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Prikshit7766/
bert-finetuned-ner

Name: bert-finetuned-ner
Availability: InStock

google-bert/bert-base-cased

finetune

autotrain_compatible

endpoints_compatible

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Dataset Used

This model was trained on the CoNLL 2003 dataset for Named Entity Recognition (NER) tasks.

The dataset includes the following labels:

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

For detailed descriptions of these labels, please refer to the dataset card.

Model Training Details

Training Arguments

Model Architecture: bert-base-cased for token classification
Learning Rate: 2e-5
Number of Epochs: 20
Weight Decay: 0.01
Evaluation Strategy: epoch
Save Strategy: epoch

Additional default parameters from the Hugging Face Transformers library were used.

Evaluation Results

Validation Set Performance

Overall Metrics:
- Precision: 94.44%
- Recall: 95.74%
- F1 Score: 95.09%
- Accuracy: 98.73%

Per-Label Performance

Entity Type	Precision	Recall	F1 Score
LOC	97.27%	97.11%	97.19%
MISC	87.46%	91.54%	89.45%
ORG	93.37%	93.44%	93.40%
PER	96.02%	98.15%	97.07%

Test Set Performance

Overall Metrics:
- Precision: 89.90%
- Recall: 91.91%
- F1 Score: 90.89%
- Accuracy: 97.27%

Per-Label Performance

Entity Type	Precision	Recall	F1 Score
LOC	92.87%	92.87%	92.87%
MISC	75.55%	82.76%	78.99%
ORG	88.32%	90.61%	89.45%
PER	95.28%	96.23%	95.75%

How to Use the Model

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

python

from transformers import pipeline

# Replace with your specific model checkpoint
model_checkpoint = "Prikshit7766/bert-finetuned-ner"
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

python

[
   {
      "entity_group":"PER",
      "score":0.9999881,
      "word":"Sylvain",
      "start":11,
      "end":18
   },
   {
      "entity_group":"ORG",
      "score":0.99961376,
      "word":"Hugging Face",
      "start":33,
      "end":45
   },
   {
      "entity_group":"LOC",
      "score":0.99989843,
      "word":"Brooklyn",
      "start":49,
      "end":57
   }
]