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distilbert-base-uncased-finetuned-spam
MFrazz/distilbert-base-uncased-finetuned-spam
distilbert-base-uncased-finetuned-spam is a text classification model from MFrazz. 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
- May 9, 2023
- Updated
- Jun 11, 2023
- SHA
- 5e122f72
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Datasets
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-base-uncased-finetuned-spam
This model is a fine-tuned version of distilbert-base-uncased on the sms_spam dataset. It achieves the following results on the evaluation set:
- Loss: 0.0370
- Accuracy: 0.9883
- F1: 0.9883
Model description
More information needed
Label Key
- LABEL_1 = SPAM
- LABEL_0 = HAM
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: 64
- eval_batch_size: 64
- 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 |
|---|---|---|---|---|---|
| 0.174 | 1.0 | 70 | 0.0444 | 0.9865 | 0.9866 |
| 0.0303 | 2.0 | 140 | 0.0370 | 0.9883 | 0.9883 |
Framework versions
- Transformers 4.27.1
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- May 9, 2023
- Updated
- Jun 11, 2023