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# biobert-cadec-ner

This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3115
- Precision: 0.6817
- Recall: 0.6858
- F1: 0.6837
- Accuracy: 0.9275

## 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: 0.00014812349590569507
- 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=1.0703999133408962e-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  | 0.2984          | 0.5957    | 0.6441 | 0.6190 | 0.9044   |
| No log        | 2.0   | 233  | 0.2894          | 0.5952    | 0.6701 | 0.6305 | 0.9050   |
| No log        | 3.0   | 349  | 0.2948          | 0.6618    | 0.6687 | 0.6652 | 0.9178   |
| No log        | 4.0   | 466  | 0.2957          | 0.6740    | 0.6634 | 0.6687 | 0.9261   |
| No log        | 4.98  | 580  | 0.3115          | 0.6817    | 0.6858 | 0.6837 | 0.9275   |


### Framework versions

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

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