`el`). - Symile-M3-5 with 5 languages: English (`en`), Greek (`el`), Hindi (`hi`), Japanese (`ja`), and Ukrainian (`uk`). - Symile-M3-10 with 10 languages: Arabic (`ar`), Greek (`el`), English (`en`), Hindi (`hi`), Japanese (`ja`), Korean (`ko`), Telugu (`te`), Thai (`th`), Ukrainian (`uk`), and Chinese (`zh-CN`). Each variant is available in four sizes: - Large (`l`): 10M training samples, 500K validation samples, 500K test samples - Medium (`m`): 5M training samples, 250K validation samples, 250K test samples - Small (`s`): 1M training samples, 50K validation samples, 50K test samples - Extra Small (`xs`): 500K training samples, 25K validation samples, 25K test samples ## Usage Before using the dataset, ensure you have the required audio and image processing libraries installed: ```bash pip install librosa soundfile pillow ``` To load a specific version of Symile-M3, use a configuration name following the pattern `symile-m3-{num_langs}-{size}` where: - `num_langs` is `2`, `5`, or `10` - `size` is `xs`, `s`, `m`, or `l` For example, to load the `xs` version of Symile-M3-5: ```python from datasets import load_dataset dataset = load_dataset(""arsaporta/symile-m3"", ""symile-m3-5-xs"") print(dataset['train'][0]) # access first train sample print(len(dataset['train'])) # get number of train samples ``` To process the dataset without loading it entirely into memory, use streaming mode to load samples one at a time: ```python from datasets import load_dataset dataset = load_dataset(""arsaporta/symile-m3"", ""symile-m3-5-xs"", streaming=True) print(next(iter(dataset['train']))) ``` To download the dataset for offline use: ```python import os from datasets import load_dataset from huggingface_hub import snapshot_download local_dir = ""./symile-m3-5-xs"" # where to save # download parquet files snapshot_download( repo_id=""arsaporta/symile-m3"", repo_type=""dataset"", local_dir=local_dir, allow_patterns=[""symile-m3-5-xs/*""] # which configuration to download ) # load the downloaded parquet files dataset = load_dataset( ""parquet"", data_files={ ""train"": os.path.join(data_dir, ""train-*.parquet""), ""validation"": os.path.join(data_dir, ""val-*.parquet""), ""test"": os.path.join(data_dir, ""test-*.parquet"") } ) ``` ## Working with Raw Data To work directly with the source images (jpeg) and audio (mp3): 1. Download the source data: - **ImageNet:** Get the training data from [Kaggle's ImageNet Challenge](https://www.kaggle.com/c/imagenet-object-localization-challenge/data?select=ILSVRC) - **Common Voice:** Download your needed languages from [Common Voice](https://commonvoice.mozilla.org/en/datasets): * All languages use Common Voice v16.0, except English which uses v14.0 * Required languages vary by configuration: - Symile-M3-2: English (`en`), Greek (`el`) - Symile-M3-5: English, Greek, Hindi (`hi`), Japanese (`ja`), Ukrainian (`uk`) - Symile-M3-10: All of the above plus Arabic (`ar`), Korean (`ko`), Telugu (`te`), Thai (`th`), Chinese (`zh-CN`) 2. Access the dataset CSV files: - Find them in the `.csv_files` directory, organized by configuration (e.g., `symile-m3-2-xs`, `symile-m3-10-l`) - Each configuration contains `train.csv`, `val.csv`, and `test.csv` - CSV paths match the default extraction paths of ImageNet (`ILSVRC/Data/CLS-LOC/train/...`) and Common Voice (`cv/{lang}/clips/...`) ## Citation ``` @inproceedings{saporta2024symile, title = {Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities} author = {Saporta, Adriel and Puli, Aahlad and Goldstein, Mark and Ranganath, Rajesh} booktitle = {Advances in Neural Information Processing Systems}, year = {2024} } ```" bltlab/lr-sum,"{""license"": ""cc-by-4.0"", ""task_categories"": [""summarization"", ""text-generation""], ""annotations_creators"": [""found""], ""language_creators"": [""found""], ""language"": [""am"", ""az"", ""bn"", ""bo"", ""bs"", ""ku"", ""zh"", ""el"", ""en"", ""fa"", ""fr"", ""ht"", ""ha"", ""hy"", ""id"", ""ka"", ""km"", ""rw"", ""ko"", ""lo"", ""mk"", ""my"", ""nd"", ""pt"", ""ps"", ""ru"", ""sn"", ""so"", ""es"", ""sq"", ""sr"", ""sw"", ""th"", ""ti"", ""tr"", ""uk"", ""ur"", ""uz"", ""vi""], ""pretty_name"": ""LR-Sum"", ""size_categories"": [""100K

