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--- |
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license: mit |
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datasets: |
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- bigbio/chemdner |
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- ncbi_disease |
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- jnlpba |
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- bigbio/n2c2_2018_track2 |
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- bigbio/bc5cdr |
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language: |
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- en |
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metrics: |
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- precision |
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- recall |
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- f1 |
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pipeline_tag: token-classification |
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tags: |
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- token-classification |
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- biology |
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- medical |
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- zero-shot |
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- few-shot |
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library_name: transformers |
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--- |
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# Zero and few shot NER for biomedical texts |
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## Model description |
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Model takes as input two strings. String1 is NER label. String1 must be phrase for entity. String2 is short text where String1 is searched for semantically. |
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model outputs list of zeros and ones corresponding to the occurance of Named Entity and corresponing to the tokens(tokens given by transformer tokenizer) of the Sring2. |
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## Example of usage |
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``` |
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from transformers import AutoTokenizer |
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from transformers import BertForTokenClassification |
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modelname = 'ProdicusII/ZeroShotBioNER' # modelpath |
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tokenizer = AutoTokenizer.from_pretrained(modelname) ## loading the tokenizer of that model |
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string1 = 'Drug' |
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string2 = 'No recent antibiotics or other nephrotoxins, and no symptoms of UTI with benign UA.' |
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encodings = tokenizer(string1, string2, is_split_into_words=False, |
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padding=True, truncation=True, add_special_tokens=True, return_offsets_mapping=False, |
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max_length=512, return_tensors='pt') |
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model = BertForTokenClassification.from_pretrained(modelname, num_labels=2) |
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prediction_logits = model(**encodings) |
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print(prediction_logits) |
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``` |
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## Code availibility |
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Code used for training and testing the model is available at https://github.com/br-ai-ns-institute/Zero-ShotNER |
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## Citation |
