GPT2-BioPT - a Language Model for Portuguese Biomedical text generation
Introduction
GPT2-BioPT (Portuguese Biomedical GPT-2 small) is a language model for Portuguese based on the OpenAI GPT-2 model, trained from the GPorTuguese-2 with biomedical literature.
We used Transfer Learning and Fine-tuning techniques with 110MB of training data, corresponding to 16,209,373 tokens and 729,654 sentences.
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in this paper and first released at this page (February 14, 2019).
Disclaimer: The team releasing GPT-2 also wrote a model card for their model. Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token i only uses the inputs from 1 to i but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.
How to use GPT2-BioPT with HuggingFace
from transformers import pipeline
chef = pipeline('text-generation',model="pucpr/gpt2-bio-pt", tokenizer="pucpr/gpt2-bio-pt",config={'max_length':800})
result = chef('O paciente chegou no hospital')[0]['generated_text']
print(result)
Resultado:
O paciente chegou no hospital três meses após a operação, não houve complicações graves. Entre os grupos que apresentaram maior número de lesões, o exame da cavidade pélvica estava significantemente associado à ausência de complicações. Foi encontrada uma maior incidência de fraturas (...)
Citation
@INPROCEEDINGS{9474713,
author={Schneider, Elisa Terumi Rubel and de Souza, João Vitor Andrioli and Gumiel, Yohan Bonescki and Moro, Claudia and Paraiso, Emerson Cabrera},
booktitle={2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)},
title={A GPT-2 Language Model for Biomedical Texts in Portuguese},
year={2021},
volume={},
number={},
pages={474-479},
doi={10.1109/CBMS52027.2021.00056}}
