---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_roberta_agnews_padding50model
  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: 0.9485526315789473
---

<!-- 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. -->

# N_roberta_agnews_padding50model

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: 0.5524
- Accuracy: 0.9486

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1998        | 1.0   | 7500   | 0.2132          | 0.9382   |
| 0.1682        | 2.0   | 15000  | 0.2009          | 0.9475   |
| 0.1506        | 3.0   | 22500  | 0.2273          | 0.9446   |
| 0.1294        | 4.0   | 30000  | 0.2495          | 0.9482   |
| 0.1028        | 5.0   | 37500  | 0.2612          | 0.9459   |
| 0.0797        | 6.0   | 45000  | 0.2966          | 0.9457   |
| 0.0646        | 7.0   | 52500  | 0.3040          | 0.9458   |
| 0.0531        | 8.0   | 60000  | 0.3825          | 0.9446   |
| 0.0443        | 9.0   | 67500  | 0.3838          | 0.9425   |
| 0.0345        | 10.0  | 75000  | 0.3968          | 0.9475   |
| 0.0395        | 11.0  | 82500  | 0.4132          | 0.9474   |
| 0.019         | 12.0  | 90000  | 0.4612          | 0.9453   |
| 0.0219        | 13.0  | 97500  | 0.4559          | 0.9458   |
| 0.0067        | 14.0  | 105000 | 0.4692          | 0.9467   |
| 0.0065        | 15.0  | 112500 | 0.5118          | 0.9461   |
| 0.0045        | 16.0  | 120000 | 0.5115          | 0.9470   |
| 0.004         | 17.0  | 127500 | 0.5326          | 0.9472   |
| 0.0079        | 18.0  | 135000 | 0.5088          | 0.9483   |
| 0.0039        | 19.0  | 142500 | 0.5359          | 0.9504   |
| 0.0024        | 20.0  | 150000 | 0.5524          | 0.9486   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3