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--- |
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dataset_info: |
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features: |
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- name: ID |
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dtype: int32 |
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- name: Task Name |
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dtype: string |
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- name: PDF |
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dtype: |
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pdf: |
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decode: false |
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- name: Language |
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dtype: string |
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- name: Task |
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dtype: string |
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- name: Content |
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dtype: string |
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- name: TXT Filename |
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dtype: string |
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- name: PDF Filename |
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dtype: string |
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splits: |
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- name: parallel |
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num_bytes: 6844760.0 |
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num_examples: 96 |
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- name: not_parallel |
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num_bytes: 2359134.0 |
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num_examples: 47 |
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download_size: 8100216 |
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dataset_size: 9203894.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: parallel |
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path: data/parallel-* |
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- split: not_parallel |
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path: data/not_parallel-* |
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--- |
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# Dataset Card: PISA Multimodal (Parallel & Not-Parallel) |
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## Summary |
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This dataset contains **48 parallel multimodal samples** (paired TXT↔PDF) derived from **PISA studies up to 2012**, plus **47 non-parallel samples** (TXT-only or PDF-only). Each sample may include multiple questions. Content is available in **German** and **English**. |
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**Source & usage:** Materials are published by the **OECD** and are provided here **for non-commercial use** only. Please verify that your usage complies with OECD terms. |
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**Quality note:** Some of the **parallel** samples are **noisy** (e.g., OCR artifacts or imperfect alignments). These are **explicitly labeled in the filename** so they can be filtered out during experiments. |
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## Organization & Splits |
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- **`train`** — *parallel* split: 48 samples (TXT and matching PDF). |
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- **`not_parallel`** — 47 samples where only TXT **or** only PDF is present. |
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*Note:* In this split, one of `Content` or `PDF` can be `null`. |
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*(Counts reflect the provided corpus; they may change if you add/remove files.)* |
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## Languages |
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- **German:** `deu_Latn` |
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- **English:** `eng_Latn` |
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## Tasks |
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- **Reading** comprehension |
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- **Math** problem solving |
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## Data Fields (Columns) |
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- **`ID`** *(int32)* — Running index of the sample (per build script). |
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- **`Task Name`** *(string)* — Base name of the material (filename stem before the last underscore), e.g., `Bookshelves`. |
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- **`PDF`** *(Pdf feature)* — Full PDF object (stored via `datasets.Pdf`). May be `null` in `not_parallel` if missing. |
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- **`Language`** *(string)* — Language code of the folder, e.g., `deu_Latn` or `eng_Latn`. |
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- **`Task`** *(string)* — Task category from the folder name: `reading` or `math`. |
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- **`Content`** *(string)* — Full text from the paired `.txt`. May be `null` in `not_parallel` if missing. |
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- **`TXT Filename`** *(string)* — Basename of the associated `.txt` file (if present). |
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- **`PDF Filename`** *(string)* — Basename of the associated `.pdf` file (if present). |
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## Source Data & Provenance |
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- **Origin:** Items compiled from **OECD PISA** materials (≤2012). |
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- **Derivation:** Parallel pairs were formed by matching TXT and PDF stems; the `not_parallel` split contains items missing one counterpart. |
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## Licensing & Usage |
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- **License:** *Other / Non-commercial.* The content is published by the **OECD**. Redistribution and use should comply with **OECD non-commercial terms**. This card is not legal advice—please review the official terms for your use case. |
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## How to Load |
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```python |
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from datasets import load_dataset |
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# From the Hub (replace with your repo id) |
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ds = load_dataset("your-username/your-dataset") |
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# Parallel split |
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train = ds["train"] |
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# Not-parallel split |
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not_parallel = ds["not_parallel"] |
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# Example access |
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row = train[0] |
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print(row["Task Name"], row["Language"], row["Task"]) |
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# PDF is a Pdf feature record with a path/bytes: |
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print(row["PDF"]) |
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``` |
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## Intended Uses & Limitations |
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- **Intended:** research on multimodal QA, OCR+NLP, cross-lingual evaluation, dataset tooling. |
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- **Not intended:** commercial exploitation of OECD content; deployment without verifying rights. |
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- **Limitations:** Some samples include multiple questions per PDF/TXT; `not_parallel` items are incomplete by design; some parallel items are flagged as **noisy** in filenames. |
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## Citation |
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If you use this dataset, please cite the OECD PISA materials appropriately and reference this dataset card. |
