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fine-tuned-MoritzLaurer-deberta-v3-base-zeroshot-v2.0-swag
This model is a fine-tuned version of MoritzLaurer/deberta-v3-base-zeroshot-v2.0 on the swag dataset. It achieves the following results on the evaluation set:
- Loss: 0.4214
- Accuracy: 0.8867
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: 1.5e-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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.591 | 1.0 | 4597 | 0.3253 | 0.8818 |
| 0.4143 | 2.0 | 9194 | 0.3045 | 0.8847 |
| 0.2994 | 3.0 | 13791 | 0.3439 | 0.8871 |
| 0.2106 | 4.0 | 18388 | 0.4214 | 0.8867 |
Framework versions
- Transformers 4.41.2
- Pytorch 1.11.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for amritpuhan/fine-tuned-MoritzLaurer-deberta-v3-base-zeroshot-v2.0-swag
Base model
microsoft/deberta-v3-base
# Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("amritpuhan/fine-tuned-MoritzLaurer-deberta-v3-base-zeroshot-v2.0-swag") model = AutoModelForMultipleChoice.from_pretrained("amritpuhan/fine-tuned-MoritzLaurer-deberta-v3-base-zeroshot-v2.0-swag")