---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pretrain_model
  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. -->

# pretrain_model

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: 0.6196
- Precision: 0.6607
- Recall: 0.6589
- F1: 0.6598
- Accuracy: 0.6575

## 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: 8
- eval_batch_size: 8
- 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: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6965        | 0.1377 | 500  | 0.6910          | 0.526     | 1.0    | 0.6894 | 0.526    |
| 0.6963        | 0.2755 | 1000 | 0.6921          | 0.526     | 1.0    | 0.6894 | 0.526    |
| 0.6957        | 0.4132 | 1500 | 0.6666          | 0.6154    | 0.7300 | 0.6678 | 0.618    |
| 0.6914        | 0.5510 | 2000 | 0.6834          | 0.7069    | 0.4677 | 0.5629 | 0.618    |
| 0.6768        | 0.6887 | 2500 | 0.6838          | 0.6412    | 0.6388 | 0.64   | 0.622    |
| 0.6786        | 0.8264 | 3000 | 0.6539          | 0.7273    | 0.4259 | 0.5372 | 0.614    |
| 0.663         | 0.9642 | 3500 | 0.6743          | 0.6560    | 0.5437 | 0.5946 | 0.61     |
| 0.6564        | 1.1019 | 4000 | 0.6381          | 0.6763    | 0.6198 | 0.6468 | 0.644    |
| 0.6468        | 1.2397 | 4500 | 0.6010          | 0.6613    | 0.7871 | 0.7188 | 0.676    |
| 0.6275        | 1.3774 | 5000 | 0.6103          | 0.7246    | 0.5703 | 0.6383 | 0.66     |
| 0.6275        | 1.5152 | 5500 | 0.6018          | 0.7311    | 0.5894 | 0.6526 | 0.67     |
| 0.6141        | 1.6529 | 6000 | 0.5947          | 0.7269    | 0.6578 | 0.6906 | 0.69     |
| 0.617         | 1.7906 | 6500 | 0.5872          | 0.7165    | 0.6920 | 0.7041 | 0.694    |
| 0.6059        | 1.9284 | 7000 | 0.5816          | 0.7227    | 0.7034 | 0.7129 | 0.702    |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3