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: roberta_agnews_padding0model
  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: 0.9493421052631579
      name: Accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta_agnews_padding0model

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: 0.5563
- Accuracy: 0.9493

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1901        | 1.0   | 7500   | 0.2099          | 0.9378   |
| 0.1664        | 2.0   | 15000  | 0.2084          | 0.9446   |
| 0.148         | 3.0   | 22500  | 0.2164          | 0.9479   |
| 0.1201        | 4.0   | 30000  | 0.2506          | 0.9442   |
| 0.0999        | 5.0   | 37500  | 0.2447          | 0.9505   |
| 0.0731        | 6.0   | 45000  | 0.3085          | 0.9463   |
| 0.0668        | 7.0   | 52500  | 0.3298          | 0.9467   |
| 0.0577        | 8.0   | 60000  | 0.3703          | 0.9453   |
| 0.0435        | 9.0   | 67500  | 0.3854          | 0.9462   |
| 0.0313        | 10.0  | 75000  | 0.3833          | 0.945    |
| 0.023         | 11.0  | 82500  | 0.4196          | 0.9459   |
| 0.0231        | 12.0  | 90000  | 0.4412          | 0.9441   |
| 0.0207        | 13.0  | 97500  | 0.4519          | 0.9458   |
| 0.0153        | 14.0  | 105000 | 0.4682          | 0.9463   |
| 0.0136        | 15.0  | 112500 | 0.4854          | 0.9487   |
| 0.0118        | 16.0  | 120000 | 0.5146          | 0.9468   |
| 0.0058        | 17.0  | 127500 | 0.5119          | 0.9487   |
| 0.002         | 18.0  | 135000 | 0.5292          | 0.9495   |
| 0.0026        | 19.0  | 142500 | 0.5443          | 0.9483   |
| 0.0015        | 20.0  | 150000 | 0.5563          | 0.9493   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
