metadata

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

distilbert-base-uncased-finetuned-news

This model is a fine-tuned version of 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