Staging to master to add the latest fixes

README.md

@@ -85,6 +85,8 @@ The following is a list of related repositories that we like and think are usefu
 |[AzureML-BERT](https://github.com/Microsoft/AzureML-BERT)|End-to-end recipes for pre-training and fine-tuning BERT using Azure Machine Learning service.|
 |[MASS](https://github.com/microsoft/MASS)|MASS: Masked Sequence to Sequence Pre-training for Language Generation.|
 |[MT-DNN](https://github.com/namisan/mt-dnn)|Multi-Task Deep Neural Networks for Natural Language Understanding.|
+|[UniLM](https://github.com/microsoft/unilm)|Unified Language Model Pre-training.|
+
 
 
 ## Build Status

SETUP.md

@@ -47,9 +47,9 @@ You can learn how to create a Notebook VM [here](https://docs.microsoft.com/en-u
 We provide a script, [generate_conda_file.py](tools/generate_conda_file.py), to generate a conda-environment yaml file
 which you can use to create the target environment using the Python version 3.6 with all the correct dependencies.
 
-Assuming the repo is cloned as `nlp` in the system, to install **a default (Python CPU) environment**:
+Assuming the repo is cloned as `nlp-recipes` in the system, to install **a default (Python CPU) environment**:
 
-    cd nlp
+    cd nlp-recipes
     python tools/generate_conda_file.py
     conda env create -f nlp_cpu.yaml
 
@@ -62,7 +62,7 @@ Click on the following menus to see how to install the Python GPU environment:
 
 Assuming that you have a GPU machine, to install the Python GPU environment, which by default installs the CPU environment:
 
-    cd nlp
+    cd nlp-recipes
     python tools/generate_conda_file.py --gpu
     conda env create -n nlp_gpu -f nlp_gpu.yaml
 
@@ -79,7 +79,7 @@ Assuming that you have an Azure GPU DSVM machine, here are the steps to setup th
 
 2. Install the GPU environment.
 
-        cd nlp
+        cd nlp-recipes
         python tools/generate_conda_file.py --gpu
         conda env create -n nlp_gpu -f nlp_gpu.yaml
 
@@ -110,7 +110,7 @@ Running the command tells pip to install the `utils_nlp` package from source in
 
 > It is also possible to install directly from Github, which is the best way to utilize the `utils_nlp` package in external projects (while still reflecting updates to the source as it's installed as an editable `'-e'` package). 
 
->   `pip install -e  git+git@github.com:microsoft/nlp.git@master#egg=utils_nlp`  
+>   `pip install -e  git+git@github.com:microsoft/nlp-recipes.git@master#egg=utils_nlp`  
 
 Either command, from above, makes `utils_nlp` available in your conda virtual environment. You can verify it was properly installed by running:  
 

docs/source/conf.py

@@ -34,7 +34,7 @@
 # The full version, including alpha/beta/rc tags
 release = VERSION
 
-prefix = "NLP"
+prefix = "NLPRecipes"
 
 # -- General configuration ---------------------------------------------------
 

docs/source/index.rst

@@ -2,9 +2,9 @@
 NLP Utilities
 ===================================================
 
-The `NLP repository <https://github.com/Microsoft/NLP>`_ provides examples and best practices for building NLP systems, provided as Jupyter notebooks. 
+The `NLP repository <https://github.com/microsoft/nlp-recipes>`_ provides examples and best practices for building NLP systems, provided as Jupyter notebooks. 
 
-The module `utils_nlp <https://github.com/microsoft/nlp/tree/master/utils_nlp>`_ contains functions to simplify common tasks used when developing and 
+The module `utils_nlp <https://github.com/microsoft/nlp-recipes/tree/master/utils_nlp>`_ contains functions to simplify common tasks used when developing and 
 evaluating NLP systems. 
 
