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added pixelserver to azureml notebooks
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heatherbshapiro committed Sep 17, 2019
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9 changes: 8 additions & 1 deletion examples/README.md
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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](text_classification)|Topic Classification|BERT, XLNet|en, hi, ar|
Expand All @@ -14,3 +13,11 @@ This folder contains examples and best practices, written in Jupyter notebooks,
|[Annotation](annotation)|Text Annotation|Doccano||
|[Model Explainability](model_explainability)|DNN Layer Explanation|DUUDNM (Guan et al.)|

## 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](https://privacy.microsoft.com/en-US/privacystatement).

To opt out of tracking, please go to the raw `.ipynb` files and remove the following line of code (the URL will be slightly different depending on the file):

```sh
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/nlp/examples/text_classification/tc_bert_azureml.png)"
```
7 changes: 7 additions & 0 deletions examples/entailment/entailment_xnli_bert_azureml.ipynb
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"\n",
"**Note: To learn how to do pre-training on your own, please reference the [AzureML-BERT repo](https://github.com/microsoft/AzureML-BERT) created by Microsoft.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/nlp/examples/entailment/entailment_xnli_bert_azureml.png)"
]
},
{
"cell_type": "code",
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7 changes: 7 additions & 0 deletions examples/question_answering/bidaf_aml_deep_dive.ipynb
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"source": [
"# BiDAF Model Deep Dive on AzureML"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/nlp/examples/question_answering/bidaf_aml_deep_dive.png)"
]
},
{
"cell_type": "markdown",
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"# Question Answering: Fine-Tune BERT on AzureML (PyTorch)\n",
"**BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding** [\\[1\\]](#References)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/nlp/examples/question_answering/pretrained_BERT_SQuAD_deep_dive_aml.png)"
]
},
{
"cell_type": "markdown",
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"), [BiDAF](https://www.semanticscholar.org/paper/Bidirectional-Attention-Flow-for-Machine-Seo-Kembhavi/007ab5528b3bd310a80d553cccad4b78dc496b02\n",
"), using Azure Container Instances ([ACI](https://azure.microsoft.com/en-us/services/container-instances/))."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/nlp/examples/question_answering/bidaf_quickstart.png)"
]
},
{
"cell_type": "markdown",
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"source": [
"# Local Automated Machine Learning Model with ACI Deployment for Predicting Sentence Similarity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/nlp/examples/sentence_similarity/automl_local_deployment_aci.png)"
]
},
{
"cell_type": "markdown",
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"source": [
"# AzureML Pipeline, AutoML, AKS Deployment for Sentence Similarity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/nlp/examples/sentence_similarity/automl_with_pipelines_deployment_aks.png)"
]
},
{
"cell_type": "markdown",
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7 changes: 7 additions & 0 deletions examples/sentence_similarity/bert_senteval.ipynb
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"source": [
"# Parallel Experimentation with BERT on AzureML"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/nlp/examples/sentence_similarity/bert_senteval.png)"
]
},
{
"cell_type": "markdown",
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7 changes: 7 additions & 0 deletions examples/sentence_similarity/gensen_aml_deep_dive.ipynb
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"# Training GenSen on AzureML with SNLI Dataset\n",
"**GenSen: Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning** [\\[1\\]](#References)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/nlp/examples/sentence_similarity/gensen_aml_deep_dive.png)"
]
},
{
"cell_type": "markdown",
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7 changes: 7 additions & 0 deletions examples/text_classification/tc_bert_azureml.ipynb
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"# Text Classification of MultiNLI Sentences using BERT with Azure ML Pipelines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/nlp/examples/text_classification/tc_bert_azureml.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
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