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bert-base-uncased-finetuned-learningRate-2-cola-2e-05
yagmurery/bert-base-uncased-finetuned-learningRate-2-cola-2e-05
bert-base-uncased-finetuned-learningRate-2-cola-2e-05 is a text classification model from yagmurery. 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
- May 5, 2023
- Updated
- May 5, 2023
- SHA
- 3e916d03
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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. -->
bert-base-uncased-finetuned-learningRate-2-cola-2e-05
This model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.4685
- Matthews Correlation: 0.5823
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.4986 | 1.0 | 535 | 0.5249 | 0.4947 |
| 0.3134 | 2.0 | 1070 | 0.4685 | 0.5823 |
| 0.1964 | 3.0 | 1605 | 0.6025 | 0.5445 |
| 0.144 | 4.0 | 2140 | 0.7324 | 0.5699 |
| 0.0898 | 5.0 | 2675 | 0.8637 | 0.5720 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- May 5, 2023
- Updated
- May 5, 2023