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