SciFive-large-Pubmed_PMC Open-Source Model - Empowering Biomedical Literature Processing, Efficient and Practical!

Scifive Large Pubmed PMC

Developed by razent

SciFive is a Transformer-based text-to-text model specifically optimized for biomedical literature processing tasks, trained on PubMed and PMC datasets.

Large Language Model

Transformers

English#Biomedical Text Generation #PubMed/PMC Pre-training #Scientific Literature Q&A

Downloads 1,256

Release Time : 3/2/2022

Model Overview

This model focuses on text processing tasks in the biomedical field, including text classification, question answering systems, and text generation, aiming to help researchers process and analyze biomedical literature more efficiently.

Model Features

Specialized for Biomedical Field

Specifically trained and optimized for biomedical literature, demonstrating stronger performance in this domain

Multi-task Processing Capability

Capable of handling various natural language processing tasks including text classification, question answering, and text generation

Based on Large-scale Biomedical Corpus

Trained on PubMed and PMC, two major biomedical literature databases, possessing rich domain knowledge

Model Capabilities

Biomedical text classification

Biomedical question answering system

Biomedical text generation

Biomedical literature summarization

Biomedical terminology recognition

Use Cases

Medical Research

Automatic Literature Summarization

Automatically generate abstracts for biomedical research papers

Improves researchers' literature reading efficiency

Medical Q&A System

Answer questions about specific medical concepts or research

Assists in medical knowledge acquisition

Drug Development

Drug Interaction Analysis

Extract drug interaction information from literature

Accelerates drug development process

🚀 SciFive Pubmed+PMC Large

A text-to-text transformer model for biomedical literature, suitable for multiple NLP tasks in the biomedical field.

🚀 Quick Start

This model, SciFive Pubmed+PMC Large, is a powerful text - to - text transformer model in the biomedical domain. It can be applied to various natural language processing tasks such as token classification, text classification, question - answering, text2text generation, and text generation.

✨ Features

Multiple Task Support: Capable of handling token - classification, text - classification, question - answering, text2text - generation, and text - generation tasks.
Rich Training Data: Trained on datasets like pubmed and pmc/open_access, which are rich in biomedical literature.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-large-Pubmed_PMC")  
model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-large-Pubmed_PMC")

sentence = "Identification of APC2 , a homologue of the adenomatous polyposis coli tumour suppressor ."
text =  sentence + " </s>"

encoding = tokenizer.encode_plus(text, pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")

outputs = model.generate(
    input_ids=input_ids, attention_mask=attention_masks,
    max_length=256,
    early_stopping=True
)

for output in outputs:
    line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
    print(line)

Advanced Usage

For more details, do check out our Github repo.

📚 Documentation

Introduction

Paper: SciFive: a text - to - text transformer model for biomedical literature

Authors: Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan - Bonnet

📄 License

No license information is provided in the original document.

Property	Details
Tags	token - classification, text - classification, question - answering, text2text - generation, text - generation
Datasets	pubmed, pmc/open_access

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