Ner Disease Ncbi Bionlp Bc5cdr Pubmed

🚀 NER to find Gene & Gene products

This model is designed for named - entity recognition (NER) to identify gene and gene products. It was trained on specific datasets and can effectively classify relevant tokens.

🚀 Quick Start

The model was trained on the ncbi - disease and BC5CDR datasets and was pretrained on this [pubmed - pretrained roberta model](/raynardj/roberta - pubmed).

All the labels, the possible token classes:

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

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

✨ Features

• Trained on ncbi - disease and BC5CDR datasets.
• Pretrained on [pubmed - pretrained roberta model](/raynardj/roberta - pubmed).

💻 Usage Examples

Basic Usage

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")

Advanced Usage

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)

📚 Documentation

Check our NER model on:

• [gene and gene products](/raynardj/ner - gene - dna - rna - jnlpba - pubmed)
• [chemical substance](/raynardj/ner - chemical - bionlp - bc5cdr - pubmed)
• [disease](/raynardj/ner - disease - ncbi - bionlp - bc5cdr - pubmed)

📄 License

The model is licensed under the apache - 2.0 license.