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roberta-base-finetuned-cola
Jinchen/roberta-base-finetuned-cola
roberta-base-finetuned-cola is a text classification model from Jinchen. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Jul 13, 2022
- Updated
- Jul 15, 2022
- SHA
- d0a1a5d4
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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. -->
roberta-base-finetuned-cola
This model is a fine-tuned version of roberta-base on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.4211
- Matthews Correlation: 0.6279
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- training precision: Mixed Precision
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.4218 | 1.0 | 133 | 0.4236 | 0.5243 |
| 0.2077 | 2.0 | 266 | 0.3970 | 0.5930 |
| 0.184 | 3.0 | 399 | 0.4211 | 0.6279 |
| 0.1807 | 4.0 | 532 | 0.4854 | 0.6197 |
| 0.1405 | 5.0 | 665 | 0.5693 | 0.5968 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.3.2
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jul 13, 2022
- Updated
- Jul 15, 2022