1 Line to train a classifier for Reddit Sentiment, Amazone Phone reviews in 100+ languages, and much more with NLU 1.1.4!
NLU 1.1.4 Release Notes - Classify Reddit Sentiment, Amazone Phone reviews in 100+ languages, and much more with NLU 1.1.4!
We are very excited to announce NLU 1.1.4 has been released and comes with a lot of tutorials showcasing how you can train a multilingual text classifier on just one starting language which then will be able to classify labels correct for text in over 100+ languages.
This is possible by leveraging the language-agnostic BERT Sentence Embeddings(LABSE). In addition to that tutorials for English pure classifiers for stock market sentiment, sarcasm and negations have been added.
Finally, this release makes working in Spark environments easier, by providing a return_spark_df directly from NLU predictions.
New Features
- parameter on the
predict()method onnlu.load(). You can now callnlu.load(model).predict('Some data',return_spark_df=True)and will recieve a spark dataframe
New NLU Multi-Lingual training tutorials
These notebooks showcase how to leverage the powerful language-agnostic BERT Sentence Embeddings(LABSE) to train a language-agnostic classifier.
You can train on one start language(i.e. English dataset) and your model will be able to correctly predict the labels in every one of the 100+ languages of the LABSE embeddings.
- Multilingual Twitter Sentiment, binary classification (2class)
- Multilingual Stock Market Sentiment, binary classification (2class)
- Multilingual Reddit Comments Sentiment, binary classification (2class)
- Multilingual COVID19 Sentiment, binary classification (2class)
- Multilingual Apple Tweets Sentiment, binary classification (2class)
- Multilingual News classification, multi class classification (4class)
- Multilingual TripAdvisor Hotel Reviews, multi-class classification (3class)
- Multilingual Amazon Phone Reviews, multi-class classification (3class)
New NLU training tutorials (English)
These are simple training notebooks for binary classification for English
- Biological Texts Negation, binary classification (2class)
- News Headlines Sarcasm, binary classification (2class)
- COVID19 Sentiment, binary classification (2class)
- Natural Disasters Sentiment, binary classification (2class)
- Stock Market Sentiment, binary classification (2class)