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

# 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: 2.0309
- Precision: 0.2689
- Recall: 0.2544
- F1: 0.2615
- Accuracy: 0.8742

## 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: 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: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1094        | 0.4292 | 100  | 1.8029          | 0.3026    | 0.1599 | 0.2092 | 0.8942   |
| 0.1068        | 0.8584 | 200  | 1.7311          | 0.2883    | 0.2617 | 0.2744 | 0.8789   |
| 0.059         | 1.2876 | 300  | 2.0629          | 0.3091    | 0.2212 | 0.2579 | 0.8886   |
| 0.0713        | 1.7167 | 400  | 2.5245          | 0.3529    | 0.1308 | 0.1909 | 0.9029   |
| 0.0634        | 2.1459 | 500  | 2.3395          | 0.3122    | 0.1786 | 0.2272 | 0.8937   |
| 0.0572        | 2.5751 | 600  | 2.2058          | 0.2864    | 0.2347 | 0.2580 | 0.8819   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0