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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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widget: |
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- text: Drug<SEP>He was given aspirin and paracetamol. |
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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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```python |
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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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## Available classes |
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The following datasets and entities were used for training and therefore they can be used as label in the first segment (as a first string). Note that multiword string have been merged. |
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* NCBI |
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* Specific Disease |
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* Composite Mention |
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* Modifier |
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* Disease Class |
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* BIORED |
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* Sequence Variant |
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* Gene Or Gene Product |
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* Disease Or Phenotypic Feature |
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* Chemical Entity |
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* Cell Line |
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* Organism Taxon |
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* CDR Disease |
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* Chemical |
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* CHEMDNER |
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* Chemical |
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* Chemical Family |
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* JNLPBA |
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* Protein |
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* DNA |
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* Cell Type |
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* Cell Line |
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* RNA |
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* n2c2 |
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* Drug |
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* Frequency |
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* Strength |
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* Dosage |
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* Form |
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* Reason |
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* Route |
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* ADE |
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* Duration |
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On top of this, one can use the model in zero-shot regime with other classes, and also fine-tune it with few examples of other classes. |
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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 |
