hindi2vec
State-of-the-Art Language Modeling and Text Classification in Hindi Language
Results
We achieved State of the Art Perplexity = 46.81 for Hindi compared to 40.68 for English (lower is better)
- To the best of my knowledge on September 18, 2018
Update: nlp-for-hindi uses sentencepiece instead of the word based spacCy tokenizer which I use. On those tokens, the measured perplexity for that LM is ~35. I encourage you to check that work out as well.
Downloads
- Pretrained Language Models that you can use in your classification for transfer learning
- EXCLUSIVE: BBC Hindi data of 4335 documents for text classification and text summarization. Release Notes
- Raw Data for Language Model shared above: Hindi Wikipedia with about 21k unique tokens for minfreq = 50
- Wikipedia Processed Data - please use this to train your model
TODO
- Language modeling based on wikipedia dump
- Release Language Models: Hindi Language Model
- Create Text classification Datasets: BBC Hindi
- Benchmark text classification with FastText
Idea Dump
- Change the custom head to be used for transliteration instead of classification, Hindi script (Devnagri) to English script (Roman)
- MTL tasks for training and inference using custom heads
- Text to Speech - using datasets from news recordings or Hindi subtitles of dubbed movies
FastAI Installation
This version of the notebook uses fastai lib's v0.7, used in their Part 2 v2 course in Summer 2018. The best way to install it via conda as mentioned here
Special thanks to Jeremy, Rachel and other contributors to fastai. This work is a reproduction of their work in English to Hindi. Thanks to @cstorm125 for thai2vec which inspired this work.