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Summarization"],"metadata":{"id":"mY5ZeufxmRJM"}},{"cell_type":"code","source":["!pip install -q contractions rouge"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kYChjoT4ObR-","executionInfo":{"status":"ok","timestamp":1702479022605,"user_tz":-60,"elapsed":8125,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"3eb31cb2-94ec-4668-c7b0-bcbbf1024289"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m289.9/289.9 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.8/110.8 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import nltk\n","nltk.download('punkt') # one time execution\n","nltk.download('stopwords')\n","nltk.download('wordnet')\n","nltk.download('omw-1.4')\n","import re\n","\n","\n","import contractions\n","import gensim\n","import rouge\n","import difflib\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from tqdm import tqdm"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uVt4LkbemQ_S","executionInfo":{"status":"ok","timestamp":1702479038381,"user_tz":-60,"elapsed":4081,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"bb5e9063-f8a7-43d7-e356-c93942b7bdae"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stderr","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data]   Unzipping tokenizers/punkt.zip.\n","[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data]   Unzipping corpora/stopwords.zip.\n","[nltk_data] Downloading package wordnet to /root/nltk_data...\n","[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"]}]},{"cell_type":"code","source":["DOCUMENT = \"\"\"\n","Italy, officially the Italian Republic (Italian: Repubblica Italiana [reˈpubblika itaˈljaːna]),[13][14] is a country consisting of a peninsula delimited by the Alps and several islands surrounding it,[15] whose territory largely coincides with the homonymous geographical region.[16] Italy is located in the centre of the Mediterranean Sea, in Southern Europe,[17][18][19] and is also considered part of Western Europe.[20][21] A unitary parliamentary republic with Rome as its capital and largest city, the country covers a total area of 301,340 km2 (116,350 sq mi) and shares land borders with France, Switzerland, Austria, Slovenia, and the enclaved microstates of Vatican City and San Marino. Italy has a territorial exclave in Switzerland (Campione) and a maritime exclave in Tunisian waters (Lampedusa). With around 60 million inhabitants, Italy is the third-most populous member state of the European Union. Due to its central geographic location in Southern Europe and the Mediterranean, Italy has historically been home to myriad peoples and cultures. In addition to the various ancient peoples dispersed throughout what is now modern-day Italy, the most predominant being the Indo-European Italic peoples who gave the peninsula its name, beginning from the classical era, Phoenicians and Carthaginians founded colonies mostly in insular Italy,[22] Greeks established settlements in the so-called Magna Graecia of Southern Italy, while Etruscans and Celts inhabited central and northern Italy respectively. An Italic tribe known as the Latins formed the Roman Kingdom in the 8th century BC, which eventually became a republic with a government of the Senate and the People. The Roman Republic initially conquered and assimilated its neighbours on the Italian peninsula, eventually expanding and conquering parts of Europe, North Africa and Asia. By the first century BC, the Roman Empire emerged as the dominant power in the Mediterranean Basin and became a leading cultural, political and religious centre, inaugurating the Pax Romana, a period of more than 200 years during which Italy's law, technology, economy, art, and literature developed.[23][24] During the Early Middle Ages, Italy endured the fall of the Western Roman Empire and barbarian invasions, but by the 11th century numerous rival city-states and maritime republics, mainly in the northern and central regions of Italy, became prosperous through trade, commerce, and banking, laying the groundwork for modern capitalism.[25] These mostly independent statelets served as Europe's main trading hubs with Asia and the Near East, often enjoying a greater degree of democracy than the larger feudal monarchies that were consolidating throughout Europe; however, part of central Italy was under the control of the theocratic Papal States, while Southern Italy remained largely feudal until the 19th century, partially as a result of a succession of Byzantine, Arab, Norman, Angevin, Aragonese, and other foreign conquests of the region.[26] The Renaissance began in Italy and spread to the rest of Europe, bringing a renewed interest in humanism, science, exploration, and art. Italian culture flourished, producing famous scholars, artists, and polymaths. During the Middle Ages, Italian explorers discovered new routes to the Far East and the New World, helping to usher in the European Age of Discovery. Nevertheless, Italy's commercial and political power significantly waned with the opening of trade routes that bypassed the Mediterranean.[27] Centuries of foreign meddling and conquest, and the rivalry and infighting between the Italian city-states, such as the Italian Wars of the 15th and 16th centuries, left Italy politically fragmented, and it was further conquered and divided among multiple foreign European powers over the centuries. By the mid-19th century, rising Italian nationalism and calls for independence from foreign control led to a period of revolutionary political upheaval. After centuries of foreign domination and political division, Italy was almost entirely unified in 1861 following a war of independence, establishing the Kingdom of Italy.[28] From the late 19th century to the early 20th century, Italy rapidly industrialised, mainly in the north, and acquired a colonial empire,[29] while the south remained largely impoverished and excluded from industrialisation, fuelling a large and influential diaspora.[30] Despite being one of the victorious allied powers in World War I, Italy entered a period of economic crisis and social turmoil, leading to the rise of the Italian fascist dictatorship in 1922. Participation in World War II on the Axis side ended in military defeat and economic destruction during the Italian campaign. Following the rise of the Italian Resistance and the liberation of Italy, the country abolished its monarchy, established a democratic Republic, enjoyed a prolonged economic boom, and became a highly developed country.[31] Italy has an advanced economy. The country is the eighth-largest by nominal GDP (third in the European Union), the sixth-largest by national wealth and the third-largest by central bank gold reserve. It ranks highly in life expectancy, quality of life,[32] healthcare,[33] and education. The country is a great power and it has a significant role in regional[34][35] and global [36][37] economic, military, cultural, and diplomatic affairs. Italy is a founding and leading member of the European Union and a member of numerous international institutions, including the United Nations, NATO, the OECD, the Organization for Security and Co-operation in Europe, the World Trade Organization, the Group of Seven, the G20, the Union for the Mediterranean, the Latin Union, the Council of Europe, Uniting for Consensus, the Schengen Area, and many more. The source of many inventions and discoveries, the country has long been a global centre of art, music, literature, philosophy, science and technology, and fashion, and has greatly influenced and contributed to diverse fields including cinema, cuisine, sports, jurisprudence, banking, and business.[38] As a reflection of its cultural wealth, Italy has the world's largest number of World Heritage Sites (58), and is the fifth-most visited country.\n","\"\"\"\n","\n","DOCUMENT"],"metadata":{"id":"hXEQURHwm618","colab":{"base_uri":"https://localhost:8080/","height":192},"executionInfo":{"status":"ok","timestamp":1702479102625,"user_tz":-60,"elapsed":8,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"055c47eb-ad49-4fbf-9460-8a425a42c169"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"\\nItaly, officially the Italian Republic (Italian: Repubblica Italiana [reˈpubblika itaˈljaːna]),[13][14] is a country consisting of a peninsula delimited by the Alps and several islands surrounding it,[15] whose territory largely coincides with the homonymous geographical region.[16] Italy is located in the centre of the Mediterranean Sea, in Southern Europe,[17][18][19] and is also considered part of Western Europe.[20][21] A unitary parliamentary republic with Rome as its capital and largest city, the country covers a total area of 301,340 km2 (116,350 sq mi) and shares land borders with France, Switzerland, Austria, Slovenia, and the enclaved microstates of Vatican City and San Marino. Italy has a territorial exclave in Switzerland (Campione) and a maritime exclave in Tunisian waters (Lampedusa). With around 60 million inhabitants, Italy is the third-most populous member state of the European Union. Due to its central geographic location in Southern Europe and the Mediterranean, Italy has historically been home to myriad peoples and cultures. In addition to the various ancient peoples dispersed throughout what is now modern-day Italy, the most predominant being the Indo-European Italic peoples who gave the peninsula its name, beginning from the classical era, Phoenicians and Carthaginians founded colonies mostly in insular Italy,[22] Greeks established settlements in the so-called Magna Graecia of Southern Italy, while Etruscans and Celts inhabited central and northern Italy respectively. An Italic tribe known as the Latins formed the Roman Kingdom in the 8th century BC, which eventually became a republic with a government of the Senate and the People. The Roman Republic initially conquered and assimilated its neighbours on the Italian peninsula, eventually expanding and conquering parts of Europe, North Africa and Asia. By the first century BC, the Roman Empire emerged as the dominant power in the Mediterranean Basin and became a leading cultural, political and religious centre, inaugurating the Pax Romana, a period of more than 200 years during which Italy's law, technology, economy, art, and literature developed.[23][24] During the Early Middle Ages, Italy endured the fall of the Western Roman Empire and barbarian invasions, but by the 11th century numerous rival city-states and maritime republics, mainly in the northern and central regions of Italy, became prosperous through trade, commerce, and banking, laying the groundwork for modern capitalism.[25] These mostly independent statelets served as Europe's main trading hubs with Asia and the Near East, often enjoying a greater degree of democracy than the larger feudal monarchies that were consolidating throughout Europe; however, part of central Italy was under the control of the theocratic Papal States, while Southern Italy remained largely feudal until the 19th century, partially as a result of a succession of Byzantine, Arab, Norman, Angevin, Aragonese, and other foreign conquests of the region.[26] The Renaissance began in Italy and spread to the rest of Europe, bringing a renewed interest in humanism, science, exploration, and art. Italian culture flourished, producing famous scholars, artists, and polymaths. During the Middle Ages, Italian explorers discovered new routes to the Far East and the New World, helping to usher in the European Age of Discovery. Nevertheless, Italy's commercial and political power significantly waned with the opening of trade routes that bypassed the Mediterranean.[27] Centuries of foreign meddling and conquest, and the rivalry and infighting between the Italian city-states, such as the Italian Wars of the 15th and 16th centuries, left Italy politically fragmented, and it was further conquered and divided among multiple foreign European powers over the centuries. By the mid-19th century, rising Italian nationalism and calls for independence from foreign control led to a period of revolutionary political upheaval. After centuries of foreign domination and political division, Italy was almost entirely unified in 1861 following a war of independence, establishing the Kingdom of Italy.[28] From the late 19th century to the early 20th century, Italy rapidly industrialised, mainly in the north, and acquired a colonial empire,[29] while the south remained largely impoverished and excluded from industrialisation, fuelling a large and influential diaspora.[30] Despite being one of the victorious allied powers in World War I, Italy entered a period of economic crisis and social turmoil, leading to the rise of the Italian fascist dictatorship in 1922. Participation in World War II on the Axis side ended in military defeat and economic destruction during the Italian campaign. Following the rise of the Italian Resistance and the liberation of Italy, the country abolished its monarchy, established a democratic Republic, enjoyed a prolonged economic boom, and became a highly developed country.[31] Italy has an advanced economy. The country is the eighth-largest by nominal GDP (third in the European Union), the sixth-largest by national wealth and the third-largest by central bank gold reserve. It ranks highly in life expectancy, quality of life,[32] healthcare,[33] and education. The country is a great power and it has a significant role in regional[34][35] and global [36][37] economic, military, cultural, and diplomatic affairs. Italy is a founding and leading member of the European Union and a member of numerous international institutions, including the United Nations, NATO, the OECD, the Organization for Security and Co-operation in Europe, the World Trade Organization, the Group of Seven, the G20, the Union for the Mediterranean, the Latin Union, the Council of Europe, Uniting for Consensus, the Schengen Area, and many more. The source of many inventions and discoveries, the country has long been a global centre of art, music, literature, philosophy, science and technology, and fashion, and has greatly influenced and contributed to diverse fields including cinema, cuisine, sports, jurisprudence, banking, and business.[38] As a reflection of its cultural wealth, Italy has the world's largest number of World Heritage Sites (58), and is the fifth-most visited country.\\n\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["DOCUMENT = re.sub(r'\\n|\\r', ' ', DOCUMENT)\n","DOCUMENT = re.sub(r' +', ' ', DOCUMENT)\n","DOCUMENT = re.sub(r'\\d+', ' ', DOCUMENT)\n","pattern = r'\\[[^\\]]*\\]'\n","DOCUMENT = re.sub(pattern, ' ', DOCUMENT)\n","DOCUMENT = DOCUMENT.strip()\n","DOCUMENT"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":192},"id":"85CkaLOGm6xq","executionInfo":{"status":"ok","timestamp":1702479150017,"user_tz":-60,"elapsed":241,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"2823134b-9509-4da7-c2c5-39ee7e8edcdd"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"Italy, officially the Italian Republic (Italian: Repubblica Italiana  ),   is a country consisting of a peninsula delimited by the Alps and several islands surrounding it,  whose territory largely coincides with the homonymous geographical region.  Italy is located in the centre of the Mediterranean Sea, in Southern Europe,    and is also considered part of Western Europe.   