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bert-base-uncased-finetuned-cola
aysin/bert-base-uncased-finetuned-cola
bert-base-uncased-finetuned-cola is a text classification model from aysin. 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
- Apr 30, 2023
- Updated
- May 6, 2023
- SHA
- 1663e61f
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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-cola
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: 0.4500
- Matthews Correlation: 0.555170
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: 3.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- dropout: 0.18
- max_length: 16
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| No log | 1.0 | 268 | 0.4692 | 0.4912 |
| 0.4636 | 2.0 | 536 | 0.4500 | 0.5313 |
| 0.4636 | 3.0 | 804 | 0.4809 | 0.5233 |
| 0.01977 | 10.0 | - | - | 0.5552 |
| Average Training Accuracy: 99.553% | ||||
| Average Validation Accuracy: 82.69% |
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
- Apr 30, 2023
- Updated
- May 6, 2023