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_padding90model
  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: 0.9443421052631579
      name: Accuracy
---

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# distilbert_agnews_padding90model

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: 0.6543
- Accuracy: 0.9443

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1824        | 1.0   | 7500   | 0.1893          | 0.9428   |
| 0.1381        | 2.0   | 15000  | 0.2112          | 0.9401   |
| 0.1208        | 3.0   | 22500  | 0.2110          | 0.9449   |
| 0.091         | 4.0   | 30000  | 0.2609          | 0.9428   |
| 0.065         | 5.0   | 37500  | 0.2991          | 0.9417   |
| 0.0447        | 6.0   | 45000  | 0.3947          | 0.9408   |
| 0.0374        | 7.0   | 52500  | 0.4052          | 0.9354   |
| 0.0275        | 8.0   | 60000  | 0.4441          | 0.9418   |
| 0.0198        | 9.0   | 67500  | 0.4479          | 0.9408   |
| 0.0131        | 10.0  | 75000  | 0.5038          | 0.9387   |
| 0.0128        | 11.0  | 82500  | 0.5114          | 0.9424   |
| 0.0098        | 12.0  | 90000  | 0.5130          | 0.9436   |
| 0.0085        | 13.0  | 97500  | 0.5473          | 0.9414   |
| 0.0066        | 14.0  | 105000 | 0.5601          | 0.9429   |
| 0.0054        | 15.0  | 112500 | 0.5617          | 0.9424   |
| 0.0023        | 16.0  | 120000 | 0.5911          | 0.9433   |
| 0.0021        | 17.0  | 127500 | 0.6165          | 0.9442   |
| 0.0038        | 18.0  | 135000 | 0.6105          | 0.9446   |
| 0.0008        | 19.0  | 142500 | 0.6462          | 0.9422   |
| 0.0004        | 20.0  | 150000 | 0.6543          | 0.9443   |


### Framework versions

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