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microsoft-deberta-v3-large_cls_SentEval-CR
ghatgetanuj/microsoft-deberta-v3-large_cls_SentEval-CR
microsoft-deberta-v3-large_cls_SentEval-CR is a text classification model from ghatgetanuj. 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
- Dec 12, 2022
- Updated
- Dec 12, 2022
- SHA
- c6db8b1e
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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. -->
microsoft-deberta-v3-large_cls_SentEval-CR
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: 0.2799
- Accuracy: 0.9363
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: 4e-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: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 189 | 0.3706 | 0.9110 |
| No log | 2.0 | 378 | 0.2884 | 0.9243 |
| 0.3597 | 3.0 | 567 | 0.3018 | 0.9177 |
| 0.3597 | 4.0 | 756 | 0.1996 | 0.9363 |
| 0.3597 | 5.0 | 945 | 0.2799 | 0.9363 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Dec 12, 2022
- Updated
- Dec 12, 2022