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--- |
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language: |
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- en |
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tags: |
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- Named Entity Recognition |
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- SciBERT |
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- Adverse Effect |
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- Drug |
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- Medical |
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datasets: |
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- ade_corpus_v2 |
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- tner/bc5cdr |
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- commanderstrife/jnlpba |
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- bc2gm_corpus |
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- drAbreu/bc4chemd_ner |
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- linnaeus |
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- chintagunta85/ncbi_disease |
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widget: |
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- text: "Abortion, miscarriage or uterine hemorrhage associated with misoprostol (Cytotec), a labor-inducing drug." |
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example_title: "Abortion, miscarriage, ..." |
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- text: "Addiction to many sedatives and analgesics, such as diazepam, morphine, etc." |
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example_title: "Addiction to many..." |
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- text: "Birth defects associated with thalidomide" |
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example_title: "Birth defects associated..." |
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- text: "Bleeding of the intestine associated with aspirin therapy" |
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example_title: "Bleeding of the intestine..." |
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- text: "Cardiovascular disease associated with COX-2 inhibitors (i.e. Vioxx)" |
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example_title: "Cardiovascular disease..." |
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--- |
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This is a SciBERT-based model fine-tuned to perform Named Entity Recognition for drug names and adverse drug effects. |
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This model classifies input tokens into one of five classes: |
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- `B-DRUG`: beginning of a drug entity |
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- `I-DRUG`: within a drug entity |
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- `B-EFFECT`: beginning of an AE entity |
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- `I-EFFECT`: within an AE entity |
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- `O`: outside either of the above entities |
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To get started using this model for inference, simply set up an NER `pipeline` like below: |
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```python |
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from transformers import (AutoModelForTokenClassification, |
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AutoTokenizer, |
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pipeline, |
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) |
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model_checkpoint = "jsylee/scibert_scivocab_uncased-finetuned-ner" |
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model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, num_labels=5, |
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id2label={0: 'O', 1: 'B-DRUG', 2: 'I-DRUG', 3: 'B-EFFECT', 4: 'I-EFFECT'} |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) |
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model_pipeline = pipeline(task="ner", model=model, tokenizer=tokenizer) |
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print( model_pipeline ("Abortion, miscarriage or uterine hemorrhage associated with misoprostol (Cytotec), a labor-inducing drug.")) |
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``` |
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SciBERT: https://huggingface.co/allenai/scibert_scivocab_uncased |
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Dataset: https://huggingface.co/datasets/ade_corpus_v2 |
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