---
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
---
# 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 NER and corresponing to tokens(tokens given by transformer tokenizer) of the Sring2, not to words.

## Example of usage

## Code availibility

Code used for training and testing the model is available at https://github.com/br-ai-ns-institute/Zero-ShotNER 

## Citation