NLU release notes

 

1.0.3 Release Notes

We are happy to announce NLU 1.0.3 comes with a lot new features, training classifiers, saving them and loading them offline, enabling running NLU with no internet connection, new notebooks and articles!

NLU 1.0.3 New Features

  • Train a Deep Learning classifier in 1 line! The popular ClassifierDL which can achieve state of the art results on any multi class text classification problem is now trainable! All it takes is just nlu.load(‘train.classifier).fit(dataset) . Your dataset can be a Pandas/Spark/Modin/Ray/Dask dataframe and needs to have a column named x for text data and a column named y for labels
  • Saving pipelines to HDD is now possible with nlu.save(path)
  • Loading pipelines from disk now possible with nlu.load(path=path).
  • NLU offline mode: Loading from disk makes running NLU offline now possible, since you can load pipelines/models from your local hard drive instead of John Snow Labs AWS servers.

NLU 1.0.3 New Notebooks and Tutorials

NLU 1.0.3 Bug fixes

  • Sentence Detector bugfix

NLU 1.0.2 Release Notes

We are glad to announce nlu 1.0.2 is released!

NLU 1.0.2 Enhancements

  • More semantically concise output levels sentence and document enforced :
    • If a pipe is set to output_level=’document’ :
      • Every Sentence Embedding will generate 1 Embedding per Document/row in the input Dataframe, instead of 1 embedding per sentence.
      • Every Classifier will classify an entire Document/row
      • Each row in the output DF is a 1 to 1 mapping of the original input DF. 1 to 1 mapping from input to output.
    • If a pipe is set to output_level=’sentence’ :
      • Every Sentence Embedding will generate 1 Embedding per Sentence,
      • Every Classifier will classify exactly one sentence
      • Each row in the output DF can is mapped to one row in the input DF, but one row in the input DF can have multiple corresponding rows in the output DF. 1 to N mapping from input to output.
  • Improved generation of column names for classifiers. based on input nlu reference
  • Improved generation of column names for embeddings, based on input nlu reference
  • Improved automatic output level inference
  • Various test updates
  • Integration of CI pipeline with Github Actions

New Documentation is out!

Check it out here : http://nlu.johnsnowlabs.com/

NLU 1.0.1 Release Notes

NLU 1.0.1 Bugfixes

  • Fixed bug that caused NER pipelines to crash in NLU when input string caused the NER model to predict without additional metadata

1.0 Release Notes

  • Automatic to Numpy conversion of embeddings
  • Added various testing classes
  • New 6 embeddings at once notebook with t-SNE and Medium article
  • Integration of Spark NLP 2.6.2 enhancements and bugfixes https://github.com/JohnSnowLabs/spark-nlp/releases/tag/2.6.2
  • Updated old T-SNE notebooks with more elegant and simpler generation of t-SNE embeddings

0.2.1 Release Notes

  • Various bugfixes
  • Improved output column names when using multiple classifirs at once

0.2 Release Notes

  • Improved output column names classifiers

0.1 Release Notes

1.0 Release Notes

  • Automatic to Numpy conversion of embeddings
  • Added various testing classes
  • New 6 embeddings at once notebook with t-SNE and Medium article
  • Integration of Spark NLP 2.6.2 enhancements and bugfixes https://github.com/JohnSnowLabs/spark-nlp/releases/tag/2.6.2
  • Updated old T-SNE notebooks with more elegant and simpler generation of t-SNE embeddings

0.2.1 Release Notes

  • Various bugfixes
  • Improved output column names when using multiple classifirs at once

0.2 Release Notes

  • Improved output column names classifiers

0.1 Release Notes

We are glad to announce that NLU 0.0.1 has been released! NLU makes the 350+ models and annotators in Spark NLPs arsenal available in just 1 line of python code and it works with Pandas dataframes! A picture says more than a 1000 words, so here is a demo clip of the 12 coolest features in NLU, all just in 1 line!

NLU in action

What does NLU 0.1 include?

NLU in action

What does NLU 0.1 include?

  • NLU provides everything a data scientist might want to wish for in one line of code!
  • 350 + pre-trained models
  • 100+ of the latest NLP word embeddings ( BERT, ELMO, ALBERT, XLNET, GLOVE, BIOBERT, ELECTRA, COVIDBERT) and different variations of them
  • 50+ of the latest NLP sentence embeddings ( BERT, ELECTRA, USE) and different variations of them
  • 50+ Classifiers (NER, POS, Emotion, Sarcasm, Questions, Spam)
  • 40+ Supported Languages
  • Labeled and Unlabeled Dependency parsing
  • Various Text Cleaning and Pre-Processing methods like Stemming, Lemmatizing, Normalizing, Filtering, Cleaning pipelines and more

NLU 0.1 Features Google Collab Notebook Demos

NLU on Medium :

  • Introduction to NLU
  • One line BERT Word Embeddings and t-SNE plotting with NLU
  • BERT, ALBERT, ELECTRA, ELMO, XLNET, GLOVE Word Embeddings in one line and plotting with t-SNE
  • NLU Documentation
  • NLU website
  • NLU Github
  • NLU Documentation
  • Overview of all NLU example notebooks
  • Having questions or want to share an idea? Join the new NLU slack channel!

NLU on Medium : Introduction to NLU One line BERT Word Embeddings and t-SNE plotting with NLU BERT, ALBERT, ELECTRA, ELMO, XLNET, GLOVE Word Embeddings in one line and plotting with t-SNE NLU Documentation NLU website NLU Github NLU Documentation Overview of all NLU example notebooks Having questions or want to share an idea? Join the new NLU slack channel!

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