transformers safetensors distilbert text-classification named-entity-recognition en dataset:conll2003 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
datasets:
- conll2003
language: en
license: apache-2.0
metrics:
- f1
- accuracy
- precision
- recall
tags:
- named-entity-recognition
- distilbert
- transformers
model_type: distilbert
---

# 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:

```python
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}")
