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distilbert-base-uncased-finetuned-cola
bird2889801/distilbert-base-uncased-finetuned-cola
distilbert-base-uncased-finetuned-cola is a text classification model from bird2889801. 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
- distilbert
- Access
- Public
- Created
- Mar 4, 2024
- Updated
- Mar 4, 2024
- SHA
- 84725931
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Base models
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. -->
distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8408
- Matthews Correlation: 0.5403
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: 1.8855671484783746e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 19
- 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 |
|---|---|---|---|---|
| 0.5253 | 1.0 | 535 | 0.4775 | 0.4464 |
| 0.3521 | 2.0 | 1070 | 0.4734 | 0.5022 |
| 0.2444 | 3.0 | 1605 | 0.6442 | 0.5136 |
| 0.1749 | 4.0 | 2140 | 0.7873 | 0.5161 |
| 0.1256 | 5.0 | 2675 | 0.8408 | 0.5403 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 4, 2024
- Updated
- Mar 4, 2024