transformers pytorch distilbert text-classification generated_from_trainer dataset:ag_news base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- ag_news
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: N_distilbert_agnews_padding20model
  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: 0.9447368421052632
      name: Accuracy
---

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# N_distilbert_agnews_padding20model

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: 0.6409
- Accuracy: 0.9447

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1753        | 1.0   | 7500   | 0.1871          | 0.9414   |
| 0.1361        | 2.0   | 15000  | 0.2002          | 0.9443   |
| 0.1127        | 3.0   | 22500  | 0.2222          | 0.9439   |
| 0.0824        | 4.0   | 30000  | 0.2883          | 0.9411   |
| 0.0503        | 5.0   | 37500  | 0.3229          | 0.9405   |
| 0.0379        | 6.0   | 45000  | 0.3573          | 0.9426   |
| 0.0336        | 7.0   | 52500  | 0.3657          | 0.9421   |
| 0.0287        | 8.0   | 60000  | 0.4377          | 0.9433   |
| 0.0174        | 9.0   | 67500  | 0.4701          | 0.9421   |
| 0.0177        | 10.0  | 75000  | 0.4664          | 0.9436   |
| 0.0176        | 11.0  | 82500  | 0.4614          | 0.9461   |
| 0.0106        | 12.0  | 90000  | 0.5123          | 0.9436   |
| 0.0091        | 13.0  | 97500  | 0.5772          | 0.9428   |
| 0.0043        | 14.0  | 105000 | 0.5659          | 0.9436   |
| 0.0048        | 15.0  | 112500 | 0.5934          | 0.9442   |
| 0.0037        | 16.0  | 120000 | 0.5910          | 0.945    |
| 0.0039        | 17.0  | 127500 | 0.6058          | 0.9461   |
| 0.0022        | 18.0  | 135000 | 0.6002          | 0.9468   |
| 0.0025        | 19.0  | 142500 | 0.6316          | 0.9461   |
| 0.0018        | 20.0  | 150000 | 0.6409          | 0.9447   |


### Framework versions

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