---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_bert_agnews_padding40model
  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: 0.9473684210526315
---

<!-- 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_padding40model

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: 0.5661
- Accuracy: 0.9474

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1785        | 1.0   | 7500   | 0.1884          | 0.9421   |
| 0.1379        | 2.0   | 15000  | 0.1990          | 0.9478   |
| 0.1127        | 3.0   | 22500  | 0.2389          | 0.9408   |
| 0.0846        | 4.0   | 30000  | 0.2528          | 0.9492   |
| 0.0581        | 5.0   | 37500  | 0.3041          | 0.9436   |
| 0.0456        | 6.0   | 45000  | 0.3415          | 0.9468   |
| 0.0411        | 7.0   | 52500  | 0.4081          | 0.9430   |
| 0.0239        | 8.0   | 60000  | 0.4415          | 0.9433   |
| 0.0202        | 9.0   | 67500  | 0.4380          | 0.9404   |
| 0.0126        | 10.0  | 75000  | 0.4637          | 0.9425   |
| 0.0175        | 11.0  | 82500  | 0.4485          | 0.9455   |
| 0.0126        | 12.0  | 90000  | 0.4761          | 0.9449   |
| 0.0046        | 13.0  | 97500  | 0.5009          | 0.9455   |
| 0.0038        | 14.0  | 105000 | 0.4784          | 0.9482   |
| 0.0035        | 15.0  | 112500 | 0.5282          | 0.9451   |
| 0.0046        | 16.0  | 120000 | 0.5256          | 0.9464   |
| 0.0026        | 17.0  | 127500 | 0.5081          | 0.9501   |
| 0.0008        | 18.0  | 135000 | 0.5543          | 0.9467   |
| 0.0002        | 19.0  | 142500 | 0.5448          | 0.9488   |
| 0.0016        | 20.0  | 150000 | 0.5661          | 0.9474   |


### Framework versions

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