Finetuning experiments
Collection
Using common-known datasets to finetune several models. Experiments are run in Colab.
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6 items
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Updated
finetuned-DebertaV3-squad-QuestionAnswering
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8714
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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.9391 | 1.0 | 334 | 0.8888 |
| 0.048 | 2.0 | 668 | 0.8080 |
| 0.4921 | 3.0 | 1002 | 0.8714 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for JinbiaoZhu/finetuned-DebertaV3-squad-QuestionAnswering
Base model
microsoft/deberta-v3-base