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_padding10model
  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: 0.9506578947368421
      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_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: 0.5342
- Accuracy: 0.9507

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1973        | 1.0   | 7500   | 0.2025          | 0.9403   |
| 0.1674        | 2.0   | 15000  | 0.1876          | 0.9471   |
| 0.1488        | 3.0   | 22500  | 0.2367          | 0.9446   |
| 0.1213        | 4.0   | 30000  | 0.2451          | 0.9461   |
| 0.0942        | 5.0   | 37500  | 0.2545          | 0.9464   |
| 0.0842        | 6.0   | 45000  | 0.3061          | 0.9446   |
| 0.0718        | 7.0   | 52500  | 0.2821          | 0.9476   |
| 0.0562        | 8.0   | 60000  | 0.4124          | 0.9443   |
| 0.0387        | 9.0   | 67500  | 0.4309          | 0.9409   |
| 0.0299        | 10.0  | 75000  | 0.4162          | 0.9470   |
| 0.028         | 11.0  | 82500  | 0.4086          | 0.9479   |
| 0.026         | 12.0  | 90000  | 0.4091          | 0.9466   |
| 0.0205        | 13.0  | 97500  | 0.4481          | 0.9457   |
| 0.0124        | 14.0  | 105000 | 0.4895          | 0.9453   |
| 0.0093        | 15.0  | 112500 | 0.5086          | 0.9463   |
| 0.0075        | 16.0  | 120000 | 0.4911          | 0.9487   |
| 0.0068        | 17.0  | 127500 | 0.4924          | 0.9496   |
| 0.0023        | 18.0  | 135000 | 0.5008          | 0.9503   |
| 0.0014        | 19.0  | 142500 | 0.5251          | 0.9505   |
| 0.0049        | 20.0  | 150000 | 0.5342          | 0.9507   |


### Framework versions

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