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README.md

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+---
+language:
+- en
+license: cc-by-4.0
+size_categories:
+- 1K<n<10K
+task_categories:
+- text-classification
+- token-classification
+tags:
+- biology
+- medical
+- biomedical
+- NLP
+- relation-extraction
+- drug-protein-interactions
+- BioCreative
+- PubMed
+- pharmacology
+- NER
+dataset_info:
+  features:
+  - name: pmid
+    dtype: string
+  - name: title
+    dtype: string
+  - name: abstract
+    dtype: string
+  - name: text
+    dtype: string
+  - name: entities
+    list:
+    - name: id
+      dtype: string
+    - name: type
+      dtype: string
+    - name: text
+      dtype: string
+    - name: start
+      dtype: int64
+    - name: end
+      dtype: int64
+  - name: relations
+    list:
+    - name: type
+      dtype: string
+    - name: arg1
+      dtype: string
+    - name: arg2
+      dtype: string
+  splits:
+  - name: train
+    num_examples: 3500
+  - name: validation
+    num_examples: 750
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train.parquet
+  - split: validation
+    path: data/validation.parquet
+---
+
+# DrugProt (Parquet)
+
+A clean, ready-to-use Parquet version of the **DrugProt** corpus from [BioCreative VII Track 1](https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/), converted for seamless use with the Hugging Face `datasets` library.
+
+DrugProt is a gold-standard corpus of **4,250 PubMed abstracts** annotated for **drug/chemical–protein interactions**, covering **13 fine-grained relation types** and **3 entity types**. It is designed for training and evaluating biomedical relation extraction systems.
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("OpenMed/drugprot-parquet")
+
+# Access splits
+train = dataset["train"]       # 3,500 abstracts
+val   = dataset["validation"]  # 750 abstracts
+
+# Inspect a sample
+example = train[0]
+print(example["title"])
+print(f"Entities: {len(example['entities'])}")
+print(f"Relations: {len(example['relations'])}")
+```
+
+## Dataset Description
+
+Each example represents a PubMed abstract with expert-annotated entity spans and relation labels:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `pmid` | `string` | PubMed article ID |
+| `title` | `string` | Article title |
+| `abstract` | `string` | Article abstract |
+| `text` | `string` | Full text (title + abstract) |
+| `entities` | `list[dict]` | Annotated entity spans |
+| `relations` | `list[dict]` | Annotated drug–protein relations |
+
+### Entity Schema
+
+Each entity contains:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | `string` | Unique entity ID (e.g., `T1`, `T2`) |
+| `type` | `string` | Entity type: `CHEMICAL`, `GENE-Y`, or `GENE-N` |
+| `text` | `string` | Surface text of the entity mention |
+| `start` | `int` | Character offset (start) in `text` field |
+| `end` | `int` | Character offset (end) in `text` field |
+
+**Entity types:**
+- **CHEMICAL** — Drugs, small molecules, metabolites, and other chemical compounds
+- **GENE-Y** — Gene/protein mentions that are a normalized, valid gene/protein
+- **GENE-N** — Gene/protein mentions that are NOT normalized (e.g., protein families, complexes)
+
+### Relation Schema
+
+Each relation contains:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `type` | `string` | One of 13 relation categories (see below) |
+| `arg1` | `string` | Entity ID of the first argument |
+| `arg2` | `string` | Entity ID of the second argument |
+
+## Relation Types (13 Classes)
+
+| Relation | Description | Train | Val |
+|----------|-------------|------:|----:|
+| `INHIBITOR` | Chemical inhibits the protein | 5,388 | 1,150 |
+| `DIRECT-REGULATOR` | Chemical directly regulates protein (mechanism unspecified) | 2,247 | 458 |
