Open-source Text Summarization Model t5-small-finetuned-pubmed - Free Support for Medical Literature Abstract Generation

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T5 Small Finetuned Pubmed

Developed by andkelly21

A text summarization generation model fine-tuned on the PubMed abstract dataset based on the t5-small model

Text Generation

Transformers

Open Source License:Apache-2.0 #Medical Abstract Generation #PubMed Fine-tuning #Clinical Case Analysis

Downloads 18

Release Time : 12/25/2022

Model Overview

This model is a fine-tuned version of the t5-small base model on the PubMed abstract dataset, primarily used for medical literature abstract generation tasks.

Model Features

Medical Domain Optimization

Fine-tuned on the PubMed medical abstract dataset, optimized for medical text summarization tasks

Lightweight Model

Based on the t5-small architecture with a moderate parameter size, suitable for resource-limited environments

Structured Output

Capable of generating structured medical case summaries

Model Capabilities

Medical Text Summarization Generation

Clinical Case Report Generation

Medical Literature Information Extraction

Use Cases

Medical Research

Clinical Case Summaries

Convert detailed case reports into concise summaries

As shown in the example, it can effectively extract key medical information

Medical Literature Processing

Automatically generate medical paper abstracts

Medical Information Systems

Electronic Medical Record Processing

Extract key information from electronic medical records to generate summaries

🚀 t5-small-finetuned-pubmed

This model is a fine - tuned version of [t5 - small](https://huggingface.co/t5 - small) on a truncated [PubMed Summarization](https://huggingface.co/datasets/ccdv/pubmed - summarization) dataset, aiming to provide high - quality summarization results.

🚀 Quick Start

This model is a fine - tuned version of [t5 - small](https://huggingface.co/t5 - small) on a truncated [PubMed Summarization](https://huggingface.co/datasets/ccdv/pubmed - summarization) dataset. It achieves the following results on the evaluation set:

Loss: 2.7252
Rouge1: 19.4457
Rouge2: 3.125
Rougel: 18.3168
Rougelsum: 18.5625

✨ Features

Medical Case Example:
- A medical case is presented where a 67 - year - old male patient with a history of hypertension, obesity, and hyperlipidemia received the Pfizer COVID - 19 vaccine. He later developed an acute myocardial infarction (AMI), leading to cardiogenic shock and multi - organ failure, and unfortunately passed away. This case was reported to the Vaccine Adverse Event Reporting System (VAERS) for further investigation.

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5.6e - 05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
3.2735	1.0	13	2.9820	18.745	3.7918	15.7876	15.8512
3.0428	2.0	26	2.8828	17.953	2.5	15.49	15.468
2.6259	3.0	39	2.8283	21.5532	5.9278	19.7523	19.9232
3.0795	4.0	52	2.7910	20.9244	5.9278	19.8685	20.0181
2.8276	5.0	65	2.7613	20.6403	3.125	18.0574	18.2227
2.64	6.0	78	2.7404	19.4457	3.125	18.3168	18.5625
2.5525	7.0	91	2.7286	19.4457	3.125	18.3168	18.5625
2.4951	8.0	104	2.7252	19.4457	3.125	18.3168	18.5625

Framework versions

Transformers 4.25.1
Pytorch 1.13.0
Datasets 2.8.0
Tokenizers 0.13.2

📄 License

The model is licensed under the Apache 2.0 license.

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