https://github.com/yangalan123/datamap-project

https://github.com/yangalan123/datamap-project

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/yangalan123/datamap-project
• Owner: yangalan123
• Created: 2021-04-09T05:07:21.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2023-04-12T06:25:50.000Z (almost 2 years ago)
• Last Synced: 2024-04-20T13:04:22.294Z (9 months ago)
• Language: Python
• Size: 3.32 MB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# DataMap Pipeline for Active Learning and Data Verification

Author: Chenghao Yang ([email protected])

## Requirements (include those from DataMap)

- loguru == 0.3.0

- transformers == 4.0.1

- torch == 1.7.0

- tqdm == 4.43.0

- seaborn == 0.10.0

- pandas == 1.0.3

- matplotlib == 3.2.1

- numpy == 1.18.2

- jsonnet == 0.15.0

- tensorboardx == 2.0

- torch == 1.4.0

- spacy == 2.1.9

- scikit-learn == 0.22.2.post1

## How to Use

1. Read `config.py` and sample data in TestTask. Prepare your data as the same format as the sample data and doing necessary edits to `config.py`. Generally, you can play with the model hyper-parameter, seed, and the task name. Feel free to rename `TestTask/` to other names like `TASK_NAME/`, as long as you make corresponding changes in `config.py`.

1. Run `python main.py` to train the model. 

1. Run `python data_processor.py` to do necessary post-editing for data files generated in `TASK_NAME/`. Then run `python log_processor.py` to prepare the training dynamics data.

1. Switch to `cartography/` by the command `cd cartography`.

1. Run `python -m cartography.selection.train_dy_filtering --task_name TASK_NAME --data_dir ../ --plots_dir ../datamap_figures --model_dir ../ --filter --metric Metrics --plot` to plot the datamap figure and filter hard-to-learn / ambiguous data. For more  details about this command, please read README.md in `cartography/`.