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t5-medical-text-simplification
esahit/t5-medical-text-simplification
t5-medical-text-simplification is a machine learning model from esahit. 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. 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
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Feb 2, 2024
- Updated
- Feb 2, 2024
- SHA
- 70236b3c
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. -->
t5-medical-text-simplification
This model is a fine-tuned version of mrm8488/t5-small-finetuned-text-simplification on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4158
- Bleu: {'bleu': 0.24913061085239344, 'precisions': [0.6300697552884507, 0.46170603353322726, 0.3783389479827051, 0.3190805662507599], 'brevity_penalty': 0.5754971743889961, 'length_ratio': 0.6441136869219061, 'translation_length': 44011, 'reference_length': 68328}
- Sari: {'sari': 21.772869578730884}
- Fkgl: 10.2474
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: 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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Sari | Fkgl |
|---|---|---|---|---|---|---|
| 1.5524 | 1.0 | 1578 | 1.4317 | {'bleu': 0.24854970426705067, 'precisions': [0.626776178839714, 0.45794346978557504, 0.37443247809101465, 0.3154227136604469], 'brevity_penalty': 0.5792493345645447, 'length_ratio': 0.646821215314366, 'translation_length': 44196, 'reference_length': 68328} | {'sari': 21.542679628603977} | 10.2949 |
| 1.5282 | 2.0 | 3156 | 1.4249 | {'bleu': 0.24886563197246125, 'precisions': [0.6285792076961474, 0.4604086221222934, 0.3770192256766061, 0.3176616771658094], 'brevity_penalty': 0.5767757332645675, 'length_ratio': 0.6450357101042032, 'translation_length': 44074, 'reference_length': 68328} | {'sari': 21.665573517166536} | 10.2937 |
| 1.4997 | 3.0 | 4734 | 1.4176 | {'bleu': 0.24852094682922746, 'precisions': [0.629403208945048, 0.4605591734808794, 0.377421066595914, 0.3182660566398332], 'brevity_penalty': 0.5753144561890373, 'length_ratio': 0.6439819693244351, 'translation_length': 44002, 'reference_length': 68328} | {'sari': 21.700716936778782} | 10.2544 |
| 1.5028 | 4.0 | 6312 | 1.4176 | {'bleu': 0.24876653336273433, 'precisions': [0.6299538437052363, 0.4615309246785058, 0.37816241471767237, 0.3188943296728769], 'brevity_penalty': 0.5748880487421792, 'length_ratio': 0.6436746282636694, 'translation_length': 43981, 'reference_length': 68328} | {'sari': 21.750120178010484} | 10.2531 |
| 1.4976 | 5.0 | 7890 | 1.4158 | {'bleu': 0.24913061085239344, 'precisions': [0.6300697552884507, 0.46170603353322726, 0.3783389479827051, 0.3190805662507599], 'brevity_penalty': 0.5754971743889961, 'length_ratio': 0.6441136869219061, 'translation_length': 44011, 'reference_length': 68328} | {'sari': 21.772869578730884} | 10.2474 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Feb 2, 2024
- Updated
- Feb 2, 2024