transformers pytorch bert text-classification generated_from_trainer dataset:ag_news base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- ag_news
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: N_bert_agnews_padding70model
  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: 0.9464473684210526
      name: Accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# N_bert_agnews_padding70model

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: 0.5807
- Accuracy: 0.9464

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1804        | 1.0   | 7500   | 0.1897          | 0.9418   |
| 0.1359        | 2.0   | 15000  | 0.1998          | 0.9454   |
| 0.1189        | 3.0   | 22500  | 0.2309          | 0.9443   |
| 0.0882        | 4.0   | 30000  | 0.2622          | 0.9458   |
| 0.0579        | 5.0   | 37500  | 0.3019          | 0.9417   |
| 0.0389        | 6.0   | 45000  | 0.3608          | 0.9438   |
| 0.0359        | 7.0   | 52500  | 0.3635          | 0.9426   |
| 0.0296        | 8.0   | 60000  | 0.4064          | 0.9428   |
| 0.015         | 9.0   | 67500  | 0.4595          | 0.9417   |
| 0.0191        | 10.0  | 75000  | 0.4516          | 0.9433   |
| 0.0146        | 11.0  | 82500  | 0.5207          | 0.9409   |
| 0.0138        | 12.0  | 90000  | 0.4787          | 0.9442   |
| 0.0108        | 13.0  | 97500  | 0.5216          | 0.9408   |
| 0.0071        | 14.0  | 105000 | 0.5377          | 0.9455   |
| 0.0047        | 15.0  | 112500 | 0.5283          | 0.9459   |
| 0.0035        | 16.0  | 120000 | 0.4928          | 0.9451   |
| 0.0007        | 17.0  | 127500 | 0.5760          | 0.9454   |
| 0.0004        | 18.0  | 135000 | 0.5759          | 0.9449   |
| 0.0002        | 19.0  | 142500 | 0.5637          | 0.9472   |
| 0.0003        | 20.0  | 150000 | 0.5807          | 0.9464   |


### Framework versions

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