---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: roberta_agnews_padding10model
  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: 0.9502631578947368
---

<!-- 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. -->

# roberta_agnews_padding10model

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: 0.5337
- Accuracy: 0.9503

## 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   | 0.2068          | 0.9404   |
| 0.1632        | 2.0   | 15000  | 0.1954          | 0.9457   |
| 0.1432        | 3.0   | 22500  | 0.2422          | 0.9478   |
| 0.1223        | 4.0   | 30000  | 0.2275          | 0.9486   |
| 0.0994        | 5.0   | 37500  | 0.2442          | 0.9486   |
| 0.079         | 6.0   | 45000  | 0.3053          | 0.9486   |
| 0.0759        | 7.0   | 52500  | 0.3104          | 0.9463   |
| 0.0506        | 8.0   | 60000  | 0.3757          | 0.9472   |
| 0.0436        | 9.0   | 67500  | 0.3468          | 0.9470   |
| 0.025         | 10.0  | 75000  | 0.4170          | 0.9468   |
| 0.0303        | 11.0  | 82500  | 0.4168          | 0.9462   |
| 0.0273        | 12.0  | 90000  | 0.4173          | 0.9486   |
| 0.024         | 13.0  | 97500  | 0.4305          | 0.9476   |
| 0.0139        | 14.0  | 105000 | 0.4549          | 0.9480   |
| 0.0111        | 15.0  | 112500 | 0.4961          | 0.9483   |
| 0.0102        | 16.0  | 120000 | 0.4733          | 0.9488   |
| 0.0036        | 17.0  | 127500 | 0.5044          | 0.9493   |
| 0.0025        | 18.0  | 135000 | 0.5070          | 0.95     |
| 0.0024        | 19.0  | 142500 | 0.5196          | 0.9508   |
| 0.0018        | 20.0  | 150000 | 0.5337          | 0.9503   |


### Framework versions

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