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distilbert-bbc-news-classification
Umesh/distilbert-bbc-news-classification
distilbert-bbc-news-classification is a text classification model from Umesh. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Jan 21, 2023
- Updated
- Jan 23, 2023
- SHA
- f3c215ce
Try a prompt
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
distilbert-bbc-news-classification
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0669
- Accuracy: 0.9880
- F1-score: 0.9880
- Recall: 0.9886
- Precision: 0.9875
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Recall | Precision |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 98 | 0.1495 | 0.9775 | 0.9775 | 0.9772 | 0.9781 |
| No log | 2.0 | 196 | 0.0737 | 0.9880 | 0.9882 | 0.9885 | 0.9880 |
| No log | 3.0 | 294 | 0.0669 | 0.9880 | 0.9880 | 0.9886 | 0.9875 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Jan 21, 2023
- Updated
- Jan 23, 2023