---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_bert_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: 0.9456578947368421
---

<!-- 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_padding10model

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: 0.5739
- Accuracy: 0.9457

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.176         | 1.0   | 7500   | 0.1846          | 0.9449   |
| 0.1315        | 2.0   | 15000  | 0.1942          | 0.9455   |
| 0.1116        | 3.0   | 22500  | 0.2441          | 0.9434   |
| 0.0804        | 4.0   | 30000  | 0.3103          | 0.9421   |
| 0.0548        | 5.0   | 37500  | 0.2975          | 0.9429   |
| 0.0417        | 6.0   | 45000  | 0.3861          | 0.9413   |
| 0.0299        | 7.0   | 52500  | 0.4010          | 0.9388   |
| 0.0325        | 8.0   | 60000  | 0.4365          | 0.9428   |
| 0.0232        | 9.0   | 67500  | 0.4431          | 0.9429   |
| 0.0173        | 10.0  | 75000  | 0.4699          | 0.9386   |
| 0.0139        | 11.0  | 82500  | 0.4937          | 0.9412   |
| 0.0121        | 12.0  | 90000  | 0.4899          | 0.9439   |
| 0.0047        | 13.0  | 97500  | 0.5263          | 0.9449   |
| 0.0106        | 14.0  | 105000 | 0.5317          | 0.9436   |
| 0.0028        | 15.0  | 112500 | 0.5426          | 0.9426   |
| 0.0052        | 16.0  | 120000 | 0.5332          | 0.9472   |
| 0.0025        | 17.0  | 127500 | 0.5458          | 0.9464   |
| 0.0023        | 18.0  | 135000 | 0.5433          | 0.9442   |
| 0.0003        | 19.0  | 142500 | 0.5707          | 0.9461   |
| 0.0009        | 20.0  | 150000 | 0.5739          | 0.9457   |


### Framework versions

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