https://github.com/steph1793/Pointer_Transformer_Generator

:scorpius::heavy_plus_sign::sagittarius::arrow_right::heavy_check_mark: Build a summarizer models combining transformers and pointing mechanism
https://github.com/steph1793/Pointer_Transformer_Generator

Last synced: 3 months ago
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:scorpius::heavy_plus_sign::sagittarius::arrow_right::heavy_check_mark: Build a summarizer models combining transformers and pointing mechanism

• Host: GitHub
• URL: https://github.com/steph1793/Pointer_Transformer_Generator
• Owner: steph1793
• License: mit
• Created: 2019-08-12T21:35:01.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-11-28T21:07:37.000Z (almost 5 years ago)
• Last Synced: 2024-06-14T01:46:37.708Z (5 months ago)
• Language: Python
• Homepage:
• Size: 64.5 KB
• Stars: 34
• Watchers: 3
• Forks: 6
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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• StarryDivineSky - steph1793/Pointer_Transformer_Generator

README

        
# Pointer_Transformer_Generator tensorflow 2.0.0

For the abstractive summarization task, I wanted to experiment the transformer model. I recreated a transformer model (thanks to tensorflow transformer tutorial) and added a pointer module (have a look at this paper for more informations on the pointer generator network : https://arxiv.org/abs/1704.04368 ).

PS : I will add very soon a section explaining the integration of the pointer module in the transformer

Please follow the next steps to launch the project :

## Step 1 : The data

### Option 1 : Download the data

Download the data (chunk files format : tfrecords)

https://drive.google.com/open?id=1uHrMWd7Pbs_-DCl0eeMxePbxgmSce5LO

### Option 2 : Download raw data and process it

Use this project : 

https://github.com/steph1793/CNN-DailyMail-Bin-To-TFRecords

## Step 2 : launch the project : 

**python main.py --max_enc_len=400 \ 


--max_dec_len=100 \ 


--batch_size=16 \ 


--vocab_size=50000 \ 


--num_layers=3 \ 


--model_depth=512 \ 


--num_heads=8 \ 


--dff=2048 \ 


--seed=123 \ 


--log_step_count_steps=1 \ 


--max_steps=230000 \ 


--mode=train \ 


--save_summary_steps=10000 \ 


--checkpoints_save_steps=10000 \ 


--model_dir=model_folder \ 


--data_dir=data_folder \ 


--vocab_path=vocab \ 
**

PS : Feel free to change some of the hyperparameters


python main.py --help , for more details on the hyperparameters

## Requirements

- python >= 3.6

- tensorflow 2.0.0

- argparse

- os

- glob

- numpy