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# cso_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: 0.0525
- Precision: 0.7858
- Recall: 0.7174
- F1: 0.7500
- Accuracy: 0.9824

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0914        | 1.0   | 5873  | 0.0822          | 0.7346    | 0.5135 | 0.6045 | 0.9730   |
| 0.069         | 2.0   | 11746 | 0.0653          | 0.7796    | 0.6010 | 0.6787 | 0.9781   |
| 0.057         | 3.0   | 17619 | 0.0594          | 0.7665    | 0.6702 | 0.7151 | 0.9797   |
| 0.0504        | 4.0   | 23492 | 0.0537          | 0.7936    | 0.6982 | 0.7429 | 0.9820   |
| 0.0455        | 5.0   | 29365 | 0.0525          | 0.7858    | 0.7174 | 0.7500 | 0.9824   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "cso_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