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

More information needed

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