bert-finetuned-ner-accelerate

Fine-Tuned BERT Model for Named Entity Recognition (NER) with Accelerate Library

This repository contains a fine-tuned BERT model for Named Entity Recognition (NER) tasks, trained on the CoNLL 2003 dataset using the Hugging Face Accelerate library.

The dataset includes the following labels:

• O, B-PER, I-PER, B-ORG, I-ORG, B-LOC, I-LOC, B-MISC, I-MISC

Model Training Details

Training Arguments

• Library: Hugging Face Accelerate
• Model Architecture: bert-base-cased for token classification
• Learning Rate: 2e-5
• Number of Epochs: 20
• Weight Decay: 0.01
• Batch Size: 8
• Evaluation Strategy: epoch
• Save Strategy: epoch

Additional default parameters from the Accelerate and Transformers libraries were used.

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Evaluation Results

Validation Set Performance

• Overall Metrics:
  • Precision: 95.17%
  • Recall: 93.87%
  • F1 Score: 94.52%
  • Accuracy: 98.62%

Per-Label Performance

Test Set Performance

• Overall Metrics:
  • Precision: 91.82%
  • Recall: 89.68%
  • F1 Score: 90.74%
  • Accuracy: 97.23%

Per-Label Performance

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How to Use the Model

You can load the model directly from the Hugging Face Model Hub:

from transformers import pipeline

# Replace with your specific model checkpoint
model_checkpoint = "Prikshit7766/bert-finetuned-ner-accelerate"
token_classifier = pipeline(
    "token-classification", 
    model=model_checkpoint, 
    aggregation_strategy="simple"
)

# Example usage
result = token_classifier("My name is Sylvain and I work at Hugging Face in Brooklyn.")
print(result)

Example Output

[
   {
      "entity_group": "PER",
      "score": 0.9999658,
      "word": "Sylvain",
      "start": 11,
      "end": 18
   },
   {
      "entity_group": "ORG",
      "score": 0.99996203,
      "word": "Hugging Face",
      "start": 33,
      "end": 45
   },
   {
      "entity_group": "LOC",
      "score": 0.9999542,
      "word": "Brooklyn",
      "start": 49,
      "end": 57
   }
]

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