---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_distilbert_agnews_padding50model
  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: 0.9418421052631579
---

<!-- 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_distilbert_agnews_padding50model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6732
- Accuracy: 0.9418

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1757        | 1.0   | 7500   | 0.1847          | 0.9428   |
| 0.1399        | 2.0   | 15000  | 0.1983          | 0.9439   |
| 0.1196        | 3.0   | 22500  | 0.2251          | 0.9418   |
| 0.0894        | 4.0   | 30000  | 0.2583          | 0.9436   |
| 0.0587        | 5.0   | 37500  | 0.3116          | 0.9425   |
| 0.0404        | 6.0   | 45000  | 0.3567          | 0.9432   |
| 0.0318        | 7.0   | 52500  | 0.4279          | 0.9392   |
| 0.0257        | 8.0   | 60000  | 0.4443          | 0.9407   |
| 0.0212        | 9.0   | 67500  | 0.4974          | 0.9378   |
| 0.0106        | 10.0  | 75000  | 0.4965          | 0.9417   |
| 0.0145        | 11.0  | 82500  | 0.4986          | 0.9433   |
| 0.011         | 12.0  | 90000  | 0.5389          | 0.9392   |
| 0.0121        | 13.0  | 97500  | 0.5671          | 0.9441   |
| 0.0046        | 14.0  | 105000 | 0.6063          | 0.9396   |
| 0.0011        | 15.0  | 112500 | 0.6245          | 0.9414   |
| 0.002         | 16.0  | 120000 | 0.6103          | 0.9426   |
| 0.001         | 17.0  | 127500 | 0.6181          | 0.9426   |
| 0.0017        | 18.0  | 135000 | 0.6401          | 0.9408   |
| 0.0002        | 19.0  | 142500 | 0.6667          | 0.9422   |
| 0.0004        | 20.0  | 150000 | 0.6732          | 0.9418   |


### Framework versions

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