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MiniLMv2-L12-H384-distilled-finetuned-spam-detection
Rhuax/MiniLMv2-L12-H384-distilled-finetuned-spam-detection
MiniLMv2-L12-H384-distilled-finetuned-spam-detection is a text classification model from Rhuax. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Jun 1, 2022
- Updated
- Jun 2, 2022
- SHA
- 5ff852c3
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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. -->
MiniLMv2-L12-H384-distilled-finetuned-spam-detection
This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the sms_spam dataset. It achieves the following results on the evaluation set:
- Loss: 0.0938
- Accuracy: 0.9928
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.4101 | 1.0 | 131 | 0.4930 | 0.9763 |
| 0.8003 | 2.0 | 262 | 0.3999 | 0.9799 |
| 0.377 | 3.0 | 393 | 0.3196 | 0.9828 |
| 0.302 | 4.0 | 524 | 0.3462 | 0.9828 |
| 0.1945 | 5.0 | 655 | 0.1094 | 0.9928 |
| 0.1393 | 6.0 | 786 | 0.0938 | 0.9928 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Jun 1, 2022
- Updated
- Jun 2, 2022