Updates to clarify QNLI example parameters.
"<a id=\"Connect-to-SAS-Viya-Server\"></a>\n",
"<h3> Connect to the SAS Viya Server </h3>\n",
"\n",
- "This section of code invokes the SAS Wrapper for Analytics Transfer (SWAT), configures the Python matplotlib utility for Jupyter notebook output display, connects to a SAS Viya server, and loads the SAS CAS DeepLearn action set into memory."
+ "This section of code invokes the SAS Wrapper for Analytics Transfer ([SWAT](https://github.com/sassoftware/python-swat)), configures the Python matplotlib utility for Jupyter notebook output display, connects to a SAS Viya server, and loads the SAS CAS DeepLearn action set into memory."
]
},
{
"# Specify the name of your SAS Viya host\n",
"host = 'your-host-name'\n",
"\n",
- "# Specify your installation's port ID and UserID parameters.\n",
- "s = CAS(host, 99999, 'userID')\n",
+ "# Specify your installation's port ID (5 digit number) and user ID parameters.\n",
+ "s = CAS(host, portID, 'userID')\n",
"\n",
"# load the SAS Viya Deep Learning Action Sets\n",
"s.loadactionset('deepLearn')"
"<a id=\"Set-Example-Parms\"></a>\n",
"<h3> Set the Example Task Parameters </h3>\n",
"\n",
- "The analytic task performed in this Jupyter notebook example is a binary two-sentence classification problem. The final classification layer for this task requires predictions for two classes. \n",
+ "The task performed in this Jupyter notebook example is to determine whether the answer to a question is given in the sentence portion of a question/sentence pair. This is an instance of a binary classification problem where\n",
"\n",
- "All twelve encoder layers of the BERT<sub>base</sub> model described in the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) are used. \n",
+ "* *class 1*: sentence answers question\n",
+ "* *class 2*: sentence does not answer question\n",
"\n",
- "The following code specifies the example task parameters:"
+ "The variable ``n_classes`` in the cell below indicates that the final classification layer in the BERT model requires predictions for two classes. \n",
+ "\n",
+ "BERT models are composed of a number of [Transformer](http://jalammar.github.io/illustrated-transformer/) encoder blocks or layers. For this binary classification task, the BERT<sub>base</sub> model described in the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) is used. This model consists of twelve encoder layers. You can choose to incorporate any number of encoder layers up to maximum the model supports. The variable ``num_encoder_layers`` in the cell below indicates that all twelve layers should be used. "
]
},
{
"\n",
"After you extract the input data, the data must be tokenized for use with BERT.\n",
"\n",
- "The input data consists of text strings that are composed of English words, but BERT networks cannot operate directly on text data. Instead, the text must be tokenized using WordPiece tokens. \n",
+ "The input data consists of text strings that are composed of English words, but BERT networks cannot operate directly on text data. Instead, the text must be tokenized using the [WordPiece algorithm](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37842.pdf). \n",
"\n",
"The WordPiece algorithm segments words into representative components, or tokens for NLP tasks. The WordPiece tokens are translated to WordPiece embeddings internally by the BERT model. Embeddings are a vector of numeric values that represent the tokens. BERT combines the WordPiece embeddings with position and segment embeddings when forming the input to the first encoder layer. \n",
"\n",
