---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_bert_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: 0.9467105263157894
---

<!-- 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_bert_agnews_padding50model

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5754
- Accuracy: 0.9467

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1754        | 1.0   | 7500   | 0.1909          | 0.9428   |
| 0.1362        | 2.0   | 15000  | 0.1928          | 0.9461   |
| 0.1139        | 3.0   | 22500  | 0.2106          | 0.9461   |
| 0.0825        | 4.0   | 30000  | 0.2544          | 0.9466   |
| 0.0565        | 5.0   | 37500  | 0.3046          | 0.9367   |
| 0.0372        | 6.0   | 45000  | 0.3764          | 0.9436   |
| 0.0347        | 7.0   | 52500  | 0.3646          | 0.9425   |
| 0.0346        | 8.0   | 60000  | 0.3826          | 0.9461   |
| 0.0247        | 9.0   | 67500  | 0.4244          | 0.9455   |
| 0.0113        | 10.0  | 75000  | 0.4418          | 0.9446   |
| 0.0166        | 11.0  | 82500  | 0.4917          | 0.9462   |
| 0.0157        | 12.0  | 90000  | 0.4662          | 0.9442   |
| 0.0124        | 13.0  | 97500  | 0.4864          | 0.9438   |
| 0.0055        | 14.0  | 105000 | 0.4912          | 0.9457   |
| 0.0102        | 15.0  | 112500 | 0.5040          | 0.9446   |
| 0.0045        | 16.0  | 120000 | 0.5200          | 0.9441   |
| 0.0038        | 17.0  | 127500 | 0.5374          | 0.9467   |
| 0.0012        | 18.0  | 135000 | 0.5605          | 0.9459   |
| 0.0005        | 19.0  | 142500 | 0.5809          | 0.9455   |
| 0.0003        | 20.0  | 150000 | 0.5754          | 0.9467   |


### Framework versions

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