Update README.md

README.md

@@ -8,96 +8,226 @@ size_categories:
 tasks:
 - named-entity-recognition
 - token-classification
+pretty_name: BotanicalNER
+tags:
+- named-entity-recognition
+- botany
+- multilingual
+configs:
+- config_name: botlit_de
+  data_files:
+  - split: train
+    path: botlit_de/*
+  default: true
+- config_name: botlit_en
+  data_files:
+  - split: train
+    path: botlit_en/*
+- config_name: plantblog_de
+  data_files:
+  - split: train
+    path: plantblog_de/*
+- config_name: plantblog_en
+  data_files:
+  - split: train
+    path: plantblog_en/*
+- config_name: textberg_de
+  data_files:
+  - split: train
+    path: textberg_de/*
+- config_name: textberg_en
+  data_files:
+  - split: train
+    path: textberg_en/*
+- config_name: wiki_de
+  data_files:
+  - split: train
+    path: wiki_de/*
+- config_name: wiki_en
+  data_files:
+  - split: train
+    path: wiki_en/*
+- config_name: gold_de
+  data_files:
+  - split: test
+    path: gold_de/*
+- config_name: gold_en
+  data_files:
+  - split: test
+    path: gold_en/*
+- config_name: fungi_de
+  data_files:
+  - split: test
+    path: fungi_de/*
+- config_name: fungi_en
+  data_files:
+  - split: test
+    path: fungi_en/*
+dataset_info:
+- config_name: botlit_de
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: pos_tags
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-Scientific
+          '2': I-Scientific
+          '3': B-Vernacular
+          '4': I-Vernacular
+  splits:
+  - name: train
+    num_bytes: 100000 # Placeholder
+    num_examples: 1000 # Placeholder
+# Note: The Hub will auto-populate dataset_info for all other configs.
 ---
 
 # Dataset Card for BotanicalNER
 
+## Table of Contents
+- [Dataset Card for BotanicalNER](#dataset-card-for-botanicalner)
+- [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)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/IsabelMeraner/BotanicalNER](https://github.com/IsabelMeraner/BotanicalNER)
+- **Paper:** Meraner, I. 2019. *Grasping the Nettle: Neural Entity Recognition for Scientific and Vernacular Plant Names*. Master Thesis, Institute of Computational Linguistics, University of Zurich.
+- **Point of Contact:** Isabel Meraner
+
 ### Dataset Summary
-BotanicalNER is a Named Entity Recognition dataset for scientific and vernacular plant names in German and English. The dataset was created for a master thesis project at the University of Zurich focusing on identifying and disambiguating plant names across multiple text genres to extract and preserve (ethno-)botanical knowledge.
 
-The dataset contains text with plant name entities marked in IOB format, enabling the training of NER models to recognize both scientific names (e.g., "Urtica dioica") and vernacular plant names (e.g., "nettle") in natural text.
+**BotanicalNER** is a German-English parallel dataset for Named Entity Recognition (NER) of scientific and vernacular plant names. The resources were created for the master thesis project "Grasping the Nettle" at the University of Zurich in 2019.
 
-For a full description of the dataset, please refer to the master thesis: Meraner, I. 2019. "Grasping the Nettle: Neural Entity Recognition for Scientific and Vernacular Plant Names." Master Thesis, Institute of Computational Linguistics, University of Zurich.
+The main focus of the project was to identify and disambiguate scientific and vernacular plant names across multiple text genres to provide a valuable tool for extracting and preserving (ethno-)botanical knowledge. The dataset is structured into several sub-corpora from different domains, which are available as separate configurations:
+
+-   `botlit`: Botanical literature
+-   `plantblog`: Plant-themed blog posts
+-   `wiki`: Wikipedia abstracts
+-   `textberg`: The TextBerg corpus of Alpine Club yearbooks
+-   `gold`: Gold-standard test sets
+-   `fungi`: A specialized test set for fungi names
 
 ### Supported Tasks and Leaderboards
-- **Named Entity Recognition**: The dataset is designed for training and evaluating NER models for plant name recognition.
-- **Botanical Knowledge Extraction**: Can be used as a resource for systems aiming to extract botanical knowledge from text.
+
+-   **Tasks**: Named Entity Recognition, Token Classification
+-   **Leaderboards**: N/A
 
 ### Languages
-The dataset supports two languages:
-- German (`de`)
-- English (`en`)
+
+The dataset contains texts in German (`de`) and English (`en`).
 
