File size: 1,491 Bytes
56c53bb 6fc7d66 56c53bb 6fc7d66 7edec27 6fc7d66 7edec27 6fc7d66 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
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 Named Entity and corresponing to the tokens(tokens given by transformer tokenizer) of the Sring2.
## Example of usage
from transformers import AutoTokenizer
modelname='./' #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')
from transformers import BertForTokenClassification #AutoModelForPreTraining
model = BertForTokenClassification.from_pretrained(modelname, num_labels=2)
prediction_logits=model(**encodings)
## Code availibility
Code used for training and testing the model is available at https://github.com/br-ai-ns-institute/Zero-ShotNER
## Citation |