 .. toctree::

examples/entailment/entailment_xnli_bert_azureml.ipynb

@@ -45,7 +45,7 @@
     "from azureml.core.runconfig import MpiConfiguration\n",
     "from azureml.core import Experiment\n",
     "from azureml.widgets import RunDetails\n",
-    "from azureml.core.compute import ComputeTarget\n",
+    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
     "from azureml.exceptions import ComputeTargetException\n",
     "from utils_nlp.azureml.azureml_utils import get_or_create_workspace, get_output_files"
    ]
@@ -169,7 +169,7 @@
     "except ComputeTargetException:\n",
     "    print(\"Creating new compute target: {}\".format(cluster_name))\n",
     "    compute_config = AmlCompute.provisioning_configuration(\n",
-    "        vm_size=\"STANDARD_NC6\", max_nodes=1\n",
+    "        vm_size=\"STANDARD_NC6\", max_nodes=NODE_COUNT\n",
     "    )\n",
     "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
     "    compute_target.wait_for_completion(show_output=True)\n",
@@ -524,9 +524,9 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python (nlp_gpu_transformer_bug_bash)",
    "language": "python",
-   "name": "python3"
+   "name": "nlp_gpu_transformer_bug_bash"
   },
   "language_info": {
    "codemirror_mode": {

examples/question_answering/question_answering_system_bidaf_quickstart.ipynb

@@ -175,7 +175,7 @@
    "metadata": {},
    "source": [
     "This step downloads the pre-trained [AllenNLP](https://allennlp.org/models) pretrained model and registers the model in our Workspace. The pre-trained AllenNLP model we use is called Bidirectional Attention Flow for Machine Comprehension ([BiDAF](https://www.semanticscholar.org/paper/Bidirectional-Attention-Flow-for-Machine-Seo-Kembhavi/007ab5528b3bd310a80d553cccad4b78dc496b02\n",
-    ")) It achieved state-of-the-art performance on the [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) dataset in 2017 and is a well-respected, performant baseline for QA. AllenNLP's pre-trained BIDAF model is trained on the SQuAD training set and achieves an EM score of 68.3 on the SQuAD development set. See the [BIDAF deep dive notebook](https://github.com/microsoft/nlp/examples/question_answering/bidaf_deep_dive.ipynb\n",
+    ")) It achieved state-of-the-art performance on the [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) dataset in 2017 and is a well-respected, performant baseline for QA. AllenNLP's pre-trained BIDAF model is trained on the SQuAD training set and achieves an EM score of 68.3 on the SQuAD development set. See the [BIDAF deep dive notebook](https://github.com/microsoft/nlp-recipes/examples/question_answering/bidaf_deep_dive.ipynb\n",
     ") for more information on this algorithm and AllenNLP implementation."
    ]
   },

examples/text_classification/README.md

@@ -19,8 +19,5 @@ The following summarizes each notebook for Text Classification. Each notebook pr
 |Notebook|Environment|Description|Dataset|
 |---|---|---|---|
 |[BERT for text classification on AzureML](tc_bert_azureml.ipynb) |Azure ML|A notebook which walks through fine-tuning and evaluating pre-trained BERT model on a distributed setup with AzureML. |[MultiNLI](https://www.nyu.edu/projects/bowman/multinli/)|
-|[XLNet for text classification with MNLI](tc_mnli_xlnet.ipynb)|Local| A notebook which walks through fine-tuning and evaluating a pre-trained XLNet model on a subset of the MultiNLI dataset|[MultiNLI](https://www.nyu.edu/projects/bowman/multinli/)|
-|[BERT for text classification of Hindi BBC News](tc_bbc_bert_hi.ipynb)|Local| A notebook which walks through fine-tuning and evaluating a pre-trained BERT model on Hindi BBC news data|[BBC Hindi News](https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)|
-|[BERT for text classification of Arabic News](tc_dac_bert_ar.ipynb)|Local| A notebook which walks through fine-tuning and evaluating a pre-trained BERT model on Arabic news articles|[DAC](https://data.mendeley.com/datasets/v524p5dhpj/2)|
 |[Text Classification of MultiNLI Sentences using Multiple Transformer Models](tc_mnli_transformers.ipynb)|Local| A notebook which walks through fine-tuning and evaluating a number of pre-trained transformer models|[MultiNLI](https://www.nyu.edu/projects/bowman/multinli/)|
 |[Text Classification of Multi Language Datasets using Transformer Model](tc_multi_languages_transformers.ipynb)|Local|A notebook which walks through fine-tuning and evaluating a pre-trained transformer model for multiple datasets in different language|[MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) <br> [BBC Hindi News](https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1) <br> [DAC](https://data.mendeley.com/datasets/v524p5dhpj/2)