A unitary parliamentary republic with Rome as its capital and largest city, the country covers a total area of  ,  km  ( ,  sq mi) and shares land borders with France, Switzerland, Austria, Slovenia, and the enclaved microstates of Vatican City and San Marino. Italy has a territorial exclave in Switzerland (Campione) and a maritime exclave in Tunisian waters (Lampedusa). With around   million inhabitants, Italy is the third-most populous member state of the European Union. Due to its central geographic location in Southern Europe and the Mediterranean, Italy has historically been home to myriad peoples and cultures. In addition to the various ancient peoples dispersed throughout what is now modern-day Italy, the most predominant being the Indo-European Italic peoples who gave the peninsula its name, beginning from the classical era, Phoenicians and Carthaginians founded colonies mostly in insular Italy,  Greeks established settlements in the so-called Magna Graecia of Southern Italy, while Etruscans and Celts inhabited central and northern Italy respectively. An Italic tribe known as the Latins formed the Roman Kingdom in the  th century BC, which eventually became a republic with a government of the Senate and the People. The Roman Republic initially conquered and assimilated its neighbours on the Italian peninsula, eventually expanding and conquering parts of Europe, North Africa and Asia. By the first century BC, the Roman Empire emerged as the dominant power in the Mediterranean Basin and became a leading cultural, political and religious centre, inaugurating the Pax Romana, a period of more than   years during which Italy's law, technology, economy, art, and literature developed.   During the Early Middle Ages, Italy endured the fall of the Western Roman Empire and barbarian invasions, but by the  th century numerous rival city-states and maritime republics, mainly in the northern and central regions of Italy, became prosperous through trade, commerce, and banking, laying the groundwork for modern capitalism.  These mostly independent statelets served as Europe's main trading hubs with Asia and the Near East, often enjoying a greater degree of democracy than the larger feudal monarchies that were consolidating throughout Europe; however, part of central Italy was under the control of the theocratic Papal States, while Southern Italy remained largely feudal until the  th century, partially as a result of a succession of Byzantine, Arab, Norman, Angevin, Aragonese, and other foreign conquests of the region.  The Renaissance began in Italy and spread to the rest of Europe, bringing a renewed interest in humanism, science, exploration, and art. Italian culture flourished, producing famous scholars, artists, and polymaths. During the Middle Ages, Italian explorers discovered new routes to the Far East and the New World, helping to usher in the European Age of Discovery. Nevertheless, Italy's commercial and political power significantly waned with the opening of trade routes that bypassed the Mediterranean.  Centuries of foreign meddling and conquest, and the rivalry and infighting between the Italian city-states, such as the Italian Wars of the  th and  th centuries, left Italy politically fragmented, and it was further conquered and divided among multiple foreign European powers over the centuries. By the mid- th century, rising Italian nationalism and calls for independence from foreign control led to a period of revolutionary political upheaval. After centuries of foreign domination and political division, Italy was almost entirely unified in   following a war of independence, establishing the Kingdom of Italy.  From the late  th century to the early  th century, Italy rapidly industrialised, mainly in the north, and acquired a colonial empire,  while the south remained largely impoverished and excluded from industrialisation, fuelling a large and influential diaspora.  Despite being one of the victorious allied powers in World War I, Italy entered a period of economic crisis and social turmoil, leading to the rise of the Italian fascist dictatorship in  . Participation in World War II on the Axis side ended in military defeat and economic destruction during the Italian campaign. Following the rise of the Italian Resistance and the liberation of Italy, the country abolished its monarchy, established a democratic Republic, enjoyed a prolonged economic boom, and became a highly developed country.  Italy has an advanced economy. The country is the eighth-largest by nominal GDP (third in the European Union), the sixth-largest by national wealth and the third-largest by central bank gold reserve. It ranks highly in life expectancy, quality of life,  healthcare,  and education. The country is a great power and it has a significant role in regional   and global    economic, military, cultural, and diplomatic affairs. Italy is a founding and leading member of the European Union and a member of numerous international institutions, including the United Nations, NATO, the OECD, the Organization for Security and Co-operation in Europe, the World Trade Organization, the Group of Seven, the G , the Union for the Mediterranean, the Latin Union, the Council of Europe, Uniting for Consensus, the Schengen Area, and many more. The source of many inventions and discoveries, the country has long been a global centre of art, music, literature, philosophy, science and technology, and fashion, and has greatly influenced and contributed to diverse fields including cinema, cuisine, sports, jurisprudence, banking, and business.  As a reflection of its cultural wealth, Italy has the world's largest number of World Heritage Sites ( ), and is the fifth-most visited country.\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["sentences = nltk.sent_tokenize(DOCUMENT)"],"metadata":{"id":"23JXWLSUm-BR","executionInfo":{"status":"ok","timestamp":1702479177552,"user_tz":-60,"elapsed":186,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","\n","stop_words = nltk.corpus.stopwords.words('english')\n","\n","def normalize_document(doc):\n","    # lower case and remove special characters\\whitespaces\n","    doc = re.sub(r'[^a-zA-Z\\s]', '', doc, re.I|re.A)\n","    doc = doc.lower()\n","    doc = doc.strip()\n","    # tokenize document\n","    tokens = nltk.word_tokenize(doc)\n","    # filter stopwords out of document\n","    filtered_tokens = [token for token in tokens if token not in stop_words]\n","    # re-create document from filtered tokens\n","    doc = ' '.join(filtered_tokens)\n","    return doc\n","\n","normalize_corpus = np.vectorize(normalize_document)\n","norm_sentences = normalize_corpus(sentences)\n","norm_sentences[:3]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iNx-WSaXnAzH","executionInfo":{"status":"ok","timestamp":1702479345391,"user_tz":-60,"elapsed":326,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"87a6b0f0-fad2-4e4b-8d91-664a9758de76"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['italy officially italian republic italian repubblica italiana country consisting peninsula delimited alps several islands surrounding whose territory largely coincides homonymous geographical region',\n","       'italy located centre mediterranean sea southern europe also considered part western europe',\n","       'unitary parliamentary republic rome capital largest city country covers total area km sq mi shares land borders france switzerland austria slovenia enclaved microstates vatican city san marino'],\n","      dtype='<U375')"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["from sklearn.feature_extraction.text import TfidfVectorizer\n","import pandas as pd\n","\n","tv = TfidfVectorizer(min_df=0., max_df=1., use_idf=True)\n","dt_matrix = tv.fit_transform(norm_sentences)\n","dt_matrix = dt_matrix.toarray()"],"metadata":{"id":"q3TGvsianGYe","executionInfo":{"status":"ok","timestamp":1702479373319,"user_tz":-60,"elapsed":281,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["dt_matrix.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UvRNJtR1ranx","executionInfo":{"status":"ok","timestamp":1702479380683,"user_tz":-60,"elapsed":4,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"a90a9b61-7f9f-4f1b-f03c-149ff6b978b5"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(30, 385)"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["similarity_matrix = np.matmul(dt_matrix, dt_matrix.T)\n","print(similarity_matrix.shape)\n","np.round(similarity_matrix, 3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8Au3ixrRnGIW","executionInfo":{"status":"ok","timestamp":1702479457174,"user_tz":-60,"elapsed":209,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"371b6656-12dd-4297-c572-e62cc29b3312"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["(30, 30)\n"]},{"output_type":"execute_result","data":{"text/plain":["array([[1.   , 0.013, 0.04 , 0.012, 0.014, 0.013, 0.051, 0.033, 0.118,\n","        0.   , 0.016, 0.063, 0.012, 0.056, 0.038, 0.   , 0.066, 0.044,\n","        0.023, 0.042, 0.049, 0.046, 0.124, 0.028, 0.026, 0.   , 0.029,\n","        0.007, 0.019, 0.041],\n","       [0.013, 1.   , 0.   , 0.017, 0.02 , 0.221, 0.067, 0.   , 0.082,\n","        0.081, 0.074, 0.129, 0.104, 0.   , 0.   , 0.052, 0.011, 0.   ,\n","        0.033, 0.012, 0.014, 0.   , 0.014, 0.04 , 0.   , 0.   , 0.   ,\n","        0.132, 0.044, 0.017],\n","       [0.04 , 0.   , 1.   , 0.046, 0.   , 0.   , 0.   , 0.027, 0.027,\n","        0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   ,\n","        0.   , 0.   , 0.   , 0.   , 0.062, 0.   , 0.021, 0.   , 0.024,\n","        0.025, 0.015, 0.071],\n","       [0.012, 0.017, 0.046, 1.   , 0.018, 0.016, 0.034, 0.   , 0.   ,\n","        0.   , 0.069, 0.016, 0.016, 0.   , 0.   , 0.   , 0.01 , 0.   ,\n","        0.03 , 0.011, 0.013, 0.   , 0.013, 0.037, 0.   , 0.   , 0.   ,\n","        0.009, 0.   , 0.016],\n","       [0.014, 0.02 , 0.   , 0.018, 1.   , 0.019, 0.039, 0.   , 0.   ,\n","        0.   , 0.024, 0.018, 0.018, 0.   , 0.044, 0.   , 0.046, 0.   ,\n","        0.035, 0.013, 0.015, 0.   , 0.015, 0.043, 0.114, 0.   , 0.   ,\n","        0.245, 0.   , 0.019],\n","       [0.013, 0.221, 0.   , 0.016, 0.019, 1.   , 0.172, 0.   , 0.039,\n","        0.034, 0.053, 0.089, 0.058, 0.   , 0.   , 0.05 , 0.01 , 0.   ,\n","        0.032, 0.012, 0.014, 0.   , 0.014, 0.038, 0.044, 0.   , 0.   ,\n","        0.081, 0.   , 0.017],\n","       [0.051, 0.067, 0.   , 0.034, 0.039, 0.172, 1.   , 0.036, 0.03 ,\n","        0.   , 0.086, 0.099, 0.033, 0.   , 0.   , 0.   , 0.022, 0.   ,\n","        0.065, 0.024, 0.029, 0.   , 0.061, 0.079, 0.023, 0.   , 0.   ,\n","        0.018, 0.   , 0.035],\n","       [0.033, 0.   , 0.027, 0.   , 0.   , 0.   , 0.036, 1.   , 0.147,\n","        0.142, 0.113, 0.038, 0.   , 0.   , 0.   , 0.   , 0.048, 0.073,\n","        0.065, 0.108, 0.   , 0.   , 0.076, 0.   , 0.   , 0.   , 0.   ,\n","        0.   , 0.   , 0.   ],\n","       [0.118, 0.082, 0.027, 0.   , 0.   , 0.039, 0.03 , 0.147, 1.   ,\n","        0.034, 0.032, 0.053, 0.037, 0.034, 0.023, 0.   , 0.079, 0.027,\n","        0.   , 0.048, 0.023, 0.028, 0.058, 0.   , 0.   , 0.   , 0.   ,\n","        0.041, 0.   , 0.   ],\n","       [0.   , 0.081, 0.   , 0.   , 0.   , 0.034, 0.   , 0.142, 0.034,\n","        1.   , 0.095, 0.014, 0.042, 0.   , 0.   , 0.17 , 0.   , 0.105,\n","        0.036, 0.072, 0.073, 0.   , 0.073, 0.121, 0.   , 0.   , 0.09 ,\n","        0.041, 0.126, 0.044],\n","       [0.016, 0.074, 0.   , 0.069, 0.024, 0.053, 0.086, 0.113, 0.032,\n","        0.095, 1.   , 0.062, 0.02 , 0.   , 0.08 , 0.042, 0.078, 0.051,\n","        0.04 , 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0.034, 0.025, 1.   ]])"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["import networkx\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","\n","similarity_graph = networkx.from_numpy_array(similarity_matrix)\n","\n","plt.figure(figsize=(12, 6))\n","networkx.draw_networkx(similarity_graph, node_color='lime')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":441},"id":"wShL3di5nLCG","executionInfo":{"status":"ok","timestamp":1702479494907,"user_tz":-60,"elapsed":1843,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"92229a28-6d15-48fb-ce02-62b2f0f8353a"},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["scores = networkx.pagerank(similarity_graph)\n","ranked_sentences = sorted(((score, index) for index, score\n","                                            in scores.items()),\n","                          reverse=True)\n","ranked_sentences[:10]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_MburIMOnPPr","executionInfo":{"status":"ok","timestamp":1702479574807,"user_tz":-60,"elapsed":196,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"9bdd3851-79ac-4ecb-f055-2d5b40e5c1eb"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[(0.040721209316877505, 22),\n"," (0.03994787372328047, 9),\n"," (0.03939359424138909, 10),\n"," (0.0375833609071944, 16),\n"," (0.03744652515777444, 27),\n"," (0.037387204944521565, 18),\n"," (0.03661709582201522, 1),\n"," (0.03563095404425751, 11),\n"," (0.035492104383262474, 20),\n"," (0.03518843307678377, 0)]"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["num_sentences = 5\n","\n","top_sentence_indices = [ranked_sentences[index][1]\n","                        for index in range(num_sentences)]\n","top_sentence_indices.sort()"],"metadata":{"id":"iFUD1wcRnPJq","executionInfo":{"status":"ok","timestamp":1702479620661,"user_tz":-60,"elapsed":217,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["print('.'.join(np.array(sentences)[top_sentence_indices]).replace('.', '.\\n'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ACAuuRlZoOwJ","executionInfo":{"status":"ok","timestamp":1702479629108,"user_tz":-60,"elapsed":240,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"d65963e4-aff2-4102-d6a5-72665f6f49f8"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["By the first century BC, the Roman Empire emerged as the dominant power in the Mediterranean Basin and became a leading cultural, political and religious centre, inaugurating the Pax Romana, a period of more than   years during which Italy's law, technology, economy, art, and literature developed.\n",".\n","During the Early Middle Ages, Italy endured the fall of the Western Roman Empire and barbarian invasions, but by the  th century numerous rival city-states and maritime republics, mainly in the northern and central regions of Italy, became prosperous through trade, commerce, and banking, laying the groundwork for modern capitalism.\n",".\n","Centuries of foreign meddling and conquest, and the rivalry and infighting between the Italian city-states, such as the Italian Wars of the  th and  th centuries, left Italy politically fragmented, and it was further conquered and divided among multiple foreign European powers over the centuries.\n",".\n","Following the rise of the Italian Resistance and the liberation of Italy, the country abolished its monarchy, established a democratic Republic, enjoyed a prolonged economic boom, and became a highly developed country.\n",".\n","Italy is a founding and leading member of the European Union and a member of numerous international institutions, including the United Nations, NATO, the OECD, the Organization for Security and Co-operation in Europe, the World Trade Organization, the Group of Seven, the G , the Union for the Mediterranean, the Latin Union, the Council of Europe, Uniting for Consensus, the Schengen Area, and many more.\n","\n"]}]},{"cell_type":"markdown","source":["## Example on a dataset"],"metadata":{"id":"mRrtbsryqcvs"}},{"cell_type":"code","source":["!gdown 'https://drive.google.com/uc?id=1zT0-f5NOKQOuUkiIc29AG2N9upVswC2n'\n","dtf = pd.read_csv('/content/data_summary.csv')\n","dtf.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":276},"id":"rdFB33xJmQ5n","executionInfo":{"status":"ok","timestamp":1702479666490,"user_tz":-60,"elapsed":6187,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"b10aed72-8be9-445a-9872-ceb58a18bb7d"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading...\n","From: https://drive.google.com/uc?id=1zT0-f5NOKQOuUkiIc29AG2N9upVswC2n\n","To: /content/data_summary.csv\n","100% 84.5M/84.5M [00:02<00:00, 36.0MB/s]\n"]},{"output_type":"execute_result","data":{"text/plain":["   Unnamed: 0                                               text  \\\n","0           0  It's official: U.S. President Barack Obama wan...   \n","1           1  (CNN) -- Usain Bolt rounded off the world cham...   \n","2           2  Kansas City, Missouri (CNN) -- The General Ser...   \n","3           3  Los Angeles (CNN) -- A medical doctor in Vanco...   \n","4           4  (CNN) -- Police arrested another teen Thursday...   \n","\n","                                                   y  \n","0  Syrian official: Obama climbed to the top of t...  \n","1  Usain Bolt wins third gold of world championsh...  \n","2  The employee in agency's Kansas City office is...  \n","3  NEW: A Canadian doctor says she was part of a ...  \n","4  Another arrest made in gang rape outside Calif...  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The fastest man in the world charged clear of United States rival Justin Gatlin as the Jamaican quartet of Nesta Carter, Kemar Bailey-Cole, Nickel Ashmeade and Bolt won in 37.36 seconds. The U.S finished second in 37.56 seconds with Canada taking the bronze after Britain were disqualified for a faulty handover. The 26-year-old Bolt has now collected eight gold medals at world championships, equaling the record held by American trio Carl Lewis, Michael Johnson and Allyson Felix, not to mention the small matter of six Olympic titles. The relay triumph followed individual successes in the 100 and 200 meters in the Russian capital. \"I'm proud of myself and I'll continue to work to dominate for as long as possible,\" Bolt said, having previously expressed his intention to carry on until the 2016 Rio Olympics. Victory was never seriously in doubt once he got the baton safely in hand from Ashmeade, while Gatlin and the United States third leg runner Rakieem Salaam had problems. Gatlin strayed out of his lane as he struggled to get full control of their baton and was never able to get on terms with Bolt. Earlier, Jamaica's women underlined their dominance in the sprint events by winning the 4x100m relay gold, anchored by Shelly-Ann Fraser-Pryce, who like Bolt was completing a triple. Their quartet recorded a championship record of 41.29 seconds, well clear of France, who crossed the line in second place in 42.73 seconds. Defending champions, the United States, were initially back in the bronze medal position after losing time on the second handover between Alexandria Anderson and English Gardner, but promoted to silver when France were subsequently disqualified for an illegal handover. The British quartet, who were initially fourth, were promoted to the bronze which eluded their men's team. Fraser-Pryce, like Bolt aged 26, became the first woman to achieve three golds in the 100-200 and the relay. In other final action on the last day of the championships, France's Teddy Tamgho became the third man to leap over 18m in the triple jump, exceeding the mark by four centimeters to take gold. Germany's Christina Obergfoll finally took gold at global level in the women's javelin after five previous silvers, while Kenya's Asbel Kiprop easily won a tactical men's 1500m final. Kiprop's compatriot Eunice Jepkoech Sum was a surprise winner of the women's 800m. Bolt's final dash for golden glory brought the eight-day championship to a rousing finale, but while the hosts topped the medal table from the United States there was criticism of the poor attendances in the Luzhniki Stadium. There was further concern when their pole vault gold medalist Yelena Isinbayeva made controversial remarks in support of Russia's new laws, which make \"the propagandizing of non-traditional sexual relations among minors\" a criminal offense. She later attempted to clarify her comments, but there were renewed calls by gay rights groups for a boycott of the 2014 Winter Games in Sochi, the next major sports event in Russia.\n"," \n","--- Summary ---\n","Usain Bolt wins third gold of world championship .\n","Anchors Jamaica to 4x100m relay victory .\n","Eighth gold at the championships for Bolt .\n","Jamaica double up in women's 4x100m relay .\n"]}]},{"cell_type":"code","source":["def create_stopwords(lst_langs=[\"english\"], lst_add_words=[], lst_keep_words=[]):\n","    lst_stopwords = set()\n","    for lang in lst_langs:\n","        lst_stopwords = lst_stopwords.union( set(nltk.corpus.stopwords.words(lang)) )\n","    lst_stopwords = lst_stopwords.union(lst_add_words)\n","    lst_stopwords = list(set(lst_stopwords) - set(lst_keep_words))\n","    return sorted(list(set(lst_stopwords)))\n","\n","lst_stopwords = create_stopwords(lst_langs=['english'], lst_add_words=[\"cnn\",\"say\",\"said\",\"new\",\"wa\",\"ha\"])\n","# lst_stopwords = lst_stopwords"],"metadata":{"id":"CMF3vGA5mrpM","executionInfo":{"status":"ok","timestamp":1702479852566,"user_tz":-60,"elapsed":4,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["def utils_preprocess_text(txt, lst_regex=None, punkt=True, lower=True, slang=True, lst_stopwords=None, stemm=False, lemm=True):\n","    ## Regex (in case, before cleaning)\n","    if lst_regex is not None:\n","        for regex in lst_regex:\n","            txt = re.sub(regex, '', txt)\n","\n","    ## Clean\n","    ### separate sentences with '. '\n","    txt = re.sub(r'\\.(?=[^ \\W\\d])', '. ', str(txt))\n","    ### remove punctuations and characters\n","    txt = re.sub(r'[^\\w\\s]', '', txt) if punkt is True else txt\n","    ### strip\n","    txt = \" \".join([word.strip() for word in txt.split()])\n","    ### lowercase\n","    txt = txt.lower() if lower is True else txt\n","    ### slang\n","    txt = contractions.fix(txt) if slang is True else txt\n","\n","    ## Tokenize (convert from string to list)\n","    lst_txt = txt.split()\n","\n","    ## Stemming (remove -ing, -ly, ...)\n","    if stemm is True:\n","        ps = nltk.stem.porter.PorterStemmer()\n","        lst_txt = [ps.stem(word) for word in lst_txt]\n","\n","    ## Lemmatization (convert the word into root word)\n","    if lemm is True:\n","        lem = nltk.stem.wordnet.WordNetLemmatizer()\n","        lst_txt = [lem.lemmatize(word) for word in lst_txt]\n","\n","    ## Stopwords\n","    if lst_stopwords is not None:\n","        lst_txt = [word for word in lst_txt if word not in lst_stopwords]\n","\n","    ## Back to string\n","    txt = \" \".join(lst_txt)\n","    return txt\n","\n","def add_preprocessed_text(data, column, lst_regex=None, punkt=False, lower=False, slang=False, lst_stopwords=None, stemm=False, lemm=False, remove_na=True):\n","    dtf = data.copy()\n","\n","    ## apply preprocess\n","    dtf = dtf[ pd.notnull(dtf[column]) ]\n","    tqdm.pandas(desc='Cleaning text') # lots of cool paramiters you can pass here.\n","    dtf[column+\"_clean\"] = dtf[column].progress_apply(lambda x: utils_preprocess_text(x, lst_regex, punkt, lower, slang, lst_stopwords, stemm, lemm))\n","\n","    ## residuals\n","    tqdm.pandas(desc='Checking Residuals')\n","    dtf[\"check\"] = dtf[column+\"_clean\"].progress_apply(lambda x: len(x))\n","    if dtf[\"check\"].min() == 0:\n","        print(\"--- found NAs ---\")\n","        print(dtf[[column,column+\"_clean\"]][dtf[\"check\"]==0].head())\n","        if remove_na is True:\n","            dtf = dtf[dtf[\"check\"]>0]\n","\n","    return dtf.drop(\"check\", axis=1)\n","\n","def add_text_length(data, column):\n","    dtf = data.copy()\n","    tqdm.pandas(desc='Word Count')\n","    dtf['word_count'] = dtf[column].progress_apply(lambda x: len(nltk.word_tokenize(str(x))) )\n","    tqdm.pandas(desc='Char Count')\n","    dtf['char_count'] = dtf[column].progress_apply(lambda x: sum(len(word) for word in nltk.word_tokenize(str(x))) )\n","    tqdm.pandas(desc='Sentence Count')\n","    dtf['sentence_count'] = dtf[column].progress_apply(lambda x: len(nltk.sent_tokenize(str(x))) )\n","    dtf['avg_word_length'] = dtf['char_count'] / dtf['word_count']\n","    dtf['avg_sentence_lenght'] = dtf['word_count'] / dtf['sentence_count']\n","    print()\n","    print(dtf[['char_count','word_count','sentence_count','avg_word_length','avg_sentence_lenght']].describe().T[[\"min\",\"mean\",\"max\"]])\n","    return dtf\n","\n","def plot_distributions(dtf, x, max_cat=20, top=None, y=None, bins=None, figsize=(10,5)):\n","    ## univariate\n","    if y is None:\n","        fig, ax = plt.subplots(figsize=figsize)\n","        fig.suptitle(x, fontsize=15)\n","        ### categorical\n","        if dtf[x].nunique() <= max_cat:\n","            if top is None:\n","                dtf[x].reset_index().groupby(x).count().sort_values(by=\"index\").plot(kind=\"barh\", legend=False, ax=ax).grid(axis='x')\n","            else:\n","                dtf[x].reset_index().groupby(x).count().sort_values(by=\"index\").tail(top).plot(kind=\"barh\", legend=False, ax=ax).grid(axis='x')\n","            ax.set(ylabel=None)\n","        ### numerical\n","        else:\n","            sns.distplot(dtf[x], hist=True, kde=True, kde_kws={\"shade\":True}, ax=ax)\n","            ax.grid(True)\n","            ax.set(xlabel=None, yticklabels=[], yticks=[])\n","\n","    ## bivariate\n","    else:\n","        fig, ax = plt.subplots(nrows=1, ncols=2, sharex=False, sharey=False, figsize=figsize)\n","        fig.suptitle(x, fontsize=15)\n","        for i in dtf[y].unique():\n","            sns.distplot(dtf[dtf[y]==i][x], hist=True, kde=False, bins=bins, hist_kws={\"alpha\":0.8}, axlabel=\"\", ax=ax[0])\n","            sns.distplot(dtf[dtf[y]==i][x], hist=False, kde=True, kde_kws={\"shade\":True}, axlabel=\"\", ax=ax[1])\n","        ax[0].set(title=\"histogram\")\n","        ax[0].grid(True)\n","        ax[0].legend(dtf[y].unique())\n","        ax[1].set(title=\"density\")\n","        ax[1].grid(True)\n","    plt.show()\n"],"metadata":{"id":"5EJRzkY1myEC","executionInfo":{"status":"ok","timestamp":1702479940173,"user_tz":-60,"elapsed":230,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["dtf = add_preprocessed_text(dtf, column=\"text\",\n","                            punkt=True, lower=True, slang=True, lst_stopwords=lst_stopwords, lemm=True)\n","dtf = add_preprocessed_text(dtf, column=\"y\",\n","                            punkt=True, lower=True, slang=True, lst_stopwords=lst_stopwords, lemm=True)\n","dtf.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"id":"HJFFOl3RODKp","executionInfo":{"status":"ok","timestamp":1702480072993,"user_tz":-60,"elapsed":120724,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"fd7e29c1-d9d8-4d54-807b-130a5c216f0c"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stderr","text":["Cleaning text: 100%|██████████| 20000/20000 [01:54<00:00, 175.07it/s]\n","Checking Residuals: 100%|██████████| 20000/20000 [00:00<00:00, 731556.14it/s]\n","Cleaning text: 100%|██████████| 20000/20000 [00:06<00:00, 3278.04it/s]\n","Checking Residuals: 100%|██████████| 20000/20000 [00:00<00:00, 757121.92it/s]\n"]},{"output_type":"execute_result","data":{"text/plain":["   Unnamed: 0                                               text  \\\n","0           0  It's official: U.S. President Barack Obama wan...   \n","1           1  (CNN) -- Usain Bolt rounded off the world cham...   \n","2           2  Kansas City, Missouri (CNN) -- The General Ser...   \n","3           3  Los Angeles (CNN) -- A medical doctor in Vanco...   \n","4           4  (CNN) -- Police arrested another teen Thursday...   \n","\n","                                                   y  \\\n","0  Syrian official: Obama climbed to the top of t...   \n","1  Usain Bolt wins third gold of world championsh...   \n","2  The employee in agency's Kansas City office is...   \n","3  NEW: A Canadian doctor says she was part of a ...   \n","4  Another arrest made in gang rape outside Calif...   \n","\n","                                          text_clean  \\\n","0  official president barack obama want lawmaker ...   \n","1  usain bolt rounded world championship sunday c...   \n","2  kansa city missouri general service administra...   \n","3  los angeles medical doctor vancouver british c...   \n","4  police arrested another teen thursday sixth su...   \n","\n","                                             y_clean  \n","0  syrian official obama climbed top tree doe kno...  \n","1  usain bolt win third gold world championship a...  \n","2  employee agency kansa city office among hundre...  \n","3  canadian doctor part team examining harry burk...  \n","4  another arrest made gang rape outside californ...  "],"text/html":["\n","  <div id=\"df-61053d4d-2110-42ed-be20-2a2e190c4ce1\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>text</th>\n","      <th>y</th>\n","      <th>text_clean</th>\n","      <th>y_clean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>It's official: U.S. President Barack Obama wan...</td>\n","      <td>Syrian official: Obama climbed to the top of t...</td>\n","      <td>official president barack obama want lawmaker ...</td>\n","      <td>syrian official obama climbed top tree doe kno...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>(CNN) -- Usain Bolt rounded off the world cham...</td>\n","      <td>Usain Bolt wins third gold of world championsh...</td>\n","      <td>usain bolt rounded world championship sunday c...</td>\n","      <td>usain bolt win third gold world championship a...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>Kansas City, Missouri (CNN) -- The General Ser...</td>\n","      <td>The employee in agency's Kansas City office is...</td>\n","      <td>kansa city missouri general service administra...</td>\n","      <td>employee agency kansa city office among hundre...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>Los Angeles (CNN) -- A medical doctor in Vanco...</td>\n","      <td>NEW: A Canadian doctor says she was part of a ...</td>\n","      <td>los angeles medical doctor vancouver british c...</td>\n","      <td>canadian doctor part team examining harry burk...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>(CNN) -- Police arrested another teen Thursday...</td>\n","      <td>Another arrest made in gang rape outside Calif...</td>\n","      <td>police arrested another teen thursday sixth su...