transformers pytorch roberta text-classification generated_from_trainer dataset:ag_news base_model:FacebookAI/roberta-base base_model:finetune:FacebookAI/roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: roberta-base
datasets:
- ag_news
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: N_roberta_agnews_padding20model
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: ag_news
      type: ag_news
      config: default
      split: test
      args: default
    metrics:
    - type: accuracy
      value: 0.9485526315789473
      name: Accuracy
---

<!-- 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_padding20model

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: 0.5475
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1932        | 1.0   | 7500   | 0.1956          | 0.9442   |
| 0.1695        | 2.0   | 15000  | 0.1987          | 0.9459   |
| 0.1531        | 3.0   | 22500  | 0.2017          | 0.9462   |
| 0.1247        | 4.0   | 30000  | 0.2431          | 0.9479   |
| 0.1026        | 5.0   | 37500  | 0.2465          | 0.9479   |
| 0.078         | 6.0   | 45000  | 0.3001          | 0.9478   |
| 0.0752        | 7.0   | 52500  | 0.3209          | 0.945    |
| 0.048         | 8.0   | 60000  | 0.3456          | 0.9484   |
| 0.0408        | 9.0   | 67500  | 0.4119          | 0.9436   |
| 0.0338        | 10.0  | 75000  | 0.3976          | 0.945    |
| 0.0305        | 11.0  | 82500  | 0.4211          | 0.945    |
| 0.022         | 12.0  | 90000  | 0.4607          | 0.9470   |
| 0.0224        | 13.0  | 97500  | 0.4733          | 0.9464   |
| 0.0127        | 14.0  | 105000 | 0.4984          | 0.9466   |
| 0.0135        | 15.0  | 112500 | 0.4808          | 0.9492   |
| 0.0085        | 16.0  | 120000 | 0.4993          | 0.9497   |
| 0.0045        | 17.0  | 127500 | 0.5184          | 0.9475   |
| 0.0046        | 18.0  | 135000 | 0.5259          | 0.9479   |
| 0.0037        | 19.0  | 142500 | 0.5419          | 0.9483   |
| 0.0017        | 20.0  | 150000 | 0.5475          | 0.9486   |


### Framework versions

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