README.md

---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-base_gpt-4o-2024-05-13_gpt-4o-mini-2024-07-18_20240913_044355
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta-base_gpt-4o-2024-05-13_gpt-4o-mini-2024-07-18_20240913_044355

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: 0.4503
- Accuracy: 0.8026
- F1: 0.8832
- Precision: 0.8292
- Recall: 0.9448

## 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: 32
- eval_batch_size: 8
- seed: 420
- 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 | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4781        | 1.0   | 871  | 0.4503          | 0.8026   | 0.8832 | 0.8292    | 0.9448 |
| 0.4526        | 2.0   | 1742 | 0.4536          | 0.8048   | 0.8822 | 0.8434    | 0.9248 |
| 0.424         | 3.0   | 2613 | 0.4529          | 0.8052   | 0.8837 | 0.8362    | 0.9370 |
| 0.3789        | 4.0   | 3484 | 0.4970          | 0.8029   | 0.8826 | 0.8336    | 0.9379 |
| 0.3275        | 5.0   | 4355 | 0.5587          | 0.7945   | 0.8777 | 0.8286    | 0.9330 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1