medical_jargons_simplifierT5 – AI Model by nadika | AlphaNeural AI

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nadika/
medical_jargons_simplifierT5

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text2text-generation

generated_from_trainer

luqh/ClinicalT5-base

text-generation-inference

endpoints_compatible

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medical_jargons_simplifierT5

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: 0.4734

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: 50
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
3.3369	0.2198	500	0.5700
0.601	0.4396	1000	0.5279
0.572	0.6593	1500	0.5137
0.5556	0.8791	2000	0.5051
0.5132	1.0989	2500	0.4991
0.5406	1.3187	3000	0.4941
0.513	1.5385	3500	0.4909
0.5328	1.7582	4000	0.4880
0.5304	1.9780	4500	0.4846
0.5215	2.1978	5000	0.4825
0.5296	2.4176	5500	0.4811
0.5143	2.6374	6000	0.4799
0.4768	2.8571	6500	0.4780
0.513	3.0769	7000	0.4775
0.4933	3.2967	7500	0.4761
0.4891	3.5165	8000	0.4761
0.5022	3.7363	8500	0.4749
0.523	3.9560	9000	0.4743
0.5233	4.1758	9500	0.4742
0.5004	4.3956	10000	0.4738
0.4817	4.6154	10500	0.4733
0.4848	4.8352	11000	0.4734

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1