</td>\n","      <td>another arrest made gang rape outside californ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-61053d4d-2110-42ed-be20-2a2e190c4ce1')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-61053d4d-2110-42ed-be20-2a2e190c4ce1 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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taking bronze britain disqualified faulty handover 26yearold bolt collected eight gold medal world championship equaling record held american trio carl lewis michael johnson allyson felix mention small matter six olympic title relay triumph followed individual success 100 200 meter russian capital proud ill continue work dominate long possible bolt previously expressed intention carry 2016 rio olympics victory never seriously doubt got baton safely hand ashmeade gatlin united state third leg runner rakieem salaam problem gatlin strayed lane struggled get full control baton never able get term bolt earlier jamaica woman underlined dominance sprint event winning 4x100m relay gold anchored shellyann fraserpryce like bolt completing triple quartet recorded championship record 4129 second well clear france crossed line second place 4273 second defending champion united state initially back bronze medal position losing time second handover alexandria anderson english gardner promoted silver france subsequently disqualified illegal handover british quartet initially fourth promoted bronze eluded men team fraserpryce like bolt aged 26 became first woman achieve three gold 100200 relay final action last day championship france teddy tamgho became third man leap 18m triple jump exceeding mark four centimeter take gold germany christina obergfoll finally took gold global level woman javelin five previous silver kenya asbel kiprop easily tactical men 1500m final kiprops compatriot eunice jepkoech sum surprise winner woman 800m bolt final dash golden glory brought eightday championship rousing finale host topped medal table united state criticism poor attendance luzhniki stadium concern pole vault gold medalist yelena isinbayeva made controversial remark support russia law make propagandizing nontraditional sexual relation among minor criminal offense later attempted clarify comment renewed call gay right group boycott 2014 winter game sochi next major sport event russia\n"," \n","--- Summary ---\n","usain bolt win third gold world championship anchor jamaica 4x100m relay victory eighth gold championship bolt jamaica double woman 4x100m relay\n"]}]},{"cell_type":"code","source":["X = add_text_length(dtf, \"text_clean\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4wyTQqFBRgTh","executionInfo":{"status":"ok","timestamp":1702480161433,"user_tz":-60,"elapsed":66348,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"21e1310e-50c0-4bb5-ef0b-a3a1a35e8001"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stderr","text":["Word Count: 100%|██████████| 20000/20000 [00:30<00:00, 651.36it/s]\n","Char Count: 100%|██████████| 20000/20000 [00:34<00:00, 578.37it/s]\n","Sentence Count: 100%|██████████| 20000/20000 [00:01<00:00, 17902.39it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","                            min         mean          max\n","char_count           103.000000  2260.120300  10038.00000\n","word_count            19.000000   363.755600   1309.00000\n","sentence_count         1.000000     1.000000      1.00000\n","avg_word_length        3.992945     6.210552     35.34507\n","avg_sentence_lenght   19.000000   363.755600   1309.00000\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}]},{"cell_type":"code","source":["def plot_distributions(dtf, x, max_cat=20, top=None, y=None, bins=None, figsize=(10,5)):\n","    ## univariate\n","    if y is None:\n","        fig, ax = plt.subplots(figsize=figsize)\n","        fig.suptitle(x, fontsize=15)\n","        ### categorical\n","        if dtf[x].nunique() <= max_cat:\n","            if top is None:\n","                dtf[x].reset_index().groupby(x).count().sort_values(by=\"index\").plot(kind=\"barh\", legend=False, ax=ax).grid(axis='x')\n","            else:\n","                dtf[x].reset_index().groupby(x).count().sort_values(by=\"index\").tail(top).plot(kind=\"barh\", legend=False, ax=ax).grid(axis='x')\n","            ax.set(ylabel=None)\n","        ### numerical\n","        else:\n","            sns.distplot(dtf[x], hist=True, kde=True, kde_kws={\"fill\":True}, ax=ax)\n","            ax.grid(True)\n","            ax.set(xlabel=None, yticklabels=[], yticks=[])\n","\n","    ## bivariate\n","    else:\n","        fig, ax = plt.subplots(nrows=1, ncols=2, sharex=False, sharey=False, figsize=figsize)\n","        fig.suptitle(x, fontsize=15)\n","        for i in dtf[y].unique():\n","            sns.distplot(dtf[dtf[y]==i][x], hist=True, kde=False, bins=bins, hist_kws={\"alpha\":0.8}, axlabel=\"\", ax=ax[0])\n","            sns.distplot(dtf[dtf[y]==i][x], hist=False, kde=True, kde_kws={\"fill\":True}, axlabel=\"\", ax=ax[1])\n","        ax[0].set(title=\"histogram\")\n","        ax[0].grid(True)\n","        ax[0].legend(dtf[y].unique())\n","        ax[1].set(title=\"density\")\n","        ax[1].grid(True)\n","    plt.show()\n","\n","\n","def evaluate_summary(y_test, predicted):\n","    rouge_score = rouge.Rouge()\n","    scores = rouge_score.get_scores(y_test, predicted, avg=True)\n","    score_1 = round(scores['rouge-1']['f'], 2)\n","    score_2 = round(scores['rouge-2']['f'], 2)\n","    score_L = round(scores['rouge-l']['f'], 2)\n","    print(\"rouge1:\", score_1, \"| rouge2:\", score_2, \"| rougeL:\", score_2,\n","          \"--> avg rouge:\", round(np.mean([score_1,score_2,score_L]), 2))\n","\n","\n"],"metadata":{"id":"CgPAJe6sd4pA","executionInfo":{"status":"ok","timestamp":1702480161904,"user_tz":-60,"elapsed":474,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":26,"outputs":[]},{"cell_type":"code","source":["plot_distributions(X, x=\"word_count\", figsize=(10,3))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":512},"id":"iMkYtj92eCni","executionInfo":{"status":"ok","timestamp":1702480206505,"user_tz":-60,"elapsed":355,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"1265654d-8cdc-40b8-ac73-c379dfc320cd"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-26-187eccae1a28>:15: UserWarning: \n","\n","`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n","\n","Please adapt your code to use either `displot` (a figure-level function with\n","similar flexibility) or `histplot` (an axes-level function for histograms).\n","\n","For a guide to updating your code to use the new functions, please see\n","https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n","\n","  sns.distplot(dtf[x], hist=True, kde=True, kde_kws={\"fill\":True}, ax=ax)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x300 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["y = add_text_length(dtf, \"y_clean\")\n","\n","plot_distributions(y, x=\"word_count\", max_cat=1, figsize=(10,3))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":686},"id":"RLgqppZMq6nD","executionInfo":{"status":"ok","timestamp":1702480217187,"user_tz":-60,"elapsed":9512,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"00288824-cfb6-4b88-9a55-dffb99d6fd75"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stderr","text":["Word Count: 100%|██████████| 20000/20000 [00:03<00:00, 5695.65it/s]\n","Char Count: 100%|██████████| 20000/20000 [00:05<00:00, 3922.49it/s]\n","Sentence Count: 100%|██████████| 20000/20000 [00:00<00:00, 57004.87it/s]\n","<ipython-input-26-187eccae1a28>:15: UserWarning: \n","\n","`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n","\n","Please adapt your code to use either `displot` (a figure-level function with\n","similar flexibility) or `histplot` (an axes-level function for histograms).\n","\n","For a guide to updating your code to use the new functions, please see\n","https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n","\n","  sns.distplot(dtf[x], hist=True, kde=True, kde_kws={\"fill\":True}, ax=ax)\n"]},{"output_type":"stream","name":"stdout","text":["\n","                       min        mean    max\n","char_count           35.00  164.703400  319.0\n","word_count            4.00   27.015400   50.0\n","sentence_count        1.00    1.000000    1.0\n","avg_word_length       4.15    6.117243   10.0\n","avg_sentence_lenght   4.00   27.015400   50.0\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x300 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["def display_string_matching(a, b, both=True, sentences=True, titles=[]):\n","    if sentences is True:\n","        lst_a, lst_b = utils_split_sentences(a, b)\n","    else:\n","        lst_a, lst_b = a.split(), b.split()\n","\n","    ## highlight a\n","    first_text = []\n","    for i in lst_a:\n","        if re.sub(r'[^\\w\\s]', '', i.lower()) in [re.sub(r'[^\\w\\s]', '', z.lower()) for z in lst_b]:\n","            first_text.append('<span style=\"background-color:rgba(255,215,0,0.3);\">' + i + '</span>')\n","        else:\n","            first_text.append(i)\n","    first_text = ' '.join(first_text)\n","\n","    ## highlight b\n","    second_text = []\n","    if both is True:\n","        for i in lst_b:\n","            if re.sub(r'[^\\w\\s]', '', i.lower()) in [re.sub(r'[^\\w\\s]', '', z.lower()) for z in lst_a]:\n","                second_text.append('<span style=\"background-color:rgba(255,215,0,0.3);\">' + i + '</span>')\n","            else:\n","                second_text.append(i)\n","    else:\n","        second_text.append(b)\n","    second_text = ' '.join(second_text)\n","\n","    ## concatenate\n","    if len(titles) > 0:\n","        first_text = \"<strong>\"+titles[0]+\"</strong><br>\"+first_text\n","    if len(titles) > 1:\n","        second_text = \"<strong>\"+titles[1]+\"</strong><br>\"+second_text\n","    else:\n","        second_text = \"---\"*65+\"<br><br>\"+second_text\n","    final_text = first_text +'<br><br>'+ second_text\n","    return final_text\n","\n","\n","def textrank(corpus, ratio=0.2):\n","    if type(corpus) is str:\n","        corpus = [corpus]\n","    lst_summaries = [gensim.summarization.summarize(txt, ratio=ratio) for txt in corpus]\n","    return lst_summaries\n"],"metadata":{"id":"FlPo3rBprG6O","executionInfo":{"status":"ok","timestamp":1702480348285,"user_tz":-60,"elapsed":224,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":31,"outputs":[]},{"cell_type":"code","source":["vectorizer = TfidfVectorizer(min_df=3, max_df=0.5)\n","vectorizer = vectorizer.fit(dtf.text_clean)"],"metadata":{"id":"x1423g-qyY2a","executionInfo":{"status":"ok","timestamp":1702480393977,"user_tz":-60,"elapsed":16516,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":32,"outputs":[]},{"cell_type":"code","source":["def summarize_doc(corpus, num_sentences=2):\n","  sentences = nltk.sent_tokenize(corpus)\n","  norm_sentences = normalize_corpus(sentences)\n","  dt_matrix = vectorizer.transform(norm_sentences)\n","  dt_matrix = dt_matrix.toarray() # from sparse to matrix\n","  similiarity_matrix = np.matmul(dt_matrix, dt_matrix.T)\n","\n","  similiarity_graph = networkx.from_numpy_array(similiarity_matrix)\n","  scores = networkx.pagerank(similiarity_graph)\n","\n","  ranked_sentences = sorted(((score, index) for index, score in scores.items()),\n","                            reverse=True)\n","  # import ipdb\n","  # ipdb.set_trace()\n","  top_sentence_indices = [ranked_sentences[index][1] for index in range(num_sentences)]\n","  top_sentence_indices.sort()\n","\n","  return ' '.join(np.array(sentences)[top_sentence_indices])\n"],"metadata":{"id":"_z_v81Qk1IMN","executionInfo":{"status":"ok","timestamp":1702481152452,"user_tz":-60,"elapsed":237,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":47,"outputs":[]},{"cell_type":"code","source":["!pip install -q ipdb"],"metadata":{"id":"fq8PJHtJxaFw","executionInfo":{"status":"ok","timestamp":1702481020404,"user_tz":-60,"elapsed":11033,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":43,"outputs":[]},{"cell_type":"code","source":["from functools import partial\n","\n","num_sentences = 2\n","summarize_final = partial(summarize_doc, num_sentences=num_sentences)\n","\n","dtf_test = dtf.iloc[:1000,:]\n","tqdm.pandas(desc='Summarizing text')\n","predicted = dtf_test[\"text\"].progress_apply(summarize_final)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6VAzn8DErHrE","executionInfo":{"status":"ok","timestamp":1702481076543,"user_tz":-60,"elapsed":56148,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"3fded38b-d8a6-4854-88dc-de14e5c2cec4"},"execution_count":44,"outputs":[{"output_type":"stream","name":"stderr","text":["Summarizing text: 100%|██████████| 1000/1000 [00:56<00:00, 17.79it/s]\n"]}]},{"cell_type":"code","source":["'''\n","Find the matching substrings in 2 strings.\n",":parameter\n","    :param a: string - raw text\n","    :param b: string - raw text\n",":return\n","    2 lists used in to display matches\n","'''\n","def utils_split_sentences(a, b):\n","    ## find clean matches\n","    match = difflib.SequenceMatcher(isjunk=None, a=a, b=b, autojunk=True)\n","    lst_match = [block for block in match.get_matching_blocks() if block.size > 20]\n","\n","    ## difflib didn't find any match\n","    if len(lst_match) == 0:\n","        lst_a, lst_b = nltk.sent_tokenize(a), nltk.sent_tokenize(b)\n","\n","    ## work with matches\n","    else:\n","        first_m, last_m = lst_match[0], lst_match[-1]\n","\n","        ### a\n","        string = a[0 : first_m.a]\n","        lst_a = [t for t in nltk.sent_tokenize(string)]\n","        for n in range(len(lst_match)):\n","            m = lst_match[n]\n","            string = a[m.a : m.a+m.size]\n","            lst_a.append(string)\n","            if n+1 < len(lst_match):\n","                next_m = lst_match[n+1]\n","                string = a[m.a+m.size : next_m.a]\n","                lst_a = lst_a + [t for t in nltk.sent_tokenize(string)]\n","            else:\n","                break\n","        string = a[last_m.a+last_m.size :]\n","        lst_a = lst_a + [t for t in nltk.sent_tokenize(string)]\n","\n","        ### b\n","        string = b[0 : first_m.b]\n","        lst_b = [t for t in nltk.sent_tokenize(string)]\n","        for n in range(len(lst_match)):\n","            m = lst_match[n]\n","            string = b[m.b : m.b+m.size]\n","            lst_b.append(string)\n","            if n+1 < len(lst_match):\n","                next_m = lst_match[n+1]\n","                string = b[m.b+m.size : next_m.b]\n","                lst_b = lst_b + [t for t in nltk.sent_tokenize(string)]\n","            else:\n","                break\n","        string = b[last_m.b+last_m.size :]\n","        lst_b = lst_b + [t for t in nltk.sent_tokenize(string)]\n","\n","    return lst_a, lst_b\n","\n","\n","'''\n","Highlights the matched strings in text.\n",":parameter\n","    :param a: string - raw text\n","    :param b: string - raw text\n","    :param both: bool - search a in b and, if True, viceversa\n","    :param sentences: bool - if False matches single words\n",":return\n","    text html, it can be visualized on notebook with display(HTML(text))\n","'''\n","def display_string_matching(a, b, both=True, sentences=True, titles=[]):\n","    if sentences is True:\n","        lst_a, lst_b = utils_split_sentences(a, b)\n","    else:\n","        lst_a, lst_b = a.split(), b.split()\n","\n","    ## highlight a\n","    first_text = []\n","    for i in lst_a:\n","        if re.sub(r'[^\\w\\s]', '', i.lower()) in [re.sub(r'[^\\w\\s]', '', z.lower()) for z in lst_b]:\n","            first_text.append('<span style=\"background-color:rgba(255,215,0,0.3);\">' + i + '</span>')\n","        else:\n","            first_text.append(i)\n","    first_text = ' '.join(first_text)\n","\n","    ## highlight b\n","    second_text = []\n","    if both is True:\n","        for i in lst_b:\n","            if re.sub(r'[^\\w\\s]', '', i.lower()) in [re.sub(r'[^\\w\\s]', '', z.lower()) for z in lst_a]:\n","                second_text.append('<span style=\"background-color:rgba(255,215,0,0.3);\">' + i + '</span>')\n","            else:\n","                second_text.append(i)\n","    else:\n","        second_text.append(b)\n","    second_text = ' '.join(second_text)\n","\n","    ## concatenate\n","    if len(titles) > 0:\n","        first_text = \"<strong>\"+titles[0]+\"</strong><br>\"+first_text\n","    if len(titles) > 1:\n","        second_text = \"<strong>\"+titles[1]+\"</strong><br>\"+second_text\n","    else:\n","        second_text = \"---\"*65+\"<br><br>\"+second_text\n","    final_text = first_text +'<br><br>'+ second_text\n","    return final_text\n"],"metadata":{"id":"fWTNZ8SZrJk3","executionInfo":{"status":"ok","timestamp":1702481076889,"user_tz":-60,"elapsed":351,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":45,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"yAO14w_e3zbW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["i = 1\n","match = display_string_matching(dtf_test[\"y\"].iloc[i], predicted.iloc[i], both=True, sentences=False,\n","                                titles=[\"Real Summary\", \"Predicted Summary\"])\n","\n","from IPython.core.display import display, HTML\n","display(HTML(match))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":156},"id":"yUQVL7y4rJfm","executionInfo":{"status":"ok","timestamp":1702481085903,"user_tz":-60,"elapsed":212,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"a77bdf17-515e-4aa6-c3b1-b6572b077e27"},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<strong>Real Summary</strong><br><span style=\"background-color:rgba(255,215,0,0.3);\">Usain</span> <span style=\"background-color:rgba(255,215,0,0.3);\">Bolt</span> wins <span style=\"background-color:rgba(255,215,0,0.3);\">third</span> <span style=\"background-color:rgba(255,215,0,0.3);\">gold</span> of <span style=\"background-color:rgba(255,215,0,0.3);\">world</span> championship <span style=\"background-color:rgba(255,215,0,0.3);\">.</span> Anchors <span style=\"background-color:rgba(255,215,0,0.3);\">Jamaica</span> <span style=\"background-color:rgba(255,215,0,0.3);\">to</span> <span style=\"background-color:rgba(255,215,0,0.3);\">4x100m</span> <span style=\"background-color:rgba(255,215,0,0.3);\">relay</span> <span style=\"background-color:rgba(255,215,0,0.3);\">victory</span> <span style=\"background-color:rgba(255,215,0,0.3);\">.