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# bert-cadec-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3060
- Precision: 0.6206
- Recall: 0.6924
- F1: 0.6545
- Accuracy: 0.9206

## 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: 7.939824124650849e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=7.770880460560945e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 116  | 0.3117          | 0.5283    | 0.6480 | 0.5821 | 0.8986   |
| No log        | 2.0   | 233  | 0.3007          | 0.5697    | 0.6663 | 0.6142 | 0.9070   |
| No log        | 3.0   | 349  | 0.2725          | 0.6277    | 0.6895 | 0.6572 | 0.9196   |
| No log        | 4.0   | 466  | 0.2998          | 0.6186    | 0.6789 | 0.6474 | 0.9186   |
| No log        | 4.98  | 580  | 0.3060          | 0.6206    | 0.6924 | 0.6545 | 0.9206   |


### Framework versions

- Transformers 4.27.3
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-cadec-ner", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us