pytorch tensorboard distilbert text-classification dataset:ag_news transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-news
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: ag_news
      type: ag_news
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9388157894736842
    - name: F1
      type: f1
      value: 0.9388275184627893
---

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# distilbert-base-uncased-finetuned-news

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: 0.2117
- Accuracy: 0.9388
- F1: 0.9388

## Model description

This model is intended to categorize news headlines into one of four categories; World, Sports, Science & Technology, or Business

## Intended uses & limitations

The model is limited by the training data it used. If you use the model with a news story that falls outside of the four intended categories, it produces quite confused results.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2949        | 1.0   | 3750 | 0.2501          | 0.9262   | 0.9261 |
| 0.1569        | 2.0   | 7500 | 0.2117          | 0.9388   | 0.9388 |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