</span> Eighth <span style=\"background-color:rgba(255,215,0,0.3);\">gold</span> at <span style=\"background-color:rgba(255,215,0,0.3);\">the</span> <span style=\"background-color:rgba(255,215,0,0.3);\">championships</span> for <span style=\"background-color:rgba(255,215,0,0.3);\">Bolt</span> <span style=\"background-color:rgba(255,215,0,0.3);\">.</span> <span style=\"background-color:rgba(255,215,0,0.3);\">Jamaica</span> double up <span style=\"background-color:rgba(255,215,0,0.3);\">in</span> women's <span style=\"background-color:rgba(255,215,0,0.3);\">4x100m</span> <span style=\"background-color:rgba(255,215,0,0.3);\">relay</span> <span style=\"background-color:rgba(255,215,0,0.3);\">.</span><br><br><strong>Predicted Summary</strong><br>(CNN) <span style=\"background-color:rgba(255,215,0,0.3);\">--</span> <span style=\"background-color:rgba(255,215,0,0.3);\">Usain</span> <span style=\"background-color:rgba(255,215,0,0.3);\">Bolt</span> rounded off <span style=\"background-color:rgba(255,215,0,0.3);\">the</span> <span style=\"background-color:rgba(255,215,0,0.3);\">world</span> <span style=\"background-color:rgba(255,215,0,0.3);\">championships</span> Sunday by claiming his <span style=\"background-color:rgba(255,215,0,0.3);\">third</span> <span style=\"background-color:rgba(255,215,0,0.3);\">gold</span> <span style=\"background-color:rgba(255,215,0,0.3);\">in</span> Moscow as he anchored <span style=\"background-color:rgba(255,215,0,0.3);\">Jamaica</span> <span style=\"background-color:rgba(255,215,0,0.3);\">to</span> <span style=\"background-color:rgba(255,215,0,0.3);\">victory</span> <span style=\"background-color:rgba(255,215,0,0.3);\">in</span> <span style=\"background-color:rgba(255,215,0,0.3);\">the</span> men's <span style=\"background-color:rgba(255,215,0,0.3);\">4x100m</span> relay..Earlier, Jamaica's women underlined their dominance <span style=\"background-color:rgba(255,215,0,0.3);\">in</span> <span style=\"background-color:rgba(255,215,0,0.3);\">the</span> sprint events by winning <span style=\"background-color:rgba(255,215,0,0.3);\">the</span> <span style=\"background-color:rgba(255,215,0,0.3);\">4x100m</span> <span style=\"background-color:rgba(255,215,0,0.3);\">relay</span> <span style=\"background-color:rgba(255,215,0,0.3);\">gold,</span> anchored by Shelly-Ann Fraser-Pryce, who like <span style=\"background-color:rgba(255,215,0,0.3);\">Bolt</span> was completing a triple."]},"metadata":{}}]},{"cell_type":"code","source":["match = display_string_matching(dtf_test[\"text\"][i], predicted[i], both=True, sentences=True,\n","                                titles=[\"Full Text\", \"Predicted Summary\"])\n","display(HTML(match))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":555},"id":"EcmdQAhB5irn","executionInfo":{"status":"ok","timestamp":1702481157567,"user_tz":-60,"elapsed":6,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"2a37df44-7a6d-40ff-fb97-b53adc9f11b3"},"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<strong>Full Text</strong><br><span style=\"background-color:rgba(255,215,0,0.3);\">(CNN) -- Usain Bolt rounded off the world championships Sunday by claiming his third gold in Moscow as he anchored Jamaica to victory in the men's 4x100m relay.</span>  The fastest man in the world charged clear of United States rival Justin Gatlin as the Jamaican quartet of Nesta Carter, Kemar Bailey-Cole, Nickel Ashmeade and Bolt won in 37.36 seconds. The U.S finished second in 37.56 seconds with Canada taking the bronze after Britain were disqualified for a faulty handover. The 26-year-old Bolt has now collected eight gold medals at world championships, equaling the record held by American trio Carl Lewis, Michael Johnson and Allyson Felix, not to mention the small matter of six Olympic titles. The relay triumph followed individual successes in the 100 and 200 meters in the Russian capital. \"I'm proud of myself and I'll continue to work to dominate for as long as possible,\" Bolt said, having previously expressed his intention to carry on until the 2016 Rio Olympics. Victory was never seriously in doubt once he got the baton safely in hand from Ashmeade, while Gatlin and the United States third leg runner Rakieem Salaam had problems. Gatlin strayed out of his lane as he struggled to get full control of their baton and was never able to get on terms with Bolt. <span style=\"background-color:rgba(255,215,0,0.3);\">Earlier, Jamaica's women underlined their dominance in the sprint events by winning the 4x100m relay gold, anchored by Shelly-Ann Fraser-Pryce, who like Bolt was completing a triple.</span>  Their quartet recorded a championship record of 41.29 seconds, well clear of France, who crossed the line in second place in 42.73 seconds. Defending champions, the United States, were initially back in the bronze medal position after losing time on the second handover between Alexandria Anderson and English Gardner, but promoted to silver when France were subsequently disqualified for an illegal handover. The British quartet, who were initially fourth, were promoted to the bronze which eluded their men's team. Fraser-Pryce, like Bolt aged 26, became the first woman to achieve three golds in the 100-200 and the relay. In other final action on the last day of the championships, France's Teddy Tamgho became the third man to leap over 18m in the triple jump, exceeding the mark by four centimeters to take gold. Germany's Christina Obergfoll finally took gold at global level in the women's javelin after five previous silvers, while Kenya's Asbel Kiprop easily won a tactical men's 1500m final. Kiprop's compatriot Eunice Jepkoech Sum was a surprise winner of the women's 800m. Bolt's final dash for golden glory brought the eight-day championship to a rousing finale, but while the hosts topped the medal table from the United States there was criticism of the poor attendances in the Luzhniki Stadium. There was further concern when their pole vault gold medalist Yelena Isinbayeva made controversial remarks in support of Russia's new laws, which make \"the propagandizing of non-traditional sexual relations among minors\" a criminal offense. She later attempted to clarify her comments, but there were renewed calls by gay rights groups for a boycott of the 2014 Winter Games in Sochi, the next major sports event in Russia.<br><br><strong>Predicted Summary</strong><br><span style=\"background-color:rgba(255,215,0,0.3);\">(CNN) -- Usain Bolt rounded off the world championships Sunday by claiming his third gold in Moscow as he anchored Jamaica to victory in the men's 4x100m relay.</span> . <span style=\"background-color:rgba(255,215,0,0.3);\">Earlier, Jamaica's women underlined their dominance in the sprint events by winning the 4x100m relay gold, anchored by Shelly-Ann Fraser-Pryce, who like Bolt was completing a triple.</span>"]},"metadata":{}}]},{"cell_type":"code","source":["evaluate_summary(dtf_test[\"y\"][i], predicted[i])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f4Pg1Cnu84Ek","executionInfo":{"status":"ok","timestamp":1702481174053,"user_tz":-60,"elapsed":5,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"11b877a8-3367-4715-fff0-c70f5c542418"},"execution_count":49,"outputs":[{"output_type":"stream","name":"stdout","text":["rouge1: 0.39 | rouge2: 0.1 | rougeL: 0.1 --> avg rouge: 0.26\n"]}]},{"cell_type":"markdown","source":["## Abstractive Summarization"],"metadata":{"id":"gC_tkCF17YYb"}},{"cell_type":"code","source":["!pip install txtai\n","from txtai.pipeline import Summary"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rSqxKdTe7abA","executionInfo":{"status":"ok","timestamp":1702481250726,"user_tz":-60,"elapsed":21591,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"3a069c27-af8c-407a-d8eb-94a24cbd1680"},"execution_count":50,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting txtai\n","  Downloading txtai-6.2.0-py3-none-any.whl (198 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m198.3/198.3 kB\u001b[0m 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sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.12.1->txtai) (1.12)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.12.1->txtai) (3.2.1)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.12.1->txtai) (3.1.2)\n","Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.12.1->txtai) (2.1.0)\n","Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.22.0->txtai) (0.15.0)\n","Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.22.0->txtai) (0.4.1)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.12.1->txtai) (2.1.3)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.9.0->txtai) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.9.0->txtai) (3.6)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.9.0->txtai) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.9.0->txtai) (2023.11.17)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.12.1->txtai) (1.3.0)\n","Installing collected packages: faiss-cpu, txtai\n","Successfully installed faiss-cpu-1.7.4 txtai-6.2.0\n"]}]},{"cell_type":"markdown","source":[" Go here: https://huggingface.co/models?pipeline_tag=summarization and chose any model you want, by default it will use a distilbart"],"metadata":{"id":"JwDRSGOt7iXt"}},{"cell_type":"code","source":["summary = Summary('facebook/bart-large-cnn')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":209,"referenced_widgets":["7fc01e3bbdef42c8a3bdc03a3e1be2ff","327caa453e674216ac0dd1d768bc3a84","7d3a0edc49b54c1dac90a5b58046a55e","73920bbb489f4ec4a00526f6c3dbf463","7376f614496b4275b317f265425b6459","34f1ce6ef8e44880a99109d010f526d1","2f2038d19de14d099c09e97e080f76d0","52ec49dbf9d7445892a66089f48ea7c3","9f0f3ca24fbb4a71b6783bfe7841e8b1","a6f62520779a477f95be9d69ea14d371","ab70ec70945d4717b25a55c9756d5caa","856228569cd9440ea9b23f644b1c9a32","6e4d7624ecec41cd86714a4363b30d68","bbcb7e1383f54585bbfef12e079045b2","2552ea1644074698b17501248dcb25b5","228533dca9fb42c3a0aff9dd71cb3823","c19218faaec74d4abe0b067aa2126b2c","3cc6c88ccadd4eb0ae084c55b004c946","8ba01adb680f4e218c2630d05d773d24","8b0f0987d4124a00889e2753752fc079","488f057ad4a84a8e8663d6f0c981f8f3","6cbbff7840534fc8ad3a67a235609d29","6cf773dc752047a592419f825f637e8d","a4331d84517c4cc1a7e55259d69e9c66","44c22f62bae64d7fae681865e9e91d41","3c1afcc3640d468ba7da9ea078539689","ca24fb0607404560a902bd881a570465","525d607ea1124d8b99d034cb218676fa","b2392e0f17f94118913e5e80889c930d","b02f570260604628a7a06d76db2f6812","92b08da5f39e41e284f86e85637f49c1","7c9eacd8b20b434c94011a122c9b2401","3af31dcf995e4e5d8a2e34c15aeeb0b0","4764aa5ee6f94215bc8ac2f29e262a51","c3d98ab51b9d494698e265e428d0ff6d","08e493d26c954934b209c9e54fa80fd3","3fe9d2549bbf49c980aa383335be2651","4d5742a29ddd4b89ab97c69053f48e99","80c46e1c3d6148aebf7279b78d5ea1eb","0b5b45fd017541b9baccef5333e60aff","3d053e2a742347e4a0397683983598bd","dde9db5be1fe4e83be24fba73f0b5788","e61dedd308dd4cf4999f86731e924995","3bc480ff560a47cdb6f0e2c04bd98c84","e47ea0c98a6441afb0e75a6a5f2874ce","b1e76ffe6d5a433bad9de277f1cdc5d1","3b6d0e5fae19436e9f5d58c3b6e8a756","ed1039073a5d41a0aa4aec8f40950ced","375abd2642e54faa94b3c0111646b9f5","b0860c68d8764ca2ab39514da9dadea5","ed8ad13cce3347d4814a1ce3dd3bd23d","ac753a40f2d645ffbbb00b8efe9b74a0","43e528e2570c4031963808bee41d8809","1982b7cc170a403fb82e4904bfedf889","bd72c1800a1e4380950e32a95e7100e2","b57f5729b4df4421927d7146c47bfaa1","5edf4ecaef6e446dae87944d2c0f26a6","d33c9b0ee5a34dee9bef52053ebff773","01f6e326fc9a4965aec68110200f7c63","2d7ab51d642f45d0b75002bd710799e0","a61a95e3d1a5497b9d719ec164ddcfdc","5e2aefca1fbd451bab0ab9865f1d7815","fbe46d9ccbe9424792ea230632c70a34","e55935cc92dd4328a48a3f6b5184052e","a56c49c752584432883903886227d3ad","7cbe17bdced14a97a528337f39969c2c"]},"id":"qjzOFE617d3Z","executionInfo":{"status":"ok","timestamp":1702481549285,"user_tz":-60,"elapsed":28834,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"06e5e678-313d-4d31-839b-a3bb893714dc"},"execution_count":51,"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/1.58k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7fc01e3bbdef42c8a3bdc03a3e1be2ff"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"856228569cd9440ea9b23f644b1c9a32"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6cf773dc752047a592419f825f637e8d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4764aa5ee6f94215bc8ac2f29e262a51"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e47ea0c98a6441afb0e75a6a5f2874ce"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b57f5729b4df4421927d7146c47bfaa1"}},"metadata":{}}]},{"cell_type":"code","source":["text = (\"Search is the base of many applications. Once data starts to pile up, users want to be able to find it. It’s the foundation \"\n","       \"of the internet and an ever-growing challenge that is never solved or done. The field of Natural Language Processing (NLP) is \"\n","       \"rapidly evolving with a number of new developments. Large-scale general language models are an exciting new capability \"\n","       \"allowing us to add amazing functionality quickly with limited compute and people. Innovation continues with new models \"\n","       \"and advancements coming in at what seems a weekly basis. This article introduces txtai, an AI-powered search engine \"\n","       \"that enables Natural Language Understanding (NLU) based search in any application.\"\n",")\n","\n","summary(text, maxlength=30)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"o7L8JIVz7u5t","executionInfo":{"status":"ok","timestamp":1702481588294,"user_tz":-60,"elapsed":4207,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"8ac5b3d7-2145-4bde-ef60-df14ee65cfcf"},"execution_count":52,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'The field of Natural Language Processing (NLP) is rapidly evolving with a number of new developments. Large-scale general language models are'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":52}]},{"cell_type":"code","source":["i = 0\n","summarized_text = summary(dtf_test[\"text\"][i], maxlength=40)"],"metadata":{"id":"a4qtyZlG9RFR","executionInfo":{"status":"ok","timestamp":1702481759500,"user_tz":-60,"elapsed":1726,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":57,"outputs":[]},{"cell_type":"code","source":["match = display_string_matching(dtf_test[\"y\"][i], summarized_text, both=True, sentences=True,\n","                                titles=[\"Summary\", \"Predicted Summary\"])\n","display(HTML(match))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":156},"id":"lxgFzPAz9W7G","executionInfo":{"status":"ok","timestamp":1702481759501,"user_tz":-60,"elapsed":7,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"59d01809-81a7-4da2-bf9f-4e848d8911fe"},"execution_count":58,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<strong>Summary</strong><br>Syrian official: Obama climbed to the top of the tree, \"doesn't know how to get down\"\n","Obama sends a letter to the heads of the House and Senate . Obama to seek congressional approval on military action against Syria . Aim is to determine whether CW were used, not by whom, says U.N. spokesman .<br><br><strong>Predicted Summary</strong><br>U. N. inspectors leave Syria, carrying evidence that will determine whether chemical weapons were used. \"The issues are too big for business as usual,\" President Barack Obama says. U."]},"metadata":{}}]},{"cell_type":"code","source":["match = display_string_matching(dtf_test[\"text\"][i], summarized_text, both=True, sentences=True,\n","                                titles=[\"Full Text\", \"Predicted Summary\"])\n","display(HTML(match))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"OXlxHXJL80KN","executionInfo":{"status":"ok","timestamp":1702481764102,"user_tz":-60,"elapsed":348,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"e64d8f98-1a60-41d4-fab8-f74387047d03"},"execution_count":59,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<strong>Full Text</strong><br>It's official: U.S. President Barack Obama wants lawmakers to weigh in on whether to use military force in Syria. Obama sent a letter to the heads of the House and Senate on Saturday night, hours after announcing that he believes military action against Syrian targets is the right step to take over the alleged use of chemical weapons. The proposed legislation from Obama asks Congress to approve the use of military force \"to deter, disrupt, prevent and degrade the potential for future uses of chemical weapons or other weapons of mass destruction.