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---
license: mit
datasets:
- bigbio/chemdner
- ncbi_disease
- jnlpba
- bigbio/n2c2_2018_track2
- bigbio/bc5cdr
language:
- en
metrics:
- precision
- recall
- f1
pipeline_tag: token-classification
tags:
- token-classification
- biology
- medical
- zero-shot
- few-shot
library_name: transformers
---
# Zero and few shot NER for biomedical texts
## Model description
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.
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.
## Example of usage
```
from transformers import AutoTokenizer
from transformers import BertForTokenClassification
modelname = 'ProdicusII/ZeroShotBioNER' # modelpath
tokenizer = AutoTokenizer.from_pretrained(modelname) ## loading the tokenizer of that model
string1 = 'Drug'
string2 = 'No recent antibiotics or other nephrotoxins, and no symptoms of UTI with benign UA.'
encodings = tokenizer(string1, string2, is_split_into_words=False,
padding=True, truncation=True, add_special_tokens=True, return_offsets_mapping=False,
max_length=512, return_tensors='pt')
model = BertForTokenClassification.from_pretrained(modelname, num_labels=2)
prediction_logits = model(**encodings)
print(prediction_logits)
```
## Code availibility
Code used for training and testing the model is available at https://github.com/br-ai-ns-institute/Zero-ShotNER
## Citation |