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deberta-v3-small-finetuned-sst2-finetuned-cola
rdp99/deberta-v3-small-finetuned-sst2-finetuned-cola
deberta-v3-small-finetuned-sst2-finetuned-cola is a text classification model from rdp99. 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
- deberta-v2
- Access
- Public
- Created
- Mar 5, 2024
- Updated
- Mar 5, 2024
- SHA
- eb648f4c
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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. -->
deberta-v3-small-finetuned-sst2-finetuned-cola
This model is a fine-tuned version of mrm8488/deberta-v3-small-finetuned-sst2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6941
- Matthews Correlation: 0.1319
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 |
|---|---|---|---|---|
| 0.6105 | 1.0 | 535 | 0.6169 | 0.0 |
| 0.6054 | 2.0 | 1070 | 0.6131 | 0.0315 |
| 0.5652 | 3.0 | 1605 | 0.6219 | 0.0802 |
| 0.5163 | 4.0 | 2140 | 0.6941 | 0.1319 |
| 0.4718 | 5.0 | 2675 | 0.7269 | 0.1110 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Mar 5, 2024
- Updated
- Mar 5, 2024