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: 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: 0.9452631578947368
      name: Accuracy
---

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# 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: 0.6477
- Accuracy: 0.9453

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.18          | 1.0   | 7500   | 0.1831          | 0.9426   |
| 0.1365        | 2.0   | 15000  | 0.2039          | 0.9420   |
| 0.1176        | 3.0   | 22500  | 0.2202          | 0.9470   |
| 0.0899        | 4.0   | 30000  | 0.2601          | 0.9443   |
| 0.0547        | 5.0   | 37500  | 0.2919          | 0.9429   |
| 0.0387        | 6.0   | 45000  | 0.3618          | 0.9459   |
| 0.0351        | 7.0   | 52500  | 0.4129          | 0.9413   |
| 0.031         | 8.0   | 60000  | 0.4379          | 0.9436   |
| 0.0171        | 9.0   | 67500  | 0.4794          | 0.9429   |
| 0.0156        | 10.0  | 75000  | 0.4744          | 0.9438   |
| 0.0147        | 11.0  | 82500  | 0.4832          | 0.9457   |
| 0.0108        | 12.0  | 90000  | 0.5166          | 0.9447   |
| 0.0034        | 13.0  | 97500  | 0.5083          | 0.9459   |
| 0.0065        | 14.0  | 105000 | 0.5451          | 0.9446   |
| 0.0062        | 15.0  | 112500 | 0.5926          | 0.9443   |
| 0.0031        | 16.0  | 120000 | 0.6059          | 0.9433   |
| 0.001         | 17.0  | 127500 | 0.6312          | 0.9463   |
| 0.0004        | 18.0  | 135000 | 0.6197          | 0.9454   |
| 0.0004        | 19.0  | 142500 | 0.6472          | 0.9455   |
| 0.0002        | 20.0  | 150000 | 0.6477          | 0.9453   |


### Framework versions

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