+| `SUBSTRATE` | Chemical is a substrate of the enzyme | 2,003 | 494 |
+| `ACTIVATOR` | Chemical activates the protein | 1,428 | 246 |
+| `INDIRECT-UPREGULATOR` | Chemical indirectly increases protein activity/expression | 1,378 | 302 |
+| `INDIRECT-DOWNREGULATOR` | Chemical indirectly decreases protein activity/expression | 1,329 | 332 |
+| `ANTAGONIST` | Chemical acts as antagonist of the receptor/protein | 972 | 218 |
+| `PRODUCT-OF` | Chemical is a product of the enzyme | 920 | 158 |
+| `PART-OF` | Chemical is part of the protein complex | 885 | 257 |
+| `AGONIST` | Chemical acts as agonist of the receptor/protein | 658 | 131 |
+| `AGONIST-ACTIVATOR` | Chemical is both agonist and activator | 29 | 10 |
+| `SUBSTRATE_PRODUCT-OF` | Chemical is both substrate and product | 24 | 3 |
+| `AGONIST-INHIBITOR` | Chemical is agonist but inhibits downstream effects | 13 | 2 |
+| **Total** | | **17,274** | **3,761** |
+
+## Dataset Statistics
+
+|  | Train | Validation | Total |
+|--|------:|-----------:|------:|
+| Abstracts | 3,500 | 750 | **4,250** |
+| Abstracts with relations | 2,433 | — | — |
+| Total entities | 89,529 | — | — |
+|  • CHEMICAL | 46,274 (51.7%) | — | — |
+|  • GENE-Y | 28,421 (31.7%) | — | — |
+|  • GENE-N | 14,834 (16.6%) | — | — |
+| Total relations | 17,274 | 3,761 | **21,035** |
+| Avg. entities / abstract | 25.6 | — | — |
+| Avg. relations / abstract | 4.9 | — | — |
+
+## Usage Examples
+
+### Relation Extraction
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("OpenMed/drugprot-parquet", split="train")
+
+for example in ds:
+    entities = {e["id"]: e for e in example["entities"]}
+    for rel in example["relations"]:
+        arg1 = entities[rel["arg1"]]
+        arg2 = entities[rel["arg2"]]
+        print(f"{arg1['text']} --[{rel['type']}]--> {arg2['text']}")
+```
+
+### Named Entity Recognition (NER)
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("OpenMed/drugprot-parquet", split="train")
+
+for example in ds:
+    text = example["text"]
+    for ent in example["entities"]:
+        span = text[ent["start"]:ent["end"]]
+        assert span == ent["text"], f"Offset mismatch: '{span}' != '{ent['text']}'"
+        print(f"[{ent['type']}] {ent['text']} ({ent['start']}:{ent['end']})")
+```
+
+### Convert to Token Classification Format
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("OpenMed/drugprot-parquet", split="train")
+
+# Build BIO tags from character offsets
+example = ds[0]
+text = example["text"]
+char_labels = ["O"] * len(text)
+for ent in sorted(example["entities"], key=lambda e: e["start"]):
+    tag = ent["type"]
+    char_labels[ent["start"]] = f"B-{tag}"
+    for i in range(ent["start"] + 1, ent["end"]):
+        char_labels[i] = f"I-{tag}"
+```
+
+## Source
+
+This dataset is a Parquet conversion of the [DrugProt BioCreative VII corpus](https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/). The original data was released under **CC BY 4.0** for the BioCreative VII shared task.
+
+**Original paper:**
+
+> M. Krallinger, O. Rabal, A. Lourenco, J. Oyarzabal, A. Valencia.  
+> *"Overview of the BioCreative VII Track 1 – DrugProt: Drug-Protein Relation Extraction."*  
+> Proceedings of the BioCreative VII Challenge Evaluation Workshop, 2021.
+
+**BibTeX:**
+
+```bibtex
+@inproceedings{drugprot2021,
+  title={Overview of the BioCreative VII Track 1 -- DrugProt: Drug-Protein Relation Extraction},
+  author={Krallinger, Martin and Rabal, Obdulia and Lourenco, Analia and Oyarzabal, Julen and Valencia, Alfonso},
+  booktitle={Proceedings of the BioCreative VII Challenge Evaluation Workshop},
+  year={2021}
+}
+```
+
+## License
+
+CC BY 4.0 — following the original DrugProt corpus license.
+
+## About OpenMed
+
+[OpenMed](https://huggingface.co/OpenMed) provides clean, standardized biomedical datasets and RL training environments for medical AI research.