chaii - hindi & tamil question answering

Last update: Dec 18, 2022

Overview

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

chaii - hindi & tamil question answering

Related tags

Overview

chaii - hindi & tamil question answering

Datasets required

Training

XLM-Roberta:

Rembert:

Muril-Large

Inference

Owner

abhishek thakur

Example code for "Real-World Natural Language Processing"

🧪 Cutting-edge experimental spaCy components and features

PIZZA - a task-oriented semantic parsing dataset

Text to speech for Vietnamese, ez to use, ez to update

An implementation of model parallel GPT-3-like models on GPUs, based on the DeepSpeed library. Designed to be able to train models in the hundreds of billions of parameters or larger.

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

This repository contains the code for "Generating Datasets with Pretrained Language Models".

Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision

SAINT PyTorch implementation

PyTorch implementation of the paper: Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding

neural network based speaker embedder

Explore different way to mix speech model(wav2vec2, hubert) and nlp model(BART,T5,GPT) together

Text classification on IMDB dataset using Keras and Bi-LSTM network

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

A Semi-Intelligent ChatBot filled with statistical and economical data for the Premier League.

Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

NLP, before and after spaCy

SciBERT is a BERT model trained on scientific text.

Open Source Neural Machine Translation in PyTorch

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis