DistilBERT for Named Entity Recognition

This repository contains a fine-tuned DistilBERT model trained on the CoNLL-2003 dataset for Named Entity Recognition (NER). The model identifies and classifies named entities in English text.

Model Details

• Base Model: distilbert-base-uncased
• Task: Named Entity Recognition (NER)
• Dataset: CoNLL-2003
• Framework: PyTorch and Hugging Face Transformers

The model recognizes four entity categories:

• PER: Person
• ORG: Organization
• LOC: Location
• MISC: Miscellaneous entities

It uses the BIO tagging scheme to identify entity boundaries.

How to Use

from transformers import pipeline

ner_pipeline = pipeline(
    "ner",
    model="mohammad748/distilbert-conll2003-ner",
    aggregation_strategy="simple"
)

text = "Barack Obama was born in Hawaii."

results = ner_pipeline(text)

for entity in results:
    print(entity)

The output includes the identified entities, their categories, confidence scores, and positions in the text.