Models	AR	DE	FR	IT	JA	KO	RU	TH	VI	Average
GPT-4O	20.2	34.2	41.2	32.7	20.0	33.9	11.5	22.5	34.2	27.8
Claude3 Opus	15.1	33.4	40.6	34.4	19.4	27.2	13.0	19.5	29.1	25.7
Gemini Ultra	14.7	32.3	40.0	31.8	12.3	17.2	11.8	20.3	28.6	23.2
GPT-4V	11.5	31.5	40.4	32.3	11.5	16.7	10.3	15.0	28.9	22.0
QwenVL Max	7.7	31.4	37.6	30.2	18.6	25.4	10.4	4.8	23.5	21.1
Claude3 Sonnet	10.5	28.9	35.6	31.8	13.9	22.2	11.0	15.2	20.8	21.1
QwenVL Plus	4.8	28.8	33.7	27.1	12.8	19.9	9.4	5.6	18.1	17.8
MiniCPM-Llama3-V-2_5	6.1	29.6	35.7	26.0	12.1	13.1	5.7	12.6	15.3	17.3
InternVL-V1.5	3.4	27.1	31.4	27.1	9.9	9.0	4.9	8.7	12.4	14.9
GLM4V	0.3	30.0	34.1	30.1	3.4	5.7	3.0	3.5	12.3	13.6
TextSquare	3.7	27.0	30.8	26.7	3.2	7.2	6.7	5.2	12.4	13.6
Mini-Gemini-HD-34B	2.2	25.0	29.2	25.5	6.1	8.6	4.1	4.3	11.8	13.0
InternLM-Xcomposer2-4KHD	2.0	20.6	23.2	21.6	5.6	7.7	4.1	6.1	10.1	11.2
Llava-Next-34B	3.3	24.0	28.0	22.3	3.6	6.1	2.6	0.4	9.8	11.1
TextMonkey	2.0	18.1	19.9	22.1	4.6	7.2	3.2	0.9	11.1	9.9
MiniCPM-V-2	1.3	12.7	14.9	17.0	3.7	5.6	2.2	2.2	6.8	7.4
mPLUG-DocOwl 1.5	1.0	13.9	14.9	18.2	2.9	5.0	2.0	0.9	6.4	7.2
YI-VL-34B	1.7	13.5	15.7	12.1	4.8	5.2	0.8	3.5	4.1	6.8
DeepSeek-VL	0.6	14.2	15.3	15.2	2.9	3.8	1.6	0.9	5.2	6.6

Name	Explanation	Example models
xP3x	Mixture of 17 tasks in 277 languages with English prompts	WIP - Join us at Project Aya @C4AI to help!
xP3	Mixture of 13 training tasks in 46 languages with English prompts	bloomz & mt0-xxl
xP3mt	Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)	bloomz-mt & mt0-xxl-mt
xP3all	xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts
xP3megds	Megatron-DeepSpeed processed version of xP3	bloomz
P3	Repreprocessed version of the English-only P3 with 8 training tasks	bloomz-p3 & mt0-xxl-p3

Category	MT-Bench	KoMT-Bench
Writing
1st Turn	Imagine you are writing a blog post comparing two popular smartphone models. Develop an outline for the blog post, including key points and subheadings to effectively compare and contrast the features, performance, and user experience of the two models. Please answer in fewer than 200 words.	두 개의 인기 스마트폰 모델을 비교하는 블로그 게시물을 작성한다고 가정합니다. 두 모델의 기능, 성능, 사용자 경험을 효과적으로 비교하고 대조할 수 있도록 핵심 사항과 소제목을 포함하여 블로그 게시물의 개요를 작성하세요. 200자 이내로 답하십시오.
2nd Turn	Take your previous response and rephrase it as a limerick.	이전 답변을 충청도 사투리로 재작성하십시오.
Math
1st Turn	When a number is divided by 10, the remainder is 4. What is the remainder when twice the number is divided by 4?	어떤 숫자를 10으로 나눈 나머지는 4입니다. 그 숫자의 두 배를 4로 나눈 나머지를 구하세요.
2nd Turn	What about when twice the number is divided by 5?	그 숫자의 두 배를 5로 나누면 어떨까요?
Humanities
1st Turn	Provide insights into the correlation between economic indicators such as GDP, inflation, and unemployment rates. Explain how fiscal and monetary policies affect those indicators.	GDP, 인플레이션, 실업률과 같은 경제 지표 간의 상관관계에 대한 통찰을 제시하세요. 이러한 지표들에 재정 및 통화 정책이 어떤 영향을 미치는지 설명하세요.
2nd Turn	Now, explain them again like I'm five.	이제 제가 5살이라 생각하고 다시 설명해 주세요.

CulturaX

Cleaned, Enormous, and Public: The Multilingual Fuel to Democratize Large Language Models for 167 Languages

CLIcK 🇰🇷🧠

🐋 The OpenOrca Dataset! 🐋

🐋 The OpenOrca Dataset! 🐋

HALvest

Open Scientific Papers Harvested from HAL (Unfiltered)

🐋 The OpenOrca Dataset! 🐋

🐋 The OpenOrca Dataset! 🐋

🐋 The OpenOrca Dataset! 🐋

CLIcK 🇰🇷🧠

Citation information:

🐋 The OpenOrca Dataset! 🐋