Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- ade-benchmark-corpus/ade_corpus_v2
+language:
+- en
+base_model:
+- dmis-lab/biobert-base-cased-v1.2
+pipeline_tag: text-classification
+tags:
+- biomedical
+- nlp
+- adverse-drug-effects
+- bert
+- biobert
+---
+
+# BioBERT for Adverse Drug Effect (ADE) Classification
+
+This model is a fine-tuned version of [`dmis-lab/biobert-base-cased-v1.2`](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) for binary sentence classification: Does a sentence describe an **adverse drug effect (ADE)**? 
+It was fine-tuned on the [ADE Corpus V2](https://huggingface.co/datasets/ade-benchmark-corpus/ade_corpus_v2) dataset and compared against a classical TF-IDF + Logistic Regression baseline as part of a broader project benchmarking classical vs. transformer approaches on imbalanced biomedical text.
+
+**Project Repo:** [GitHub](https://github.com/steven-cheun/nlp-ade-classification)
+
+## Results (Test Set: N=3,528)
+
+| Model | Weighted F1 | ADE Class F1 | Accuracy | Total Errors |
+|---|---|---|---|---|
+| TF-IDF + Logistic Regression | 0.90 | 0.84 | 90% | 349 |
+| **BioBERT (this model)** | **0.96** | **0.93** | **96%** | **145** |
+
+BioBERT reduced misclassifications by **58%** (349 → 145 errors) compared to the classical baseline.
+
+## Training Details
+
+- **Base model:** `dmis-lab/biobert-base-cased-v1.2` (110M parameters)
+- **Epochs:** 3 (Best checckpoint selected by validation F1)
+- **Learning rate:** 2e-5
+- **Batch size:** 16
+- **Max sequence length:** 128
+- **Precision:** fp16
+- **Data split:** stratified 70/15/15 train/val/test (seed=42)
+
+| Epoch | Train Loss | Val F1 | Val Accuracy |
+|---|---|---|---|
+| 1 | 0.175 | 0.943 | 0.943 |
+| 2 | 0.114 | 0.952 | 0.952 |
+| 3 | 0.043 | 0.952 | 0.952 |
+
+## Usage
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("scheun/biobert-ade-classifier")
+tokenizer = AutoTokenizer.from_pretrained("scheun/biobert-ade-classifier")
+
+inputs = tokenizer("Patient developed severe nausea after taking the medication.", return_tensors="pt")
+outputs = model(**inputs)
+prediction = outputs.logits.argmax(-1).item()
+print(prediction)  # 0 = not ADE, 1 = ADE
+```
+
+## Limitations
+
+- Trained on MEDLINE case report sentences. Performance may vary on other text domains.
+- Binary classification only. It does not extract which drug or which effect is mentioned.
+
+## References
+
+- Gurulingappa et al. (2012), *Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports*
+- Lee et al. (2020), *BioBERT: a pre-trained biomedical language representation model for biomedical text mining*