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medical_jargons_simplifier2
nadika/medical_jargons_simplifier2
medical_jargons_simplifier2 is a machine learning model from nadika. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- t5
- Access
- Public
- Created
- Jun 20, 2024
- Updated
- Jun 20, 2024
- SHA
- fa882acd
Base models
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. -->
medical_jargons_simplifier2
This model is a fine-tuned version of luqh/ClinicalT5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4641
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 10.6338 | 0.3378 | 50 | 5.9582 |
| 3.6156 | 0.6757 | 100 | 1.0741 |
| 1.3304 | 1.0135 | 150 | 0.8368 |
| 1.0096 | 1.3514 | 200 | 0.7519 |
| 0.933 | 1.6892 | 250 | 0.7019 |
| 0.8178 | 2.0270 | 300 | 0.6586 |
| 0.7714 | 2.3649 | 350 | 0.6188 |
| 0.7077 | 2.7027 | 400 | 0.5924 |
| 0.7406 | 3.0405 | 450 | 0.5673 |
| 0.6601 | 3.3784 | 500 | 0.5531 |
| 0.6637 | 3.7162 | 550 | 0.5388 |
| 0.6489 | 4.0541 | 600 | 0.5281 |
| 0.6369 | 4.3919 | 650 | 0.5187 |
| 0.5996 | 4.7297 | 700 | 0.5109 |
| 0.5816 | 5.0676 | 750 | 0.5028 |
| 0.5714 | 5.4054 | 800 | 0.4961 |
| 0.5826 | 5.7432 | 850 | 0.4910 |
| 0.5646 | 6.0811 | 900 | 0.4855 |
| 0.5379 | 6.4189 | 950 | 0.4827 |
| 0.5586 | 6.7568 | 1000 | 0.4785 |
| 0.5408 | 7.0946 | 1050 | 0.4751 |
| 0.5576 | 7.4324 | 1100 | 0.4727 |
| 0.5241 | 7.7703 | 1150 | 0.4710 |
| 0.5298 | 8.1081 | 1200 | 0.4695 |
| 0.5424 | 8.4459 | 1250 | 0.4677 |
| 0.5038 | 8.7838 | 1300 | 0.4665 |
| 0.5545 | 9.1216 | 1350 | 0.4653 |
| 0.523 | 9.4595 | 1400 | 0.4644 |
| 0.5029 | 9.7973 | 1450 | 0.4641 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
- Library
- transformers
- Type
- t5
- Created
- Jun 20, 2024
- Updated
- Jun 20, 2024