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mdeberta-v3-base-mnli-100
tmnam20/mdeberta-v3-base-mnli-100
mdeberta-v3-base-mnli-100 is a text classification model from tmnam20. 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
- Jan 16, 2024
- Updated
- Jan 16, 2024
- SHA
- 4e349814
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Base models
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. -->
mdeberta-v3-base-mnli-100
This model is a fine-tuned version of microsoft/mdeberta-v3-base on the tmnam20/VieGLUE/MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.4764
- Accuracy: 0.8412
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: 32
- eval_batch_size: 16
- seed: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5194 | 0.41 | 5000 | 0.4901 | 0.8127 |
| 0.4861 | 0.81 | 10000 | 0.4713 | 0.8114 |
| 0.3993 | 1.22 | 15000 | 0.4508 | 0.8285 |
| 0.3867 | 1.63 | 20000 | 0.4546 | 0.8302 |
| 0.3496 | 2.04 | 25000 | 0.4765 | 0.8295 |
| 0.3376 | 2.44 | 30000 | 0.4828 | 0.8315 |
| 0.3104 | 2.85 | 35000 | 0.4852 | 0.8314 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.2.0.dev20231203+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Languages
- en
- Created
- Jan 16, 2024
- Updated
- Jan 16, 2024