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CN_BERT_Digit
vishwa27/CN_BERT_Digit
CN_BERT_Digit 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 14, 2023
- Updated
- Nov 16, 2023
- SHA
- 5ad19b5b
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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_Digit
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: 0.0264
- F1: {'f1': 0.9936038376973816}
- Accuracy: {'accuracy': 0.9936}
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 |
|---|---|---|---|---|---|
| 0.4023 | 0.09 | 1000 | 0.5834 | {'f1': 0.7928479381443299} | {'accuracy': 0.7428} |
| 0.269 | 0.18 | 2000 | 0.2556 | {'f1': 0.8676012461059189} | {'accuracy': 0.881} |
| 0.1879 | 0.27 | 3000 | 0.1296 | {'f1': 0.9648982848025529} | {'accuracy': 0.9648} |
| 0.142 | 0.36 | 4000 | 0.1022 | {'f1': 0.9740272663946662} | {'accuracy': 0.9739} |
| 0.1172 | 0.44 | 5000 | 0.0724 | {'f1': 0.979466322785438} | {'accuracy': 0.9793} |
| 0.1044 | 0.53 | 6000 | 0.1166 | {'f1': 0.9756195043964828} | {'accuracy': 0.9756} |
| 0.0948 | 0.62 | 7000 | 0.0538 | {'f1': 0.98813441021039} | {'accuracy': 0.9881} |
| 0.075 | 0.71 | 8000 | 0.0444 | {'f1': 0.9892989298929893} | {'accuracy': 0.9893} |
| 0.0667 | 0.8 | 9000 | 0.0427 | {'f1': 0.9911168779319294} | {'accuracy': 0.9911} |
| 0.0667 | 0.89 | 10000 | 0.0448 | {'f1': 0.9908384783907588} | {'accuracy': 0.9908} |
| 0.0668 | 0.98 | 11000 | 0.0264 | {'f1': 0.9936038376973816} | {'accuracy': 0.9936} |
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 14, 2023
- Updated
- Nov 16, 2023