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_padding30model
  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: 0.9477631578947369
      name: Accuracy
---

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# N_roberta_agnews_padding30model

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: 0.5706
- Accuracy: 0.9478

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1966        | 1.0   | 7500   | 0.2071          | 0.9384   |
| 0.1667        | 2.0   | 15000  | 0.1922          | 0.9466   |
| 0.1523        | 3.0   | 22500  | 0.2323          | 0.9438   |
| 0.1194        | 4.0   | 30000  | 0.2370          | 0.9438   |
| 0.105         | 5.0   | 37500  | 0.2791          | 0.9454   |
| 0.0836        | 6.0   | 45000  | 0.2917          | 0.9433   |
| 0.0711        | 7.0   | 52500  | 0.3344          | 0.9436   |
| 0.0586        | 8.0   | 60000  | 0.3723          | 0.9416   |
| 0.0396        | 9.0   | 67500  | 0.3977          | 0.9438   |
| 0.0369        | 10.0  | 75000  | 0.4096          | 0.9425   |
| 0.0312        | 11.0  | 82500  | 0.4293          | 0.9438   |
| 0.0259        | 12.0  | 90000  | 0.4286          | 0.9436   |
| 0.0241        | 13.0  | 97500  | 0.4529          | 0.9437   |
| 0.0129        | 14.0  | 105000 | 0.4749          | 0.9442   |
| 0.0057        | 15.0  | 112500 | 0.5355          | 0.9429   |
| 0.0083        | 16.0  | 120000 | 0.5056          | 0.9475   |
| 0.0062        | 17.0  | 127500 | 0.5138          | 0.9458   |
| 0.0062        | 18.0  | 135000 | 0.5368          | 0.9463   |
| 0.0026        | 19.0  | 142500 | 0.5647          | 0.9470   |
| 0.0031        | 20.0  | 150000 | 0.5706          | 0.9478   |


### Framework versions

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