mlb-data-scripts

KO | EN

gitlite — search

#python #llm #android #machine-learning #deep-learning #macos #nodejs #java #awesome #cli #kubernetes #awesome-list #security #open-source

mlb-data-scripts

(★ 14)

Scripts to create the MLB dataset introduced in the paper Data-to-text Generation with Entity Modeling

mlb-data-scripts 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# mlb-data

This repo contains scripts to create the **MLB** dataset introduced in the paper [**Data-to-text Generation with Entity Modeling**](https://arxiv.org/abs/1906.03221) (Puduppully, R., Dong, L., & Lapata, M.; ACL 2019).

# Prerequisites
- Install the [mlbgame-api](https://github.com/ratishsp/mlbgame-api)
```
pip install git+https://github.com/ratishsp/mlbgame-api.git
```

# Steps to create the dataset
Run the following scripts in sequence
- [boxscore_data.py](https://github.com/ratishsp/mlb-data-scripts/blob/master/boxscore_data.py). It requires the argument '-year'. The values to be passed are 0, 1, 2..10. For 0 it will collect the records for the year 2018, for 1 the year 2017 and so on.
```
python boxscore_data.py -year 1 -output ~/mlb-data/api-output/  # get the data for year 2017
```
Alternatively you can download the dataset containing box/line/play-by-play scores from https://drive.google.com/drive/folders/1jLU5wYjic2BR21iOLn9Tkv415AWkFqfj?usp=sharing
 
- [extract_summaries_from_recap_html.py](https://github.com/ratishsp/mlb-data-scripts/blob/master/extract_summaries_from_recap_html.py) extracts the recaps from the html. The names of the htmls to be downloaded is available in the file [recap_file_names.txt](https://github.com/ratishsp/mlb-data-scripts/blob/master/recap_file_names.txt)
 ```
python extract_summaries_from_recap_html -recaps ~/mlb-data/recap_file_names.txt -output_folder ~/mlb-data/html-output/
 ```

- [clean_summaries.py](https://github.com/ratishsp/mlb-data-scripts/blob/master/clean_summaries.py) cleans the html of quotations and text incidental to the game.
 ```
python clean_summaries.py -input_folder ~/mlb-data/html-output/ -output_folder ~/mlb-data/html-output-cleaned/
 ```
 - [create_combined_dataset.py](https://github.com/ratishsp/mlb-data-scripts/blob/master/create_combined_dataset.py) results in a dataset with boxscores and summaries.
 ```
python create_combined_dataset.py -input_folder ~/mlb-data/api-output/ -input_summaries ~/mlb-data/html-output-cleaned/ -output_folder ~/mlb-data/combined/
 ```
- [preproc.py](https://github.com/ratishsp/mlb-data-scripts/blob/master/preproc.py) preprocesses the dataset into train, validation and test splits. The splits are defined in the file [mlb_split_keys.txt](https://github.com/ratishsp/mlb-data-scripts/blob/master/mlb_split_keys.txt).
```
python preproc.py -input ~/mlb-data/combined/ -mlb_split_keys ~/mlb-data/mlb_split_keys.txt -output ~/mlb-data/splits/
```
Alternatively you can download the json files from https://drive.google.com/drive/folders/1G4iIE-02icAU2-5skvLlTEPWDQQj1ss4?usp=sharing

mlb-data-scripts

mlb-data-scripts 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

mlb-data-scripts

mlb-data-scripts 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README