---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: roberta
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# roberta

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: 1.0480
- F1: 0.6518
- Precision: 0.6478
- Recall: 0.6755
- Accuracy: 0.6755

## 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: 4e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     | Precision | Recall | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 1.1301        | 1.0   | 4409  | 1.0908          | 0.6331 | 0.6205    | 0.6767 | 0.6767   |
| 0.9143        | 2.0   | 8818  | 1.0480          | 0.6518 | 0.6478    | 0.6755 | 0.6755   |
| 0.7126        | 3.0   | 13227 | 1.2204          | 0.6456 | 0.6351    | 0.6609 | 0.6609   |
| 0.5437        | 4.0   | 17636 | 1.3307          | 0.6394 | 0.6304    | 0.6515 | 0.6515   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0