Beehzod/smart-finetuned-ner

Fine-tuned DistilBERT for Named Entity Recognition (NER)

Model Description

This model is a fine-tuned version of DistilBERT for Named Entity Recognition (NER) tasks. It was trained on the CoNLL-2003 dataset, designed to identify entities such as persons, organizations, locations, and miscellaneous entities within English text.

• Model Architecture: DistilBERT (pre-trained transformer-based model)
• Task: Named Entity Recognition (NER)
• Entity Types: PER (Person), ORG (Organization), LOC (Location), MISC (Miscellaneous)

Training Details

• Dataset: CoNLL-2003 (standard dataset for NER tasks)
• Training Data Size: 14,000 samples for training, 3,250 samples for evaluation
• Epochs: 3
• Batch Size: 16 (training), 64 (evaluation)
• Learning Rate: 2e-5
• Optimizer: AdamW with weight decay

Evaluation Metrics

The model was evaluated using the following metrics:

• F1 Score: 0.928661
• Accuracy: 0.983252
• Precision: 0.918794
• Recall: 0.938741

Example Usage

Here’s how to use this NER model with the Hugging Face Transformers library:

from transformers import pipeline

# Load the model from the Hugging Face Hub
ner_pipeline = pipeline("ner", model="Beehzod/smart-finetuned-ner")

# Example predictions
text = "Hugging Face Inc. is based in New York City, and its CEO is Clement Delangue."
results = ner_pipeline(text)

for entity in results:
    print(f"Entity: {entity['word']}, Label: {entity['entity']}, Score: {entity['score']:.4f}")