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# email_answer_extraction

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0388
- Precision: 0.3571
- Recall: 0.5769
- F1: 0.4412
- Accuracy: 0.9859

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4874        | 1.0   | 32   | 0.0956          | 0.0339    | 0.0769 | 0.0471 | 0.9714   |
| 0.1951        | 2.0   | 64   | 0.0448          | 0.2115    | 0.4231 | 0.2821 | 0.9829   |
| 0.1086        | 3.0   | 96   | 0.0384          | 0.3556    | 0.6154 | 0.4507 | 0.9857   |
| 0.0552        | 4.0   | 128  | 0.0388          | 0.3571    | 0.5769 | 0.4412 | 0.9859   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.16.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "roberta-base", "model-index": [{"name": "email_answer_extraction", "results": []}]}
transformers safetensors roberta token-classification generated_from_trainer base_model:roberta-base license:mit autotrain_compatible endpoints_compatible region:us