medical_jargons_simplifier

nadika/medical_jargons_simplifier

medical_jargons_simplifier 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 17, 2024
Updated: Jun 17, 2024
SHA: 3e77e273

Base models

luqh/ClinicalT5-base

Model card

From the Hugging Face model README

medical_jargons_simplifier

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: 0.4405

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: 2

Training results

Training Loss	Epoch	Step	Validation Loss
3.2542	0.2017	500	0.5136
0.5482	0.4034	1000	0.4729
0.5112	0.6051	1500	0.4599
0.5042	0.8068	2000	0.4532
0.5043	1.0085	2500	0.4488
0.5085	1.2102	3000	0.4448
0.4692	1.4119	3500	0.4432
0.495	1.6136	4000	0.4416
0.4798	1.8152	4500	0.4405

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1

Library: transformers
Type: t5
Created: Jun 17, 2024
Updated: Jun 17, 2024