Chapter 12 - create a BERT model to answer questions and summarize

Chapter12/BERT_classification/BERT_classifier_with_transfer_learning.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "BERT_classifier_with_transfer_learning.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b1ej6UMvmS85"
+      },
+      "source": [
+        "# **Problem: Create a BERT model to answer questions and summarize.**\n",
+        "\n",
+        "Python program which creates a BERT model to answer questions and summarize.\n",
+        "\n",
+        "Run all the cells. After executing the last cell, you will get the answer and the summarized text.\n",
+        "\n",
+        "**Notes:**\n",
+        "\n",
+        "Following things are needed to be checked before running the program.\n",
+        " 1. transformers module is needed to run this program in a notebook. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oWQQaoyfoFHv"
+      },
+      "source": [
+        "# **Import Modules**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Q1KG9DzAoLLn"
+      },
+      "source": [
+        "# Import the transformers pipelines\n",
+        "from transformers import pipeline\n",
+        "\n",
+        "summarizer = pipeline(\"summarization\")\n",
+        "nlp = pipeline(\"question-answering\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YVKxFe7voS4w"
+      },
+      "source": [
+        "# **Sample text and question**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "kiyQ9sE3orDi"
+      },
+      "source": [
+        "# Open and read the article\n",
+        "context = r\"The four largest cities in the Netherlands are Amsterdam, Rotterdam, The Hague and Utrecht.[17] Amsterdam is the country's most populous city and nominal capital,[18] while The Hague holds the seat of the States General, Cabinet and Supreme Court.[19] The Port of Rotterdam is the busiest seaport in Europe, and the busiest in any country outside East Asia and Southeast Asia, behind only China and Singapore.\"\n",
+        "\n",
+        "# Question to ask\n",
+        "question = \"What is the capital of the Netherlands?\""
+      ],
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZhgLLPw9oucv"
+      },
+      "source": [
+        "# **Get Answer and Summarized text**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Gi5a_V7voxab"
+      },
+      "source": [
+        "# Get the answer for the question\n",
+        "result = nlp(question=question, context=context)\n",
+        "print('Answer :', result['answer'])\n",
+        "\n",
+        "# Get the summarized text\n",
+        "print(summarizer(context, max_length=51, min_length=30, do_sample=False))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Chapter12/BERT_classification/BERT_classifier_with_transfer_learning.py

@@ -1,3 +1,37 @@
-# TODO: Create a BERT model to answer questions and summarize. 
-#       Example is here: https://colab.research.google.com/drive/1GtNcdhO0OgC0IOHrlBZ2xTspTaJGdk0j?usp=sharing
-# TODO: Code should be well commented.
+'''Copyright (c) 2021 AIClub
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated 
+documentation files (the "Software"), to deal in the Software without restriction, including without 
+limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 
+the Software, and to permit persons to whom the Software is furnished to do so, subject to the following 
+conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial
+portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT 
+LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO 
+EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN 
+AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE 
+OR OTHER DEALINGS IN THE SOFTWARE.'''
+
+# Python program which creates a BERT model to answer questions and summarize.
+
+# Import the transformers pipelines
+from transformers import pipeline
+
+summarizer = pipeline("summarization")
+nlp = pipeline("question-answering")
+
+# Open and read the article
+context = r"The four largest cities in the Netherlands are Amsterdam, Rotterdam, The Hague and Utrecht.[17] Amsterdam is the country's most populous city and nominal capital,[18] while The Hague holds the seat of the States General, Cabinet and Supreme Court.[19] The Port of Rotterdam is the busiest seaport in Europe, and the busiest in any country outside East Asia and Southeast Asia, behind only China and Singapore."
+
+# Question to ask
+question = "What is the capital of the Netherlands?"
+
+# Get the answer for the question
+result = nlp(question=question, context=context)
+print('Answer :', result['answer'])
+
+# Get the summarized text
+print(summarizer(context, max_length=51, min_length=30, do_sample=False))

Chapter12/BERT_classification/README.md

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+What it does : 
+
+    1. This python program creates a BERT model to answer questions and summarizes it.
+
+Dependancies :
+
+    1. transformers module is needed to be installed in the local machine to run this program. 
+
+
+
+