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deberta-reward-model
cheonkamjeong/deberta-reward-model
deberta-reward-model is a text classification model from cheonkamjeong. 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
- Oct 6, 2024
- Updated
- Oct 6, 2024
- SHA
- 0ff97b40
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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-reward-model
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0095
- Accuracy: 0.995
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: 1.41e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0433 | 2.0 | 100 | 0.0274 | 0.9875 |
| 0.0017 | 4.0 | 200 | 0.0105 | 0.995 |
| 0.0001 | 6.0 | 300 | 0.0095 | 0.995 |
| 0.0001 | 8.0 | 400 | 0.0090 | 0.995 |
| 0.0002 | 10.0 | 500 | 0.0095 | 0.995 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Oct 6, 2024
- Updated
- Oct 6, 2024