chaii - hindi & tamil question answering
This is the solution for rank 5th in Kaggle competition: chaii - Hindi and Tamil Question Answering. The competition can be found here: https://www.kaggle.com/c/chaii-hindi-and-tamil-question-answering
Datasets required
Download squadv2 data from https://rajpurkar.github.io/SQuAD-explorer/
$ mkdir input && cd input
$ wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json
$ wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json
Download tydiqa data in the input folder:
$ wget https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-train.json
$ wget https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-dev.json
Download data from https://www.kaggle.com/tkm2261/google-translated-squad20-to-hindi-and-tamil to input folder
Download original competition dataset to input folder: https://www.kaggle.com/c/chaii-hindi-and-tamil-question-answering/data
Download outputs of this kernel: https://www.kaggle.com/rhtsingh/external-data-mlqa-xquad-preprocessing/ to input folder
Now, you have all the data needed to train the model. We will first create folds and munge the data a bit.
To create folds, please use the following command:
$ cd src
$ python create_folds.py
To munge the datasets and prepare for training, please run the following command:
$ cd src
$ python munge_data.py
Training
There are two GPU models and one model needs TPUs.
GPU models: XLM-Roberta & Rembert TPU model: Muril-Large
XLM-Roberta:
$ cd src
$ TOKENIZERS_PARALLELISM=false python xlm_roberta.py --fold 0
$ TOKENIZERS_PARALLELISM=false python xlm_roberta.py --fold 1
$ TOKENIZERS_PARALLELISM=false python xlm_roberta.py --fold 2
$ TOKENIZERS_PARALLELISM=false python xlm_roberta.py --fold 3
$ TOKENIZERS_PARALLELISM=false python xlm_roberta.py --fold 4
Rembert:
$ cd src
$ TOKENIZERS_PARALLELISM=false python rembert.py --fold 0
$ TOKENIZERS_PARALLELISM=false python rembert.py --fold 1
$ TOKENIZERS_PARALLELISM=false python rembert.py --fold 2
$ TOKENIZERS_PARALLELISM=false python rembert.py --fold 3
$ TOKENIZERS_PARALLELISM=false python rembert.py --fold 4
Muril-Large
** please note that training this model needs TPUs **
$ cd src
$ TOKENIZERS_PARALLELISM=false python muril_large.py --fold 0
$ TOKENIZERS_PARALLELISM=false python muril_large.py --fold 1
$ TOKENIZERS_PARALLELISM=false python muril_large.py --fold 2
$ TOKENIZERS_PARALLELISM=false python muril_large.py --fold 3
$ TOKENIZERS_PARALLELISM=false python muril_large.py --fold 4
Inference
After training all the models, the outputs were pushed to Kaggle Datasets.
The final model datasets can be found here:
- https://www.kaggle.com/abhishek/xlmrobertalargewithsquadv2tydiqasqdtrans384f
- https://www.kaggle.com/ubamba98/modelsrembertwithsquadv2tydiqa384
- https://www.kaggle.com/ubamba98/murillargecasedchaii
And the final inference kernel can be found here: https://www.kaggle.com/abhishek/chaii-xlm-roberta-x-muril-x-rembert-score-based
Solution writeup: https://www.kaggle.com/c/chaii-hindi-and-tamil-question-answering/discussion/288049
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