\" It's a step that is set to turn an international crisis into a fierce domestic political battle. There are key questions looming over the debate: What did U.N. weapons inspectors find in Syria? What happens if Congress votes no? And how will the Syrian government react? In a televised address from the White House Rose Garden earlier Saturday, the president said he would take his case to Congress, not because he has to -- but because he wants to. \"While I believe I have the authority to carry out this military action without specific congressional authorization, I know that the country will be stronger if we take this course, and our actions will be even more effective,\" he said. \"We should have this debate, because the issues are too big for business as usual.\" Obama said top congressional leaders had agreed to schedule a debate when the body returns to Washington on September 9. The Senate Foreign Relations Committee will hold a hearing over the matter on Tuesday, Sen. Robert Menendez said. Transcript: Read Obama's full remarks . Syrian crisis: Latest developments . U.N. inspectors leave Syria . Obama's remarks came shortly after U.N. inspectors left <span style=\"background-color:rgba(255,215,0,0.3);\"> Syria, carrying evidence that will determine whether chemical weapons were used</span>  in an attack early last week in a Damascus suburb. \"The aim of the game here, the mandate, is very clear -- and that is to ascertain whether chemical weapons were used -- and not by whom,\" U.N. spokesman Martin Nesirky told reporters on Saturday. But who used the weapons in the reported toxic gas attack in a Damascus suburb on August 21 has been a key point of global debate over the Syrian crisis. Top U.S. officials have said there's no doubt that the Syrian government was behind it, while Syrian officials have denied responsibility and blamed jihadists fighting with the rebels. British and U.S. intelligence reports say the attack involved chemical weapons, but U.N. officials have stressed the importance of waiting for an official report from inspectors. The inspectors will share their findings with U.N. Secretary-General Ban Ki-moon Ban, who has said he wants to wait until the U.N. team's final report is completed before presenting it to the U.N. Security Council. The Organization for the Prohibition of Chemical Weapons, which nine of the inspectors belong to, said Saturday that it could take up to three weeks to analyze the evidence they collected. \"It needs time to be able to analyze the information and the samples,\" Nesirky said. He noted that Ban has repeatedly said there is no alternative to a political solution to the crisis in Syria, and that \"a military solution is not an option.\" Bergen:  Syria is a problem from hell for the U.S. Obama: 'This menace must be confronted' Obama's senior advisers have debated the next steps to take, and the president's comments Saturday came amid mounting political pressure over the situation in Syria. Some U.S. lawmakers have called for immediate action while others warn of stepping into what could become a quagmire. Some global leaders have expressed support, but the British Parliament's vote against military action earlier this week was a blow to Obama's hopes of getting strong backing from key NATO allies. On Saturday, Obama proposed what he said would be a limited military action against Syrian President Bashar al-Assad. Any military attack would not be open-ended or include U.S. ground forces, he said. Syria's alleged use of chemical weapons earlier this month \"is an assault on human dignity,\" the president said. A failure to respond with force, Obama argued,  \"could lead to escalating use of chemical weapons or their proliferation to terrorist groups who would do our people harm. In a world with many dangers, this menace must be confronted.\" Syria missile strike: What would happen next? Map: U.S. and allied assets around Syria . Obama decision came Friday night . On Friday night, the president made a last-minute decision to consult lawmakers. What will happen if they vote no? It's unclear. A senior administration official told CNN that Obama has the authority to act without Congress -- even if Congress rejects his request for authorization to use force. Obama on Saturday continued to shore up support for a strike on the al-Assad government. He spoke by phone with French President Francois Hollande before his Rose Garden speech. \"The two leaders agreed that the international community must deliver a resolute message to the Assad regime -- and others who would consider using chemical weapons -- that these crimes are unacceptable and those who violate this international norm will be held accountable by the world,\" the White House said. Meanwhile, as uncertainty loomed over how Congress would weigh in, U.S. military officials said they remained at the ready. 5 key assertions: U.S. intelligence report on Syria . Syria: Who wants what after chemical weapons horror . Reactions mixed to Obama's speech . A spokesman for the Syrian National Coalition said that the opposition group was disappointed by Obama's announcement. \"Our fear now is that the lack of action could embolden the regime and they repeat his attacks in a more serious way,\" said spokesman Louay Safi. \"So we are quite concerned.\" Some members of Congress applauded Obama's decision. House Speaker John Boehner, Majority Leader Eric Cantor, Majority Whip Kevin McCarthy and Conference Chair Cathy McMorris Rodgers issued a statement Saturday praising the president. \"Under the Constitution, the responsibility to declare war lies with Congress,\" the Republican lawmakers said. \"We are glad the president is seeking authorization for any military action in Syria in response to serious, substantive questions being raised.\" More than 160 legislators, including 63 of Obama's fellow Democrats, had signed letters calling for either a vote or at least a \"full debate\" before any U.S. action. British Prime Minister David Cameron, whose own attempt to get lawmakers in his country to support military action in Syria failed earlier this week, responded to Obama's speech in a Twitter post Saturday. \"I understand and support Barack Obama's position on Syria,\" Cameron said. An influential lawmaker in Russia -- which has stood by Syria and criticized the United States -- had his own theory. \"The main reason Obama is turning to the Congress:  the military operation did not get enough support either in the world, among allies of the US or in the United States itself,\" Alexei Pushkov, chairman of the international-affairs committee of the Russian State Duma, said in a Twitter post. In the United States, scattered groups of anti-war protesters around the country took to the streets Saturday. \"Like many other Americans...we're just tired of the United States getting involved and invading and bombing other countries,\" said Robin Rosecrans, who was among hundreds at a Los Angeles demonstration. What do Syria's neighbors think? Why Russia, China, Iran stand by Assad . Syria's government unfazed . After Obama's speech, a military and political analyst on Syrian state TV said Obama is \"embarrassed\" that Russia opposes military action against Syria, is \"crying for help\" for someone to come to his rescue and is facing two defeats -- on the political and military levels. Syria's prime minister appeared unfazed by the saber-rattling. \"The Syrian Army's status is on maximum readiness and fingers are on the trigger to confront all challenges,\" Wael Nader al-Halqi said during a meeting with a delegation of Syrian expatriates from Italy, according to a banner on Syria State TV that was broadcast prior to Obama's address. An anchor on Syrian state television said Obama \"appeared to be preparing for an aggression on Syria based on repeated lies.\" A top Syrian diplomat told the state television network that Obama was facing pressure to take military action from Israel, Turkey, some Arabs and right-wing extremists in the United States. \"I think he has done well by doing what Cameron did in terms of taking the issue to Parliament,\" said Bashar Jaafari, Syria's ambassador to the United Nations. Both Obama and Cameron, he said, \"climbed to the top of the tree and don't know how to get down.\" The Syrian government has denied that it used chemical weapons in the August 21 attack, saying that jihadists fighting with the rebels used them in an effort to turn global sentiments against it. British intelligence had put the number of people killed in the attack at more than 350. On Saturday, Obama said \"all told, well over 1,000 people were murdered.\" U.S. Secretary of State John Kerry on Friday cited a death toll of 1,429, more than 400 of them children. No explanation was offered for the discrepancy. Iran: U.S. military action in Syria would spark 'disaster' Opinion: Why strikes in Syria are a bad idea .<br><br><strong>Predicted Summary</strong><br>U. N. inspectors leave <span style=\"background-color:rgba(255,215,0,0.3);\"> Syria, carrying evidence that will determine whether chemical weapons were used</span> . \"The issues are too big for business as usual,\" President Barack Obama says. U."]},"metadata":{}}]},{"cell_type":"code","source":["evaluate_summary(dtf_test[\"y\"][i], summarized_text)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ERKIaw4Z9c8w","executionInfo":{"status":"ok","timestamp":1702481792591,"user_tz":-60,"elapsed":816,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"21538a66-61d5-4ac3-8120-d472f448ab87"},"execution_count":60,"outputs":[{"output_type":"stream","name":"stdout","text":["rouge1: 0.2 | rouge2: 0.08 | rougeL: 0.08 --> avg rouge: 0.16\n"]}]},{"cell_type":"markdown","source":["# Bert Topic Modeling"],"metadata":{"id":"PwCYpZvFt3_x"}},{"cell_type":"code","execution_count":61,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AIXK-tZIqn3N","executionInfo":{"status":"ok","timestamp":1702481997105,"user_tz":-60,"elapsed":81775,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"d3a6be74-029c-46b2-841c-d98120e66617"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting bertopic\n","  Downloading bertopic-0.16.0-py2.py3-none-any.whl (154 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.1/154.1 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.10/dist-packages (from bertopic) (1.23.5)\n","Collecting hdbscan>=0.8.29 (from bertopic)\n","  Downloading hdbscan-0.8.33.tar.gz (5.2 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m29.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","  Preparing metadata (pyproject.toml) ... 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collected packages: sentencepiece, cython, pynndescent, hdbscan, umap-learn, sentence-transformers, bertopic\n","  Attempting uninstall: cython\n","    Found existing installation: Cython 3.0.6\n","    Uninstalling Cython-3.0.6:\n","      Successfully uninstalled Cython-3.0.6\n","Successfully installed bertopic-0.16.0 cython-0.29.36 hdbscan-0.8.33 pynndescent-0.5.11 sentence-transformers-2.2.2 sentencepiece-0.1.99 umap-learn-0.5.5\n"]}],"source":["!pip install -q bertopic"]},{"cell_type":"code","source":["from sklearn.datasets import fetch_20newsgroups\n","docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']"],"metadata":{"id":"YOIrsvawW8BS","executionInfo":{"status":"ok","timestamp":1702482021014,"user_tz":-60,"elapsed":23397,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":62,"outputs":[]},{"cell_type":"code","source":["print(docs[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e7ALg7InXl5B","executionInfo":{"status":"ok","timestamp":1702482233232,"user_tz":-60,"elapsed":298,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"8b8d750b-0eef-4f76-e51a-54e4a95f7554"},"execution_count":64,"outputs":[{"output_type":"stream","name":"stdout","text":["My brother is in the market for a high-performance video card that supports\n","VESA local bus with 1-2MB RAM.  Does anyone have suggestions/ideas on:\n","\n","  - Diamond Stealth Pro Local Bus\n","\n","  - Orchid Farenheit 1280\n","\n","  - ATI Graphics Ultra Pro\n","\n","  - Any other high-performance VLB card\n","\n","\n","Please post or email.  Thank you!\n","\n","  - Matt\n","\n"]}]},{"cell_type":"code","source":["from bertopic import BERTopic"],"metadata":{"id":"YuBDhL6fYdbB","executionInfo":{"status":"ok","timestamp":1702482328344,"user_tz":-60,"elapsed":29275,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":65,"outputs":[]},{"cell_type":"code","source":["topic_model = BERTopic(language='english', calculate_probabilities=True, verbose=True)\n","\n","topics, probs = topic_model.fit_transform(docs)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":656,"referenced_widgets":["124ce044b3864ed59225b4c330166e6f","04fe772c1f5d481b8d05a82aff7a9a5c","c0dc6f70ee04431a931ab983d1e60333","86c2b80dea134ed4a1b27abb7984c485","11d2da49435848159555a5aa51f60466","5ffacb875ea541a98219bb2f5438df55","3447fb49f86142bc900b40517ec9cf62","bd8ed3fa53494b5494ca43e6f73c4ea5","4964f06bcdfc4ade9c9214439f528690","033b8a0147c3480cb4a52e0120399569","4dc6235a204a46eabce74497577df69a","b370c7d7201d4472bb14e61c1d228f87","93e2535fc72146b195674adb8311d8c0","5ca20879dfa14b6c818b842ab2c1eac1","16eb2431295a4328a10175f10f0a2965","b8ad22a7c4404ab89ae75b92e8b50b1a","4681d33b9831476ea1c9ee531d0c940d","928735e2d70546e4ac8ef0001826cdfb","664b98523c2b46639abab0ead48310b0","ab870a1f3b0e4c2d9cb17e1a838dec9f","780381bbfd024cfe96d71b5c30368f52","66420b72296f45da9dc8efb721180975","06ef10fad0e44809b470d407ecf56fb7","742afdf2566c4a0ca79c1fa2dde950d3","32ddfc0939ce4a88906e402d25cf4f29","4cb0476046ff478ab764f1d5bae5c6cf","234545e357b44366baf6faecf5a90d24","f76f1d3f34dc403fbd983fd619e3c5cf","001c8fcd3ed54fb9b9928aeeeb643cc4","da908eec760449d7a7686043157a5c17","5ea05b4d2cdb40179380de4e405851da","9cbe94f9a17b4a9bad0c5f9d7eeae151","d3716173ff4046508938b0bc0bc93965","ecff02c753104aca94956029736dd615","3640f9213fb245b6a8f10b1c7034e321","5e1ee7305cdc4b8e9c0ddc7feb48027a","7a5cb31772e347f4b8f033676dfb3447","ca4b0182e17f4f4888f38314ea89f593","f170ed7de46548e4ac4e99eab9dbb59a","f8f5726c127d4673ad3431b6da845d65","3c24ff05c1df47cc9fb7340fc2688829","47d0901ae32b4c6eacaf069420bb9816","f77385659b2f465a9c8e6e49558082aa","b75a343a41f84341a7658c48efddb7ab","019d99dcd8e044ccb72d554ed3e43dff","51596f3fd4db4fedb4c1b3b6e78bf2ef","930f60c9f2624984a1a6b83659c0b213","bc62c43b9548478eb1662b652183a357","8a4f21b21680490882091525ad739e58","b615a1f388564bc68862a229293f867a","4992f9b3b72d4ad697cd64c90a265724","7ebcabed43a447fc8ac50fd15d0c3a06","da0f65bd84b344b789559e0ef0efb4cb","f09a25fdc1a94fccb14d0b97bec79b11","93b28a468ebb418e8c6e0a9579fb511a","cca973024c534ecd9004e4c51bc2302c","93f637ae6de5436ab4fd79e1687f6908","17568e347c9f400183a378cdc06049bf","1d3cf0aa92dd4f738b7da6ed6f34f678","f17d181ac16c4452914e3d67b7632a13","c2bd51eb382e405b8e79553237403453","0bed64ee13644c5d899010eddadb3111","e7f5518bcede431298c1ec46f72afe88","dcd4fb78c74641fb89f28443b6be3ee6","6d01f81e4474479da774fc0a60d738ec","1689f6cb432848f48bb229c3ed3f298b","75b680c9d0ee4bcbb41a2b5ce37c7e26","0777cf4ee1ab4669ac38e42b727db5bd","3a2b2676ace64ea38b5d1745f41ad21e","022111d6aa9e4a1b8c1139322560cb6a","5ecca62ec9e44fb783648a4d7f1ad600","82537f00f68a44418aba6982eec9f8ea","40990091ee5944b1bd1468dbb69f5841","42361d5e49544fb39843e643c709cc8a","6cd32ce18672456dba342912d9752a25","9892a2302d474bbc85c6e327625799ad","ca532b3a592f41339499dd94ce1b55c7","bce5a1c092db4f00a31227895358b40e","284e41c143fc492981244ceb00a8da67","4c30739b48fa4d9886688b73f36cd570","e868c4306257424ba8af7b285ef5dafd","28a937c8a50f45819bc6f69b26a4e0c4","9af3a9e38ee4412e90f6352c8ae2aef6","554e490f6a924c138c8dd223336b80bf","e8237b31685e4f34857d5c34679f5dbd","450b3d1ccce842bebe48a7165041952c","af8f7ba6fe8e46bca0bd04ed4cb6902f","fd83d6fba4d0474cbc8c0c27568cd419","250a1e894add47d49cd877bdc72230b0","c0423f0169aa46c5988df1ce15021114","e9de60807be3407ca01b9d7b3f395030","61e70cee5df74894bb878f93b6ddce14","31e02224932f417abcb7d7f518717490","a08964a864324e1fad862deaf892c66f","698d203750034396878f6ae5184bd0e5","0cec6f182c3d40bb9d7262eb66d1604d","429cec1d424b40a48f5396f1d8423b80","024cc49572f94987beec88ef30eacefd","32ef902a91f04c8c9cc3ed8d1f4b1bfe","6a9c7fcf426843bb8b4c9660d1463aed","e4bff91c6019413aa62611cd6e7b194a","22641d7cddf24a22b14369ab7b4b3ecc","f04a6bf05bf943eebcab58ef1222127b","0e71fec1cbb44e85a619f8aa44ef4178","5c87cd80c88c4406bc920b3d200e1099","6f0c9939fd094cc3926b97691980df47","a086b60625a147eca9bd68e9cfb1045c","03c5f278bb7d4f26b56eb958671a3b60","4cea578b30b246eba7dabb14a9a31695","c032efb7f3454850b45b6c647671508a","99f683ab3ace4a6e92f7819fbadcb24e","d9344d1b64c845a88e3bfd15ba6ea4d2","b7f0f5dc488549399d3080809fb21136","42b057992ac340c7b447488664e14b33","df75e19fe6b5402d99a2a670939805de","eedc1cfc3d9246d1abeee07c82d8e16d","68f252df4c684e08a6be6240341335ea","a8b8d88f5bc64a0795cd2b9d0be10bd3","6f26ed180e8b456a8b9ce03658560999","5fecf1eda8af4ef08b603d3cb954d638","f0ac9fafc410417da16934495c09f0b4","d17d6dd7055e422d912b34a02456cb1f","0f76182ef76d4c1cb68a36d2d79ad8a8","a00e9a962b0a45c4ba98a9c8ad0f677e","c1aecc1682df4f398ab8dc2f5ed5843c","2a9916aabb71417b896d8288c7a4437f","2ee7cf2d3938466d8e845e0cc3a9034a","4609555d1e654aaf94fab17a84d7c88d","522e8f48c0994241bab6fab0d2a500dd","553c520a62a34af4ac52a513685682bb","5a3c706e808c43cdbcdd030710477b0f","6ca48fa501a94f0bab1c0d97a634d350","ef6dd2aa763d40d0a6a574b645376bbd","0463720d0ea845efb9fa26562a404cd7","d52d23b2763946a2844fc72db3be9e1a","7a1c130d1aa24ffe8f796dbe5d3fef33","88fd62dfed804b1c93db22eec292134e","64174cb09af045b2a8d8dfba9e86789c","c023525c6040499e9accdd9ba0603363","65576be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1_key_clipper_chip_encryption   \n","3        2    532             2_whatta_ites_ekr_camels   \n","4        3    472           3_israel_israeli_jews_arab   \n","..     ...    ...                                  ...   \n","213    212     11     212_memory_shared_pixmaps_server   \n","214    213     11   213_polio_patients_postpolio_motor   \n","215    214     11           214_placebo_jb_rr_medicine   \n","216    215     10                  215_24_bit_color_xv   \n","217    216     10  216_playback_movie_frames_quicktime   \n","\n","                                        Representation  \\\n","0       [to, of, and, the, is, for, you, in, it, that]   \n","1    [game, team, games, he, players, season, hocke...   \n","2    [key, clipper, chip, encryption, keys, escrow,...   \n","3    [whatta, ites, ekr, camels, hello, cheek, hi, ...   \n","4    [israel, israeli, jews, arab, jewish, arabs, p...   \n","..                                                 ...   \n","213  [memory, shared, 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all who have filled me i...  \n","\n","[218 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-e3613236-cc4c-49d8-9971-7d6b3e0ff719\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Topic</th>\n","      <th>Count</th>\n","      <th>Name</th>\n","      <th>Representation</th>\n","      <th>Representative_Docs</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-1</td>\n","      <td>6553</td>\n","      <td>-1_to_of_and_the</td>\n","      <td>[to, of, and, the, is, for, you, in, it, that]</td>\n","      <td>[ETHER IMPLODES 2 EARTH CORE, 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0.33036605\n"]}]},{"cell_type":"code","source":["topic_model.get_topic(5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"r84LQdJ3T0vb","executionInfo":{"status":"ok","timestamp":1702482919372,"user_tz":-60,"elapsed":386,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"b016e22c-3824-443f-a93d-2fc348db9619"},"execution_count":77,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('car', 0.023927501325816832),\n"," ('engine', 0.012505844383255936),\n"," ('cars', 0.012444564161363596),\n"," ('ford', 0.0095753752716333),\n"," ('mustang', 0.008393712695853406),\n"," ('miles', 0.007642561682935501),\n"," ('toyota', 0.007573620946949412),\n"," ('v8', 0.006935763067283092),\n"," ('v6', 0.006138263963066137),\n"," ('power', 0.005655961240192886)]"]},"metadata":{},"execution_count":77}]},{"cell_type":"code","source":["# topic_model.save(\"name_of_the_model\")\n","# topic_model = BERTopic.load(\"name_of_the_model\")"],"metadata":{"id":"NosjlIoO4_pJ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## custom model"],"metadata":{"id":"Qq2NcsQRUTu2"}},{"cell_type":"code","source":["from sentence_transformers import SentenceTransformer"],"metadata":{"id":"R_X9OC_UUVCl","executionInfo":{"status":"ok","timestamp":1702483041073,"user_tz":-60,"elapsed":221,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":80,"outputs":[]},{"cell_type":"code","source":["sentence_model = SentenceTransformer('distilbert-base-nli-mean-tokens', device=\"cuda\")\n","topic_model = BERTopic(embedding_model=sentence_model, verbose=True)\n","\n","topics, probs = topic_model.fit_transform(docs)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":592,"referenced_widgets":["f05979196a6540bf87fc3e0f8bd325ef","2242b64f5c3040e7917d2c6191e13226","0b92b4fa8bbf482f917b70ec59d43ed7","91d1b1cd232045f998df4d406314c1d4","ca16ad449d064526adc628565980d661","889621d67fd5425ba87de7b3d9b13578","a9a66f995ea5480d94fc2187078e9456","c03e8fbf3649444b97a7b1f435ab0c3d","886c06b07e1b44769ea67e0742e2f723","14dfe261aec6478d816d8cd4935b8980","e6907e6981764955b82e81d8acafe708","27eb07164e0f46a5b9bc1ad537cc4e3d","21915047e70642d08ed0c386c73d0b5c","341da218a6244f39aca7a10e3f0f4f5f","38d50542829b4b1597c481c74fae22c5","65fb0b35084443ababff20903b3f41d0","925fd279cfb64cd78b6899fc319afcb1","d859ff64c0194efb89502d684af9a4e0","f1dd38f8e30c48008cf98e51234eccaf","ccb45768d31d452882ddd8de8d8e7903","06cebb730a654626a61d8889b9e861d9","5fae498d0de242bea637487ac8158e5c","0fdc18f5b3b1418da0a726b461f4f49d","0ecb9876aafb4ba6aea0c8c92217f37f","0aa77416482d4e28b7d1522603cb3fad","8915154d687f4e94ad8a9827bd5023c0","4707582e8a2443a18e74eb342434027d","f98851dafaa3479f9e9323d5b1a98055","a7753364c0ff4d598823628bb534e15b","49208e12aeb94b3e83e4d9e35c5ddc59","48380a6002a7414d86f0ce703c7fd6a1","917de08c6351467e8d807560f419f4f6","d95467e373e44a9b827dbf41f5ffe226","5a8387e3a9bc462b8dffc85388c05944","c417d27c41cf4562a8263ceefad4f86e","7c3f33e17d3f4891938dfb49712cd483","12c448f076d94990b714199f15d5032e","033c22cf2781415580b82e5138ed5f97","a1cf6fb4d92741d099fac2c082a6b1cf","8ecfbd856ed747d8a836506281660f9d","3a02f6c34a9b4fdbb3ca4e3f90bacebc","f2cfa338115a4cee9a877182cd8b6a6f","fc85e795f1cc401c8e16af21ceac2980","5ee2644bae2047bdb42a3bad4c1bfdd3","13ed6920d0f64790842605b80652b11d","be910b58174e4add8cef3920c747c795","7db10e8612f64ce3a60c7f9de1d7b8c4","a6e5c307872943f39916d051509f6d0b","7f4fd23d9eb249fe88008d6595bfafcc","49d757e8132a4792bd25b262b0268365","9e19dd31036f42299bb4271d37fe5419","2629fb253e7447e889c52f99f566d107","1e2942427911461ca728c26250ee5cb1","a6b8428389af424ea8d4ddbbbe67657b","12d1ada86f974a2b8fe568f02bd949da","4a2399b685f74392b0d0da3d1eb6899e","6f2dec4439a54b098c5159caed06cbf4","a83de958b85e4bacba57e5bc37eab27e","177d46121413411f87604bfac2f81924","f93160fcea4f4df5bdde153b23bb6126","6b866bc0fdde44288d760d8097960097","75c246bcc8dd426996d9f6601383ec6e","0a4ed14e1326466087e11a1db2620da2","75db5968a3cd49e99db0f5de4aa51384","15f4b1326f5d41b0899d7b9b0ab084fe","6fead5876f984e78929ff7186cb6dd03","82108f0008134751a796c7891c9ee76c","28277bd8ad404e9c818caff63b99fc3e","97b3bfedfbb54542a12a72420a2f9249","2da5143b862e4c498bbcd9db0f13c762","0fb4cd1a2fb24e928c12cc5c275b03dc","4a5221c9dcba43bcb64b6b34e014821a","39e9a618ce854f8189cc946b6c7f9739","0ca6e1f732724db7bb0eba66f191ada7","d4cd509e1aca4e6b82b98961c40a0794","82777db73a924c5a9eeb7c27485336a2","abf5744c48e442ebb0b607c92e2341ea","90e2c92505914f3b8af13d81a5a9301c","8d3d3c0b7bb34fafaaebab14dccaf700","adfafb8146b5442c98546008694a1d93","baa7f934be61490897a4670aac3f3a3a","1d75a8bfff4b4f43a3a1a31bff8a397b","63a3f21bc4ec4d34b0757fa3d3a54c1d","e40e86fece8846b6b7a3281ef356a4ee","0c515b5a27554740a505d8625c836fa2","ba44974f4de3481b9340356954045a54","39f83a2fd6c443c88ad7272ed6b1dc47","981b70a10cf34df7b9a0e85258b137d3","d2e3049a100647e5bf65316cf0d90f44","f16e71e90dcc410198388bec5b18b167","31c19bc838e64d8c88a8e56941fa361d","858fd15cafaf4f828e104f30db7da5e5","a7caff5685c04b4e8154b64cf23b2037","06bbe71ea9d140f38f867b678332b5d2","f2e175065e2041ae844ccd69361a46b2","ee1479ffdc5d4887b5e70636b967a31a","c3cab9fb833e46b9bacafbd2ca5d3782","5c22a4e1b0c4430995e50df12a70e439","da5aabd3795249228ba4ea4893beac0c","ba25a1d2cd524aad8c3e4a275fce22d2","34749319f2d849fdb36c5097b63cfa7e","e4ebd640512c4924aa58d8eef9d56979","213a52f1db294adbae08ee439e516d39","9d1b5450554649a49d010f71c6f42567","3b18bea2cf684f1bb4f20b2ea1cab408","b115b8ec3fad4d4cb55560d403411bae","c8e0546ab97e4fd1b12e7db241e628ad","eaec9c9a2dd14e1c8979dfedae486168","50b31903965340ac853e31ce487e5441","6b0de3fef9ed4a8eb4c55e5aa939c6b9","7e56667f8d8a4d1c9e06d70740c8c149","c091cdd60cfb4a64b136b6c0d5db2c1d","4185a2c0abbe4f998378a2c12dff6e74","01c02f2dbdac465da2cd66b8fb637652","0e665155ae1e4083a4c2cd521e4c6262","15cb70d7ac4149a38821172355eb6138","49acb09cfb80453589a806b819a01448","1afe0ae361814747b156e0d415af99fb","3124cd0a53594a9f8c105630ece0e6f9","1546d6cdfa394cd6ae62ce0b6d660827","e0f13918f6b24642b03bd7db371a3cf8","16eda3c1804945f4b845c5d691bcc1e6","9495143d41f342a9af1bab772bd4dbd9","08703b11e38f42f7bb34299f09331bad","2945a588fd3049119c44daa49c7c33b0","a6e5e36897944afa9d16f39b813330c4","c706849b48c84b7c8998d35883561186","090fa66b86934c28829b87beaafb5e48","754718323edf4a22b83946db18c2c193","dfa89781faa741cea56506c601582cc4","0b1fbaf9a2a7474d81e3fa969b853453","0ae1118c96aa4bbc88749861ed4ac3a1","77bf6dd78f494fcda9f1360e2edaf51f","a6e76a5d69044ccf9064f728ad44d79f","5a8bbd0c152b48f691683b1e1b1a1ec6","a05c8da88c3f482689b08e69c5a4d47f","8a5af10202374f4794a6b1d9ee262375","5e1ad6a126194a5f9b4745fe8e9b0a07","1d0c32171fc145b08e84b55e054f9c26","c943833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Fitting the dimensionality reduction algorithm\n","2023-12-13 16:01:13,102 - BERTopic - Dimensionality - Completed ✓\n","2023-12-13 16:01:13,104 - BERTopic - Cluster - Start clustering the reduced embeddings\n","2023-12-13 16:01:16,014 - BERTopic - Cluster - Completed ✓\n","2023-12-13 16:01:16,023 - BERTopic - Representation - Extracting topics from clusters using representation models.\n","2023-12-13 16:01:22,955 - BERTopic - Representation - Completed ✓\n"]}]},{"cell_type":"code","source":["topic_model.visualize_barchart(top_n_topics=5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":537},"id":"4xAs2ZwvUo_D","executionInfo":{"status":"ok","timestamp":1702483298420,"user_tz":-60,"elapsed":283,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"f10e1df3-46b3-4023-e213-2982f2d82563"},"execution_count":82,"outputs":[{"output_type":"display_data","data":{"text/html":["<html>\n","<head><meta charset=\"utf-8\" /></head>\n","<body>\n","    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Kasela","userId":"12416586000550437186"}},"outputId":"68a64e64-0c15-4f9e-fb8b-334af4092361"},"execution_count":86,"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6ec01655b8324582bc3097447fb89849"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dc66a4d160d54a6eaf827c5b26728030"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"45dc54edfb54401fa92bff0dc91b21db"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"82dadff5cf71450eb04e2a17bf420b50"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0d1e163d340c49e88a35d8442a5dfe76"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias']\n","- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n","All the weights of TFBertModel were initialized from the PyTorch model.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"]}]},{"cell_type":"code","source":["config"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pk1l7S6M-Urk","executionInfo":{"status":"ok","timestamp":1702484333705,"user_tz":-60,"elapsed":4,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"db540b30-0f0d-4488-8b46-d482c96be0cd"},"execution_count":87,"outputs":[{"output_type":"execute_result","data":{"text/plain":["BertConfig {\n","  \"_name_or_path\": \"bert-base-uncased\",\n","  \"architectures\": [\n","    \"BertForMaskedLM\"\n","  ],\n","  \"attention_probs_dropout_prob\": 0.1,\n","  \"classifier_dropout\": null,\n","  \"gradient_checkpointing\": false,\n","  \"hidden_act\": \"gelu\",\n","  \"hidden_dropout_prob\": 0.1,\n","  \"hidden_size\": 768,\n","  \"initializer_range\": 0.02,\n","  \"intermediate_size\": 3072,\n","  \"layer_norm_eps\": 1e-12,\n","  \"max_position_embeddings\": 512,\n","  \"model_type\": \"bert\",\n","  \"num_attention_heads\": 12,\n","  \"num_hidden_layers\": 12,\n","  \"pad_token_id\": 0,\n","  \"position_embedding_type\": \"absolute\",\n","  \"transformers_version\": \"4.35.2\",\n","  \"type_vocab_size\": 2,\n","  \"use_cache\": true,\n","  \"vocab_size\": 30522\n","}"]},"metadata":{},"execution_count":87}]},{"cell_type":"code","source":["sent= 'Just an example, random word: pytorch vs tensorflow'\n","tokens = bert_tokenizer.tokenize(sent)\n","print(tokens)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3dRzpyCrdAIZ","executionInfo":{"status":"ok","timestamp":1702484414933,"user_tz":-60,"elapsed":183,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"87559611-f01a-4e11-ac92-21e81de53097"},"execution_count":88,"outputs":[{"output_type":"stream","name":"stdout","text":["['just', 'an', 'example', ',', 'random', 'word', ':', 'p', '##yt', '##or', '##ch', 'vs', 'tensor', '##flow']\n"]}]},{"cell_type":"code","source":["ids_encode = bert_tokenizer.encode_plus(\n","    sent,\n","    add_special_tokens = True,\n","    max_length = 30,\n","    padding = 'max_length', # for batch training\n","    return_attention_mask = True,\n","    truncation=True\n",")\n","print(\"\\n\\nencode:\",ids_encode)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SqvWd8scdLqd","executionInfo":{"status":"ok","timestamp":1702484548659,"user_tz":-60,"elapsed":206,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"106c00f6-c4d4-48e7-f04b-69e63180a442"},"execution_count":89,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","\n","encode: {'input_ids': [101, 2074, 2019, 2742, 1010, 6721, 2773, 1024, 1052, 22123, 2953, 2818, 5443, 23435, 12314, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}\n"]}]},{"cell_type":"code","source":["# to go back\n","print(\"\\ndecode:\",bert_tokenizer.decode(ids_encode['input_ids']))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"koJ6fIdodSEG","executionInfo":{"status":"ok","timestamp":1702484582621,"user_tz":-60,"elapsed":203,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"de5faf26-8249-4578-f909-85f13dd04d72"},"execution_count":90,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","decode: [CLS] just an example, random word : pytorch vs tensorflow [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]\n"]}]},{"cell_type":"code","source":["bert_tokenizer.convert_ids_to_tokens(101) # special token"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"lZpfrl-OfJsU","executionInfo":{"status":"ok","timestamp":1702484617409,"user_tz":-60,"elapsed":231,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"fb1a29b4-c112-430b-bdd9-d45ac93bfcd1"},"execution_count":91,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'[CLS]'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":91}]},{"cell_type":"code","source":["%%capture\n","!wget https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n","!tar xvzf aclImdb_v1.tar.gz"],"metadata":{"id":"EpxwFs6ukdY3","executionInfo":{"status":"ok","timestamp":1702484706570,"user_tz":-60,"elapsed":23485,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":92,"outputs":[]},{"cell_type":"code","source":["import os\n","\n","def fetch_reviews(path):\n","  data = []\n","  #path = 'aclImdb/train/pos/'\n","  files = [f for f in os.listdir(path)]\n","  for file in files:\n","    with open(path+file, \"r\", encoding='utf8') as f:\n","      data.append(f.read())\n","\n","  return data"],"metadata":{"id":"HLEFInRBk0FZ","executionInfo":{"status":"ok","timestamp":1702484721093,"user_tz":-60,"elapsed":278,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":93,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","\n","df_train_pos = pd.DataFrame({'review': fetch_reviews('aclImdb/train/pos/'), 'label': 1})\n","df_train_neg = pd.DataFrame({'review': fetch_reviews('aclImdb/train/neg/'), 'label': 0})\n","\n","df_test_pos = pd.DataFrame({'review': fetch_reviews('aclImdb/test/pos/'), 'label': 1})\n","df_test_neg = pd.DataFrame({'review': fetch_reviews('aclImdb/test/neg/'), 'label': 0})\n","\n","# Merging all df's for data cleaning and preprocessing step.