 ## Dataset Structure
 
+The dataset is composed of multiple configurations, one for each sub-corpus and language (e.g., `botlit_de`, `wiki_en`, `gold_de`).
+
 ### Data Instances
-An example from the German dataset:
+
+An example from the `gold_de` configuration:
+
 ```json
 {
   "id": "0",
   "tokens": ["Die", "Brennnessel", "(", "Urtica", "dioica", ")", "ist", "eine", "wichtige", "Heilpflanze", "."],
   "pos_tags": ["ART", "NN", "$(", "NE", "NE", "$)", "VAFIN", "ART", "ADJA", "NN", "$."],
-  "ner_tags": ["O", "B-Vernacular", "O", "B-Scientific", "I-Scientific", "O", "O", "O", "O", "O", "O"]
+  "ner_tags": [0, 3, 0, 1, 2, 0, 0, 0, 0, 0, 0]
 }
 ```
 
 ### Data Fields
-- `id`: Unique identifier for each sentence, a `string` feature.
-- `tokens`: The tokens/words in the sentence, a sequence of `string`s.
-- `pos_tags`: Part-of-speech tags for each token, a sequence of `string`s.
-- `ner_tags`: Named entity recognition tags in IOB format, a sequence of `ClassLabel` features with the following classes:
-  - `O`: Outside any entity
-  - `B-Scientific`: Beginning of a scientific plant name
-  - `I-Scientific`: Inside a scientific plant name
-  - `B-Vernacular`: Beginning of a vernacular plant name
-  - `I-Vernacular`: Inside a vernacular plant name
 
-### Data Splits
-The dataset includes multiple splits for both German and English. The training data is a silver-standard corpus, while the test and fungi splits are gold-standard.
+All configurations share the same data fields:
+-   `id`: A unique identifier for the example, a `string` feature.
+-   `tokens`: The list of tokens in the sentence, a `Sequence` of `string` features.
+-   `pos_tags`: The list of part-of-speech tags, a `Sequence` of `string` features.
+-   `ner_tags`: The list of NER tags, a `Sequence` of `ClassLabel` features. The mapping from ID to tag is as follows:
 
-| Split | German Files | English Files | Description                                        |
-|-------|--------------|---------------|----------------------------------------------------|
-| train | 4            | 4             | Silver standard training data from multiple sources|
-| test  | 1            | 1             | Gold standard test fold                            |
-| fungi | 1            | 1             | Gold standard fungi testset for in-domain evaluation|
+```json
+{
+    "0": "O",
+    "1": "B-Scientific",
+    "2": "I-Scientific",
+    "3": "B-Vernacular",
+    "4": "I-Vernacular"
+}
+```
+
+### Data Splits
 
-Training data sources include:
-- Plant blog corpus
-- Wikipedia abstract corpus
-- TextBerg subcorpus
-- Botanical literature corpus
+The data is provided as several distinct corpora, loaded via configurations.
+-   The `botlit`, `plantblog`, `textberg`, and `wiki` configurations each contain a **train** split composed of silver-standard data.
+-   The `gold` and `fungi` configurations each contain a **test** split composed of gold-standard data.
 
 ## Dataset Creation
-The dataset was created to address the need for robust plant name recognition across different text genres. The training data was collected from multiple sources, tokenized, and POS-tagged using TreeTagger. The training data was automatically annotated using dictionary-based methods (silver standard), while the test data was manually annotated and verified (gold standard).
+
+### Curation Rationale
+
+The project aimed to create a robust tool for extracting plant names from diverse texts, which is a crucial step for digitizing and preserving botanical and ethno-botanical knowledge.
+
+### Source Data
+
+The data was collected from various sources, tokenized, and POS-tagged using TreeTagger.
+
+### Annotations
+
+The dataset was annotated using a dictionary-based method (silver standard) and manual correction (gold standard). The annotation scheme is IOB (Inside, Outside, Beginning) for two entity types: `Scientific` and `Vernacular` plant names.
 
 ### Personal and Sensitive Information
+
 The dataset does not contain personal or sensitive information as it focuses on botanical and scientific content.
 
 ## Considerations for Using the Data
+
 ### Social Impact of Dataset
-This dataset can have a positive social impact by:
-- Enabling better extraction and preservation of botanical knowledge.
-- Supporting biodiversity research and conservation efforts.
-- Assisting in ethnobotanical knowledge preservation.
+
+This dataset can have a positive social impact by enabling better extraction of botanical knowledge, supporting biodiversity research, and assisting in the preservation of ethnobotanical knowledge.
 
 ### Discussion of Biases
+
 Potential biases include geographic bias in plant names, source bias from different text genres, and domain bias from the specific sources used. Most training data also uses automatic annotation which may contain errors.
 
 ## Additional Information
 
 ### Dataset Curators
-Isabel Meraner, Institute of Computational Linguistics, University of Zurich (Switzerland), 2019.
+
+Isabel Meraner, Institute of Computational Linguistics, University of Zurich (2019).
 
 ### Licensing Information
-The dataset is licensed under the GNU General Public License v3.0.
+
+The dataset is licensed under the GNU General Public License v3.0 (GPL-3.0).
 
 ### Citation Information
-```
+
+```bibtex
 @mastersthesis{meraner2019grasping,
     title={Grasping the Nettle: Neural Entity Recognition for Scientific and Vernacular Plant Names},
-    author={Isabel Meraner},
+    author={Meraner, Isabel},
     year={2019},
-    school={Institute of Computational Linguistics, University of Zurich},
-    note={Available at: [https://github.com/IsabelMeraner/BotanicalNER](https://github.com/IsabelMeraner/BotanicalNER)}
+    school={Institute of Computational Linguistics, University of Zurich}
 }
 ```