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aren-golazizian/
distilbert-ner-finetuned-conll2003

Name: distilbert-ner-finetuned-conll2003
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distilbert

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Fine-tuned DistilBERT for NER (CoNLL-2003)

This repository contains a fine-tuned DistilBERT model for Named Entity Recognition (NER) trained on the CoNLL-2003 dataset.

Model Details

Base Model: distilbert-base-uncased
Task: Named Entity Recognition (NER)
Dataset: CoNLL-2003
Labels:
- O: Outside of a named entity
- B-PER, I-PER: Person names
- B-ORG, I-ORG: Organizations
- B-LOC, I-LOC: Locations
- B-MISC, I-MISC: Miscellaneous entities

How to Use the Model

You can load and use the model with the transformers library:

python

from transformers import pipeline

# Load the fine-tuned model from Hugging Face Hub
ner_pipeline = pipeline("ner", model="aren-golazizian/distilbert-ner-finetuned-conll2003")

# Example text for NER
example_text = "Hugging Face Inc. is based in New York City. Its founder is Thomas Wolf."

# Perform Named Entity Recognition
results = ner_pipeline(example_text)

# Print results
for entity in results:
    print(entity)


[
    {"entity": "B-ORG", "score": 0.99, "index": 1, "word": "Hugging Face Inc.", "start": 0, "end": 18},
    {"entity": "B-LOC", "score": 0.98, "index": 2, "word": "New York City", "start": 31, "end": 44},
    {"entity": "B-PER", "score": 0.97, "index": 3, "word": "Thomas Wolf", "start": 60, "end": 71}
]