---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: roberta-base_ag_news
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: ag_news
      type: ag_news
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.89
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta-base_ag_news

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4016
- Accuracy: 0.89
- F1-score: 0.8903

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.5297        | 1.0   | 250  | 0.4016          | 0.89     | 0.8903   |
| 0.4108        | 2.0   | 500  | 0.4182          | 0.8863   | 0.8865   |
| 0.2583        | 3.0   | 750  | 0.5196          | 0.8958   | 0.8955   |
| 0.0053        | 4.0   | 1000 | 0.5965          | 0.8884   | 0.8884   |
| 0.0037        | 5.0   | 1250 | 0.5868          | 0.8911   | 0.8906   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1