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bert_uncased_QAT
jysh1023/bert_uncased_QAT
bert_uncased_QAT is a text classification model from jysh1023. 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 18, 2023
- Updated
- Nov 18, 2023
- SHA
- 86a31358
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Base models
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. -->
bert_uncased_qat
This model is a fine-tuned version of google/bert_uncased_L-6_H-768_A-12 on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.2984
- Accuracy: 0.9094
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: 6e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2453 | 1.0 | 527 | 0.2552 | 0.8979 |
| 0.1257 | 2.0 | 1054 | 0.2997 | 0.8933 |
| 0.0818 | 3.0 | 1581 | 0.2984 | 0.9094 |
| 0.057 | 4.0 | 2108 | 0.3181 | 0.9048 |
| 0.0403 | 5.0 | 2635 | 0.3299 | 0.9083 |
| 0.0274 | 6.0 | 3162 | 0.4222 | 0.9060 |
| 0.0192 | 7.0 | 3689 | 0.4797 | 0.9083 |
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 18, 2023
- Updated
- Nov 18, 2023