| --- |
| license: mit |
| task_categories: |
| - token-classification |
| language: |
| - en |
| tags: |
| - ner |
| - pharmaceutical |
| - ocr |
| - drug-names |
| - benchmark |
| - medical |
| size_categories: |
| - 100<n<1K |
| citation: | |
| @software{perekrestova_pillchecker_2026, |
| author = {Perekrestova, Svetlana}, |
| title = {PillChecker API: Pharmaceutical Entity Extraction and Interaction Checker}, |
| version = {1.2.2}, |
| doi = {10.5281/zenodo.19792062}, |
| url = {https://github.com/SPerekrestova/pillchecker-api}, |
| date = {2026-04-26}, |
| publisher = {Zenodo}, |
| note = {GitHub Repository} |
| } |
| --- |
| |
| # PillChecker NER Benchmark |
|
|
| Benchmark dataset for evaluating Named Entity Recognition (NER) models on pharmaceutical packaging text. |
|
|
| ## Dataset Description |
|
|
| **500 synthesized pack-label texts** generated from the [MattBastar/Medicine_Details](https://huggingface.co/datasets/MattBastar/Medicine_Details) dataset, designed to simulate OCR output from photos of pill packaging. |
|
|
| Each case contains: |
| - `id`: Unique case identifier |
| - `category`: `single_ingredient`, `dual_ingredient`, or `multi_ingredient` |
| - `ocr_text`: Synthesized pharmaceutical label text (clean or with OCR noise) |
| - `expected_names`: Ground-truth list of active pharmaceutical ingredients |
| - `source_composition`: Original composition string from source dataset |
|
|
| ## Use Case |
|
|
| This dataset tests whether NER models can extract **active pharmaceutical ingredients** from short, formulaic packaging text — a domain significantly different from biomedical literature. |
|
|
| ## Benchmark Rules |
|
|
| This dataset is the canonical location for benchmark input cases only. Benchmark result history belongs in `hf://buckets/SPerva/pillchecker-experiments/benchmark-results/`, not in this dataset repository. |
|
|
| Current records contain `id`, `category`, `ocr_text`, `expected_names`, and `source_composition`. Before using this dataset for entity-linking or interaction claims, add `expected_rxcuis`, `clean_text`, `expected_interactions`, and known-safe pairs as described in the GitHub repo's `AGENTS.md` and `eval/README.md`. |
|
|
| ## Baseline Results |
|
|
| Historical OpenMed baseline results are stored in the PillChecker experiments bucket. Project-facing result claims should cite a bucket run manifest and Git commit. |
|
|
| ## Source |
|
|
| Part of the [PillChecker](https://github.com/SPerekrestova/pillchecker-api) project. |
|
|
