microsoft-prophetnet-large-uncased-squad-qg
Version: 3
prophetnet-large-uncased-squad-qg
Fine-tuned weights(converted from original fairseq version repo ) for ProphetNet on question generation
SQuAD 1.1.
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.
ProphetNet is able to predict more future tokens with a n-stream decoder. The original implementation is Fairseq version at github repo .
Fine-tuned weights(converted from original fairseq version repo ) for ProphetNet on question generation
SQuAD 1.1.
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.
ProphetNet is able to predict more future tokens with a n-stream decoder. The original implementation is Fairseq version at github repo .
Usage
from transformers import ProphetNetTokenizer, ProphetNetForConditionalGeneration, ProphetNetConfig
model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased-squad-qg')
tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased-squad-qg')
FACT_TO_GENERATE_QUESTION_FROM = ""Bill Gates [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975."
inputs = tokenizer([FACT_TO_GENERATE_QUESTION_FROM], return_tensors='pt')
# Generate Summary
question_ids = model.generate(inputs['input_ids'], num_beams=5, early_stopping=True)
tokenizer.batch_decode(question_ids, skip_special_tokens=True)
# should give: 'along with paul allen, who founded microsoft?'
Citation
@article{yan2020prophetnet,
title={Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training},
author={Yan, Yu and Qi, Weizhen and Gong, Yeyun and Liu, Dayiheng and Duan, Nan and Chen, Jiusheng and Zhang, Ruofei and Zhou, Ming},
journal={arXiv preprint arXiv:2001.04063},
year={2020}
}
microsoft/prophetnet-large-uncased-squad-qg is a pre-trained language model available on the Hugging Face Hub. It's specifically designed for the text2text-generation task in the transformers library. If you want to learn more about the model's architecture, hyperparameters, limitations, and biases, you can find this information on the model's dedicated Model Card on the Hugging Face Hub . Model Specifications
Last UpdatedJuly 2025
ProviderHuggingFace