---
datasets:
- ner_distillbert
language:
- en
library_name: pytorch
license: apache-2.0
tags:
- ner
- pytorch
- mlflow
task: token-classification
---

# ner_distillbert

## Model Description

This is a Named Entity Recognition (NER) model based on DistilBERT, a distilled version of BERT that retains 97% of BERT's performance while being 60% smaller and faster. The model identifies and classifies named entities in text such as persons, organizations, locations, and other predefined categories.

**Model Details:**
- **Model Name:** ner_distillbert
- **Version:** 1
- **Task:** Ner
- **Framework:** pytorch
- **Language(s):** en
- **License:** apache-2.0

## Intended Uses

This model is designed for ner tasks. Please evaluate on your specific use case before production deployment.

## Training Details

### Training Data
- **Dataset:** ner_distillbert
- **Dataset Size:** Not specified

### Training Configuration

## Usage

### Installation
```bash
pip install transformers torch  # For transformers models
# OR
pip install -r requirements.txt  # For other frameworks
```

### Basic Usage
```python
# Load and use the model
from inference import Model  # See inference.py in the repository

model = Model(openchs/ner_distillbert_v1)
predictions = model.predict([President Biden met with Chancellor Merkel at the White House to discuss NATO policies.])
print(predictions)
```

## Performance Metrics

### Evaluation Results
| Metric | Value |
|--------|-------|
| Epoch | 3.0000 |
| Eval Accuracy | 0.9608 |
| Eval F1 | 0.9433 |
| Eval Loss | 0.1171 |
| Eval Precision | 0.9324 |
| Eval Recall | 0.9608 |
| Eval Runtime | 0.1943 |
| Eval Samples Per Second | 82.3410 |
| Eval Steps Per Second | 10.2930 |

## MLflow Tracking

- **Experiment:** N/A
- **Run ID:** `N/A`
- **Training Date:** N/A

## Citation

```bibtex
@misc{ner_distillbert_1,
  title={ner_distillbert},
  author={BITZ-AI TEAM},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/marlonbino/ner_distillbert}
}
```