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CN_BERT_Sci
vishwa27/CN_BERT_Sci
CN_BERT_Sci is a text classification model from vishwa27. 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
- bert
- Access
- Public
- Created
- Nov 15, 2023
- Updated
- Nov 16, 2023
- SHA
- a549620d
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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_BERT_Sci
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0088
- F1: {'f1': 0.9980007996801279}
- Accuracy: {'accuracy': 0.998}
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 |
|---|---|---|---|---|---|
| 0.3819 | 0.09 | 1000 | 0.3378 | {'f1': 0.7968977217644208} | {'accuracy': 0.7905} |
| 0.2709 | 0.18 | 2000 | 0.2719 | {'f1': 0.92162615255658} | {'accuracy': 0.9252} |
| 0.169 | 0.27 | 3000 | 0.0888 | {'f1': 0.9760964045831687} | {'accuracy': 0.9758} |
| 0.0963 | 0.36 | 4000 | 0.0350 | {'f1': 0.991297389216765} | {'accuracy': 0.9913} |
| 0.0499 | 0.44 | 5000 | 0.0260 | {'f1': 0.9937381969983102} | {'accuracy': 0.9937} |
| 0.0344 | 0.53 | 6000 | 0.0170 | {'f1': 0.9963048037551183} | {'accuracy': 0.9963} |
| 0.0307 | 0.62 | 7000 | 0.0213 | {'f1': 0.9957991598319663} | {'accuracy': 0.9958} |
| 0.036 | 0.71 | 8000 | 0.0105 | {'f1': 0.997700689793062} | {'accuracy': 0.9977} |
| 0.0209 | 0.8 | 9000 | 0.0106 | {'f1': 0.9981032245183188} | {'accuracy': 0.9981} |
| 0.0253 | 0.89 | 10000 | 0.0089 | {'f1': 0.9981024667931688} | {'accuracy': 0.9981} |
| 0.0231 | 0.98 | 11000 | 0.0088 | {'f1': 0.9980007996801279} | {'accuracy': 0.998} |
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
- bert
- License
- apache-2.0
- Created
- Nov 15, 2023
- Updated
- Nov 16, 2023