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# distilbert-base-uncased-finetuned-ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0664
- Precision: 0.9323
- Recall: 0.9432
- F1: 0.9377
- Accuracy: 0.9849

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2412        | 1.0   | 878  | 0.0726          | 0.9078    | 0.9237 | 0.9157 | 0.9800   |
| 0.0497        | 2.0   | 1756 | 0.0594          | 0.9266    | 0.9353 | 0.9310 | 0.9835   |
| 0.0286        | 3.0   | 2634 | 0.0609          | 0.9252    | 0.9408 | 0.9329 | 0.9842   |
| 0.0172        | 4.0   | 3512 | 0.0646          | 0.9298    | 0.9397 | 0.9347 | 0.9846   |
| 0.0126        | 5.0   | 4390 | 0.0664          | 0.9323    | 0.9432 | 0.9377 | 0.9849   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1

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