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CN_RoBERTa_Dig
Kghate/CN_RoBERTa_Dig
CN_RoBERTa_Dig is a text classification model from Kghate. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Nov 17, 2023
- Updated
- Nov 17, 2023
- SHA
- 3b931ac8
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Base models
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. -->
CN_RoBERTa_Dig
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0055
- F1: {'f1': 0.9988009592326139}
- Accuracy: {'accuracy': 0.9988}
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 0.4018 | 0.09 | 1000 | 0.3457 | {'f1': 0.6695906432748538} | {'accuracy': 0.7514} |
| 0.3392 | 0.18 | 2000 | 0.2601 | {'f1': 0.9148995796356842} | {'accuracy': 0.9089} |
| 0.2443 | 0.27 | 3000 | 0.1276 | {'f1': 0.9713375796178344} | {'accuracy': 0.9712} |
| 0.1399 | 0.36 | 4000 | 0.0616 | {'f1': 0.9867973594718943} | {'accuracy': 0.9868} |
| 0.0926 | 0.44 | 5000 | 0.0280 | {'f1': 0.9927341494973624} | {'accuracy': 0.9927} |
| 0.0835 | 0.53 | 6000 | 0.0260 | {'f1': 0.9942196531791908} | {'accuracy': 0.9942} |
| 0.0617 | 0.62 | 7000 | 0.0129 | {'f1': 0.9969981989193516} | {'accuracy': 0.997} |
| 0.0459 | 0.71 | 8000 | 0.0097 | {'f1': 0.9977029861180465} | {'accuracy': 0.9977} |
| 0.0363 | 0.8 | 9000 | 0.0111 | {'f1': 0.9976047904191618} | {'accuracy': 0.9976} |
| 0.0421 | 0.89 | 10000 | 0.0078 | {'f1': 0.9980035935316429} | {'accuracy': 0.998} |
| 0.0317 | 0.98 | 11000 | 0.0055 | {'f1': 0.9988009592326139} | {'accuracy': 0.9988} |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Nov 17, 2023
- Updated
- Nov 17, 2023