https://github.com/alexeykarnachev/kaggle_google_qa_labeling
41th place of the Kaggle Google QA Labeling competition
https://github.com/alexeykarnachev/kaggle_google_qa_labeling
Last synced: 20 days ago
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41th place of the Kaggle Google QA Labeling competition
- Host: GitHub
- URL: https://github.com/alexeykarnachev/kaggle_google_qa_labeling
- Owner: alexeykarnachev
- Created: 2020-02-11T20:49:27.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-06-12T21:27:43.000Z (almost 2 years ago)
- Last Synced: 2025-05-12T19:23:28.934Z (20 days ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 4.72 MB
- Stars: 2
- Watchers: 2
- Forks: 3
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# Kaggle Google QA Labeling Competition
## Here is solution of 41th place of the Kaggle Google QA Labeling competition
1. Install the package
`pip install -e .`
2. Download NER-Model from Kaggle
https://www.kaggle.com/alexeykarnachev/google-qa-ner
3. Prepare dataset
`python scripts/prepare_dataset.py --seed=228 --train_df_file=data/google-quest-challenge/train.csv --test_df_file=data/google-quest-challenge/test.csv --tokenizer_cls=RobertaTokenizer --tokenizer_path=roberta-large --n_splits=7 --datasets_root=data/datasets/ --crop_strategies=both --dataset_cls=BiDataset --process_math --ner_model_dir=data/ner/code/bert_base_cased`
--ner_model_dir is a path to downloaded NER model (from previous step)
4. Run experiment training
`cd scripts`
`python run_encoder_experiment.py --config_path=../configs/base_config.yaml`
5. Wait the training process end ...
6. Archive the experiment directory
`cd experiments`
`tar zcvf .tar.gz `
7. Send it to your kaggle datasets storage
8. Now, you can inference the model in a kernel
https://www.kaggle.com/alexeykarnachev/kernel1864bcfc13
For this, attach the following datasets to the kernel:
https://www.kaggle.com/alexeykarnachev/kaggle-google-qa-labeling (this package)
https://www.kaggle.com/alexeykarnachev/google-qa-ner (NER model)
https://www.kaggle.com/alexeykarnachev/transformersdependencies (transformers lib and dependencies)
Also, attach trained experiment to the kernel
Uncomment all lines in the kernel and replace the EXPERIMENT_NAME placeholder with your experiment name