Edit model card

NER to find Gene & Gene products

The model was trained on ncbi-disease, BC5CDR dataset, pretrained on this pubmed-pretrained roberta model All the labels, the possible token classes.

{"label2id": {
    "O": 0,
    "Disease":1,
  }
 }

Notice, we removed the 'B-','I-' etc from data label.🗡

This is the template we suggest for using the model

from transformers import pipeline
PRETRAINED = "raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed"
ner = pipeline(task="ner",model=PRETRAINED, tokenizer=PRETRAINED)
ner("Your text", aggregation_strategy="first")

And here is to make your output more consecutive ⭐️

import pandas as pd
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(PRETRAINED)
def clean_output(outputs):
    results = []
    current = []
    last_idx = 0
    # make to sub group by position
    for output in outputs:
        if output["index"]-1==last_idx:
            current.append(output)
        else:
            results.append(current)
            current = [output, ]
        last_idx = output["index"]
    if len(current)>0:
        results.append(current)
    
    # from tokens to string
    strings = []
    for c in results:
        tokens = []
        starts = []
        ends = []
        for o in c:
            tokens.append(o['word'])
            starts.append(o['start'])
            ends.append(o['end'])
        new_str = tokenizer.convert_tokens_to_string(tokens)
        if new_str!='':
            strings.append(dict(
                word=new_str,
                start = min(starts),
                end = max(ends),
                entity = c[0]['entity']
            ))
    return strings
def entity_table(pipeline, **pipeline_kw):
    if "aggregation_strategy" not in pipeline_kw:
        pipeline_kw["aggregation_strategy"] = "first"
    def create_table(text):
        return pd.DataFrame(
            clean_output(
                pipeline(text, **pipeline_kw)
            )
        )
    return create_table
# will return a dataframe
entity_table(ner)(YOUR_VERY_CONTENTFUL_TEXT)

check our NER model on

Downloads last month
57
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.