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# named-entity-recognition-distilbert-A

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the Multinerd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0606
- Precision: 0.8940
- Recall: 0.9027
- F1: 0.8983
- Accuracy: 0.9833

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.032         | 1.0   | 8205  | 0.0496          | 0.8843    | 0.8928 | 0.8885 | 0.9825   |
| 0.019         | 2.0   | 16410 | 0.0540          | 0.9046    | 0.8909 | 0.8977 | 0.9835   |
| 0.0121        | 3.0   | 24615 | 0.0606          | 0.8940    | 0.9027 | 0.8983 | 0.9833   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

### Citation 
### Bibtex
```
@software{Ali_Raza,
    author = {Raza, Ali},
    license = { BSD-2-Clause license},
    title = {{Named Entity Recognition using Multinerd}},
    url = {https://github.com/raza4729/NER}
}
```

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "named-entity-recognition-distilbert-A", "results": []}]}
transformers safetensors distilbert token-classification generated_from_trainer base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us