\n","df_train = pd.concat([df_train_pos, df_train_neg], ignore_index=True)\n","print(\"Total reviews in df: \", df_train.shape)\n","\n","df_test = pd.concat([df_test_pos, df_test_neg], ignore_index=True)\n","df_train.head()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":224},"id":"kUdwGM4keocR","executionInfo":{"status":"ok","timestamp":1702484734934,"user_tz":-60,"elapsed":1308,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"0cdb3447-85ee-4e61-e55c-76168e704a5a"},"execution_count":94,"outputs":[{"output_type":"stream","name":"stdout","text":["Total reviews in df:  (25000, 2)\n"]},{"output_type":"execute_result","data":{"text/plain":["                                              review  label\n","0  Timeless musical gem, with Gene Kelly in top f...      1\n","1  Welcome to Oakland, where the dead come out to...      1\n","2  Clair Denis again revisits her theme of estran...      1\n","3  I'm too old to know (or care) exactly what the...      1\n","4  I gather at least a few people watched it on S...      1"],"text/html":["\n","  <div id=\"df-9be2fa02-d5d4-4f78-aa3a-46917d2a5287\" 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<td>I'm too old to know (or care) exactly what the...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>I gather at least a few people watched it on S...</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9be2fa02-d5d4-4f78-aa3a-46917d2a5287')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","    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padding='max_length',\n","        return_attention_mask=True,\n","        truncation=True\n","    )\n","    input_ids.append(bert_inp['input_ids'])\n","    attention_masks.append(bert_inp['attention_mask'])\n","\n","input_ids = np.asarray(input_ids)\n","attention_masks = np.array(attention_masks)\n","target = np.array(pd.get_dummies(df_train['label']))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tSomIjIwe1Sa","executionInfo":{"status":"ok","timestamp":1702485245058,"user_tz":-60,"elapsed":203049,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"61b83fc7-f20d-473e-84c4-0507c9a3b1f9"},"execution_count":98,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 25000/25000 [03:22<00:00, 123.35it/s]\n"]}]},{"cell_type":"code","source":["test_input_ids = []\n","test_attention_masks = []\n","\n","for sent in tqdm(df_test['review']):\n","    bert_inp = bert_tokenizer.encode_plus(\n","        sent,\n","        add_special_tokens=True,\n","        max_length=128,\n","        padding='max_length',\n","        return_attention_mask=True,\n","        truncation=True\n","    )\n","    test_input_ids.append(bert_inp['input_ids'])\n","    test_attention_masks.append(bert_inp['attention_mask'])\n","\n","test_input_ids = np.asarray(test_input_ids)\n","test_attention_masks = np.array(test_attention_masks)\n","test_target = np.array(pd.get_dummies(df_test['label']))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZQYdajssBIVt","executionInfo":{"status":"ok","timestamp":1702485630249,"user_tz":-60,"elapsed":377801,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"31b6afc3-e007-4297-cd03-2ec996393d3e"},"execution_count":100,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 25000/25000 [06:16<00:00, 66.38it/s] \n"]}]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","\n","X_train, X_val, y_train, y_val, train_mask, val_mask = train_test_split(input_ids, target, attention_masks, test_size=.2)"],"metadata":{"id":"CgWEUz4ufGGG","executionInfo":{"status":"ok","timestamp":1702485634737,"user_tz":-60,"elapsed":191,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":102,"outputs":[]},{"cell_type":"code","source":["bert_model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3ECcNVLZCsVf","executionInfo":{"status":"ok","timestamp":1702485630999,"user_tz":-60,"elapsed":284,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"2a241fc8-af6f-45d0-c66b-2e8015438489"},"execution_count":101,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"tf_bert_model\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," bert (TFBertMainLayer)      multiple                  109482240 \n","                                                                 \n","=================================================================\n","Total params: 109482240 (417.64 MB)\n","Trainable params: 109482240 (417.64 MB)\n","Non-trainable params: 0 (0.00 Byte)\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["input_ids = tf.keras.Input(shape=(128,), dtype='int32')\n","attention_masks = tf.keras.Input(shape=(128,), dtype='int32')\n","\n","output = bert_model(input_ids, attention_masks)\n","output = output[0] # top attention head\n","output = output[:,0,:] # CLS token embedding\n","\n","output = tf.keras.layers.Dense(32, activation='relu')(output)\n","output = tf.keras.layers.Dropout(0.2)(output)\n","output = tf.keras.layers.Dense(2, activation='softmax')(output)\n","\n","model = tf.keras.models.Model(inputs = [input_ids, attention_masks], outputs=output)"],"metadata":{"id":"b0kXHyKYCM8G","executionInfo":{"status":"ok","timestamp":1702485794713,"user_tz":-60,"elapsed":2005,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":105,"outputs":[]},{"cell_type":"code","source":["model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Kg6x9RcPfRjg","executionInfo":{"status":"ok","timestamp":1702485797785,"user_tz":-60,"elapsed":191,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"9a6d2348-2ce7-4855-81cf-fba1a177b107"},"execution_count":106,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                Output Shape                 Param #   Connected to                  \n","==================================================================================================\n"," input_5 (InputLayer)        [(None, 128)]                0         []                            \n","                                                                                                  \n"," input_6 (InputLayer)        [(None, 128)]                0         []                            \n","                                                                                                  \n"," tf_bert_model (TFBertModel  TFBaseModelOutputWithPooli   1094822   ['input_5[0][0]',             \n"," )                           ngAndCrossAttentions(last_   40         'input_6[0][0]']             \n","                             hidden_state=(None, 128, 7                                           \n","                             68),                                                                 \n","                              pooler_output=(None, 768)                                           \n","                             , past_key_values=None, hi                                           \n","                             dden_states=None, attentio                                           \n","                             ns=None, cross_attentions=                                           \n","                             None)                                                                \n","                                                                                                  \n"," tf.__operators__.getitem_2  (None, 768)                  0         ['tf_bert_model[2][0]']       \n","  (SlicingOpLambda)                                                                               \n","                                                                                                  \n"," dense_3 (Dense)             (None, 32)                   24608     ['tf.__operators__.getitem_2[0\n","                                                                    ][0]']                        \n","                                                                                                  \n"," dropout_39 (Dropout)        (None, 32)                   0         ['dense_3[0][0]']             \n","                                                                                                  \n"," dense_4 (Dense)             (None, 2)                    66        ['dropout_39[0][0]']          \n","                                                                                                  \n","==================================================================================================\n","Total params: 109506914 (417.74 MB)\n","Trainable params: 109506914 (417.74 MB)\n","Non-trainable params: 0 (0.00 Byte)\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"code","source":["for layer in model.layers[:3]:\n","    layer.trainable = False"],"metadata":{"id":"16VPtAv-fTNX","executionInfo":{"status":"ok","timestamp":1702485909944,"user_tz":-60,"elapsed":182,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}}},"execution_count":107,"outputs":[]},{"cell_type":"code","source":["model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qgkpKvliffbM","executionInfo":{"status":"ok","timestamp":1702485911828,"user_tz":-60,"elapsed":436,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"be746152-727a-4491-b1a9-1848a4c9fdf3"},"execution_count":108,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                Output Shape                 Param #   Connected to                  \n","==================================================================================================\n"," input_5 (InputLayer)        [(None, 128)]                0         []                            \n","                                                                                                  \n"," input_6 (InputLayer)        [(None, 128)]                0         []                            \n","                                                                                                  \n"," tf_bert_model (TFBertModel  TFBaseModelOutputWithPooli   1094822   ['input_5[0][0]',             \n"," )                           ngAndCrossAttentions(last_   40         'input_6[0][0]']             \n","                             hidden_state=(None, 128, 7                                           \n","                             68),                                                                 \n","                              pooler_output=(None, 768)                                           \n","                             , past_key_values=None, hi                                           \n","                             dden_states=None, attentio                                           \n","                             ns=None, cross_attentions=                                           \n","                             None)                                                                \n","                                                                                                  \n"," tf.__operators__.getitem_2  (None, 768)                  0         ['tf_bert_model[2][0]']       \n","  (SlicingOpLambda)                                                                               \n","                                                                                                  \n"," dense_3 (Dense)             (None, 32)                   24608     ['tf.__operators__.getitem_2[0\n","                                                                    ][0]']                        \n","                                                                                                  \n"," dropout_39 (Dropout)        (None, 32)                   0         ['dense_3[0][0]']             \n","                                                                                                  \n"," dense_4 (Dense)             (None, 2)                    66        ['dropout_39[0][0]']          \n","                                                                                                  \n","==================================================================================================\n","Total params: 109506914 (417.74 MB)\n","Trainable params: 24674 (96.38 KB)\n","Non-trainable params: 109482240 (417.64 MB)\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"code","source":["model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n","\n","history = model.fit(\n","    [X_train, train_mask],\n","    y_train,\n","    batch_size=32,\n","    epochs=1,\n","    validation_data=([X_val, val_mask], y_val)\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"720MU8Pgfh_g","executionInfo":{"status":"ok","timestamp":1702486354603,"user_tz":-60,"elapsed":330533,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"80f07e9d-364b-4f9c-c228-b6efb1fb795d"},"execution_count":109,"outputs":[{"output_type":"stream","name":"stdout","text":["625/625 [==============================] - 278s 429ms/step - loss: 0.5132 - accuracy: 0.7452 - val_loss: 0.4432 - val_accuracy: 0.7870\n"]}]},{"cell_type":"code","source":["y_pred = model.predict([test_input_ids, test_attention_masks])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A0JW-xwhfsxg","executionInfo":{"status":"ok","timestamp":1702486578602,"user_tz":-60,"elapsed":223172,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"2c94be3b-d762-46b1-9e53-4d645c9dfa5c"},"execution_count":110,"outputs":[{"output_type":"stream","name":"stdout","text":["782/782 [==============================] - 223s 280ms/step\n"]}]},{"cell_type":"code","source":["y_pred"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1skiaOtZGJsD","executionInfo":{"status":"ok","timestamp":1702486578602,"user_tz":-60,"elapsed":17,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"e87111b1-2a37-4534-934d-71fedd8ba069"},"execution_count":111,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.49444705, 0.50555295],\n","       [0.23364355, 0.7663564 ],\n","       [0.04869918, 0.95130086],\n","       ...,\n","       [0.8282779 , 0.17172208],\n","       [0.77879643, 0.22120358],\n","       [0.87881196, 0.12118808]], dtype=float32)"]},"metadata":{},"execution_count":111}]},{"cell_type":"code","source":["test_target"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n0k5BySzG-1T","executionInfo":{"status":"ok","timestamp":1702486627793,"user_tz":-60,"elapsed":191,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"c386cb05-4eab-480f-8ac1-3cf4b5792a68"},"execution_count":115,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0, 1],\n","       [0, 1],\n","       [0, 1],\n","       ...,\n","       [1, 0],\n","       [1, 0],\n","       [1, 0]], dtype=uint8)"]},"metadata":{},"execution_count":115}]},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","\n","print(classification_report(test_target.argmax(1), y_pred.argmax(1)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZGFl3-G5njb1","executionInfo":{"status":"ok","timestamp":1702486578602,"user_tz":-60,"elapsed":5,"user":{"displayName":"Pranav Kasela","userId":"12416586000550437186"}},"outputId":"34b5961e-e3bb-4e4b-91d0-cde2d233b0a3"},"execution_count":112,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.84      0.71      0.77     12500\n","           1       0.75      0.86      0.80     12500\n","\n","    accuracy                           0.79     25000\n","   macro avg       0.79      0.79      0.78     25000\n","weighted avg       0.79      0.79      0.78     25000\n","\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"LPUlvmoCzguU"},"execution_count":null,"outputs":[]}]}