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
552 Nov 28, 2022
1 Oct 18, 2021
20 Dec 15, 2022
5 Dec 15, 2022
884 Nov 11, 2022
20 Aug 22, 2022
4 Jul 01, 2022
74 Jan 04, 2023
29 Jan 05, 2023
66 Dec 14, 2022
12 Feb 16, 2022
30 Dec 13, 2022
64 Nov 22, 2022
172 Dec 29, 2022
8 Dec 12, 2022
3 Apr 18, 2022
537 Jan 05, 2023
21 Dec 11, 2022
1 Feb 09, 2022
1 Jan 06, 2022