Scifive Base PMC

🚀 SciFive PMC Base

SciFive PMC Base is a text - to - text transformer model designed for various biomedical literature processing tasks, offering capabilities in token - classification, text - classification, question - answering, and text generation.

🚀 Quick Start

For more details, do check out our Github repo.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-base-PMC")  
model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-base-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)

📚 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

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