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| .. | ||
| annotation | ||
| embeddings | ||
| entailment | ||
| model_explainability | ||
| named_entity_recognition | ||
| question_answering | ||
| sentence_similarity | ||
| sentiment_analysis/absa | ||
| text_classification | ||
| text_summarization | ||
| README.md | ||
README.md
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