Examples

This folder contains examples and best practices, written in Jupyter notebooks, for building Natural Language Processing systems for the following scenarios.

Category	Applications	Methods	Languages
Text Classification	Topic Classification	BERT, XLNet, RoBERTa, DistilBERT	en, hi, ar
Named Entity Recognition	Wikipedia NER	BERT	en
Text Summarization	News Summarization, Headline Generation	Extractive: BERTSumExt Abstractive: UniLM (s2s-ft)	en
Entailment	MultiNLI Natural Language Inference	BERT	en
Question Answering	SQuAD	BiDAF, BERT, XLNet, DistilBERT	en
Sentence Similarity	STS Benchmark	BERT, GenSen	en
Embeddings	Custom Embeddings Training	Word2Vec, fastText, GloVe	en
Annotation	Text Annotation	Doccano	en
Model Explainability	DNN Layer Explanation	DUUDNM (Guan et al.)	en

Data/Telemetry

The Azure Machine Learning notebooks collect browser usage data and send it to Microsoft to help improve our products and services. Read Microsoft’s privacy statement to learn more.

To opt out of tracking, a Python script under the tools folder is also provided. Executing the script will check all notebooks under the examples folder, and automatically remove the telemetry cell:

python ../tools/remove_pixelserver.py