Code and data for paper ACL 2020 Paper "Few-Shot NLG with Pre-Trained Language Model" https://arxiv.org/abs/1904.09521
pip install -r requirements.txt
Data and pre-trained GPT-2 can be downloaded via dropbox: https://www.dropbox.com/sh/u3t8yhcctqczpo0/AAAZV7S-qoIyaQW99r_88nUra?dl=0
-- data_release
---- original: full datasets for each domain
---- humans / books / songs: datasets for each domain. We provide an example processed data for 100 training examples, in preprocessed_data folder, that you can directly train the model with. To get training data of other sizes, you can go to the original_data folder to sample training sets from sample_source.box and sample_source.summary, e.g., head -n 200 sample_source.box > train.box ; head -n 200 sample_source.summary > train.summary, and then run data preprocessing to generate preprocessed data.
-- models: pre-trained GPT-2
To run our code, go to the code folder and run with:
Data preprocessing:
python preprocess.py ~/Data/NLP/few_shot_nlg/ humans
Training:
python ./Main.py --root_path ~/Data/NLP/few_shot_nlg/ --domain humans --gpt_model_name ../models/117M/ --output_path ~/Output/
Where the root path is the data folder. Specify an output path to store the results.
If you find our work helpful, please cite the arxiv version.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent