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# ner-distilbert

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0003
- Precision: 0.9988
- Recall: 0.9980
- F1: 0.9984
- Accuracy: 0.9998

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0002        | 0.16  | 250  | 0.0011          | 0.9961    | 0.9980 | 0.9971 | 0.9996   |
| 0.0001        | 0.31  | 500  | 0.0008          | 0.9977    | 0.9977 | 0.9977 | 0.9997   |
| 0.0004        | 0.47  | 750  | 0.0005          | 0.9992    | 0.9977 | 0.9984 | 0.9998   |
| 0.0002        | 0.63  | 1000 | 0.0005          | 0.9984    | 0.9977 | 0.9980 | 0.9997   |
| 0.0002        | 0.79  | 1250 | 0.0003          | 0.9988    | 0.9980 | 0.9984 | 0.9998   |
| 0.0           | 0.94  | 1500 | 0.0003          | 0.9988    | 0.9980 | 0.9984 | 0.9998   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-cased", "model-index": [{"name": "ner-distilbert", "results": []}]}
transformers pytorch safetensors distilbert token-classification generated_from_trainer base_model:distilbert-base-cased autotrain_compatible endpoints_compatible region:us