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bert-base-uncased-finetuned-glue_cola
markuscolab/bert-base-uncased-finetuned-glue_cola
bert-base-uncased-finetuned-glue_cola is a text classification model from markuscolab. 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 19, 2023
- SHA
- 66924378
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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-base-uncased-finetuned-glue_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: 0.6466
- Accuracy: 0.8293
- F1: 0.8202
- Matthews Correlation: 0.5806
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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Matthews Correlation |
|---|---|---|---|---|---|---|
| 0.5418 | 1.0 | 535 | 0.4594 | 0.8006 | 0.7836 | 0.5019 |
| 0.3635 | 2.0 | 1070 | 0.4437 | 0.8217 | 0.8084 | 0.5600 |
| 0.2019 | 3.0 | 1605 | 0.6466 | 0.8293 | 0.8202 | 0.5806 |
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 19, 2023