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v3-Large-mnli
NDugar/v3-Large-mnli
v3-Large-mnli is a zero-shot classification model from NDugar. Use it when you need labels you did not train the model on. It is set up for transformers. The card lists the license as mit.
This model is a fine-tuned version of microsoft/deberta-v3-large on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4103 - Accuracy: 0.9175
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From the Hugging Face model README
This model is a fine-tuned version of microsoft/deberta-v3-large on the GLUE MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.4103
- Accuracy: 0.9175
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3631 | 1.0 | 49088 | 0.3129 | 0.9130 |
| 0.2267 | 2.0 | 98176 | 0.4157 | 0.9153 |
Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3
- Task
- Zero-Shot Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Languages
- en
- Created
- Mar 2, 2022
- Updated
- Nov 22, 2023