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vipubmed-deberta-xsmall-finetuned-squad
namngo/vipubmed-deberta-xsmall-finetuned-squad
vipubmed-deberta-xsmall-finetuned-squad is a question answering model from namngo. Use it when the input is a question plus a passage. 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
- Question Answering
- Library
- transformers
- License
- mit
- Model type
- deberta-v2
- Access
- Public
- Created
- Aug 11, 2023
- Updated
- Aug 17, 2023
- SHA
- c76ab7b3
Try a prompt
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. -->
vipubmed-deberta-xsmall-finetuned-squad
This model is a fine-tuned version of manhtt-079/vipubmed-deberta-xsmall on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.4768
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: 6e-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
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.6181 | 1.0 | 1196 | 1.9617 |
| 1.6439 | 2.0 | 2392 | 1.5840 |
| 1.2607 | 3.0 | 3588 | 1.6354 |
| 1.0223 | 4.0 | 4784 | 1.6411 |
| 0.7925 | 5.0 | 5980 | 1.7369 |
| 0.6184 | 6.0 | 7176 | 1.8626 |
| 0.5019 | 7.0 | 8372 | 2.1214 |
| 0.4125 | 8.0 | 9568 | 2.2120 |
| 0.321 | 9.0 | 10764 | 2.3238 |
| 0.2899 | 10.0 | 11960 | 2.4768 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
- Task
- Question Answering
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Aug 11, 2023
- Updated
- Aug 17, 2023