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medqa_fine_tuned_linkbert
Shaier/medqa_fine_tuned_linkbert
medqa_fine_tuned_linkbert is a multiple choice model from Shaier. Use it for the multiple choice task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- multiple choice
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Jul 12, 2022
- Updated
- Jul 12, 2022
- SHA
- e5f165a0
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. -->
medqa_fine_tuned
This model is a fine-tuned version of michiyasunaga/BioLinkBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4462
- Accuracy: 0.4002
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 318 | 1.3208 | 0.3553 |
| 1.2802 | 2.0 | 636 | 1.3428 | 0.3703 |
| 1.2802 | 3.0 | 954 | 1.3780 | 0.3892 |
| 1.1466 | 4.0 | 1272 | 1.4234 | 0.3978 |
| 1.052 | 5.0 | 1590 | 1.4462 | 0.4002 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.11.0
- Task
- multiple choice
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jul 12, 2022
- Updated
- Jul 12, 2022