julien-c HF staff commited on
Commit
387a8e2
1 Parent(s): 81e1db4

Migrate model card from transformers-repo

Browse files

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/microsoft/prophetnet-large-uncased-squad-qg/README.md

Files changed (1) hide show
  1. README.md +39 -0
README.md ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: en
3
+ datasets:
4
+ - squad
5
+ ---
6
+
7
+ ##
8
+ prophetnet-large-uncased-squad-qg
9
+ Fine-tuned weights(converted from [original fairseq version repo](https://github.com/microsoft/ProphetNet)) for [ProphetNet](https://arxiv.org/abs/2001.04063) on question generation
10
+ SQuAD 1.1.
11
+ ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.
12
+ ProphetNet is able to predict more future tokens with a n-stream decoder. The original implementation is Fairseq version at [github repo](https://github.com/microsoft/ProphetNet).
13
+
14
+ ### Usage
15
+ ```
16
+ from transformers import ProphetNetTokenizer, ProphetNetForConditionalGeneration, ProphetNetConfig
17
+
18
+ model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased-squad-qg')
19
+ tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased-squad-qg')
20
+
21
+ FACT_TO_GENERATE_QUESTION_FROM = ""Bill Gates [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975."
22
+
23
+ inputs = tokenizer([FACT_TO_GENERATE_QUESTION_FROM], return_tensors='pt')
24
+
25
+ # Generate Summary
26
+ question_ids = model.generate(inputs['input_ids'], num_beams=5, early_stopping=True)
27
+ tokenizer.batch_decode(question_ids, skip_special_tokens=True)
28
+
29
+ # should give: 'along with paul allen, who founded microsoft?'
30
+ ```
31
+ ### Citation
32
+ ```bibtex
33
+ @article{yan2020prophetnet,
34
+ title={Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training},
35
+ author={Yan, Yu and Qi, Weizhen and Gong, Yeyun and Liu, Dayiheng and Duan, Nan and Chen, Jiusheng and Zhang, Ruofei and Zhou, Ming},
36
+ journal={arXiv preprint arXiv:2001.04063},
37
+ year={2020}
38
+ }
39
+ ```