Neural_QA,_Summarization_and_Dependency_Parsing.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Assignment 3: Neural Question Answering, Neural Submarization and Dependency Parsing"
      ],
      "metadata": {
        "id": "DPnGrR237WkR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q1: Neural Question Answering"
      ],
      "metadata": {
        "id": "t0o_9x64Z8bz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "7bgJjfc7KWsm"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "name1 = \"deepset/bert-base-cased-squad2\"\n",
        "name2 = \"deepset/roberta-base-squad2\"\n",
        "name3 = \"deepset/roberta-base-squad2-distilled\""
      ],
      "metadata": {
        "id": "n0vJNi18Kd9J"
      },
      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "model_bert = pipeline(\"question-answering\", model=name1)\n",
        "model_roberta = pipeline(\"question-answering\", model=name2)\n",
        "model_distilled = pipeline(\"question-answering\", model=name3)"
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        },
        "id": "NjOKS96kKiBa",
        "outputId": "bf534587-d5b5-4832-f426-f924ee5f3d5d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/508 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2dd923af2dd84097a279cb5ca629573f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/433M [00:00<?, ?B/s]"
            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at deepset/bert-base-cased-squad2 were not used when initializing BertForQuestionAnswering: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
            "- This IS expected if you are initializing BertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
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          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2387049a7d6b46d79bc44a7a9d91b3ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd9d6c9d00be4aa384ffe8fd75b4a66f"
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model_bert(question=\"What color is the sky?\", context=\"The sky is blue.\"))\n",
        "\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(model_roberta(question=\"What color is the sky?\", context=\"The sky is blue.\"))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(model_distilled(question=\"What color is the sky?\", context=\"The sky is blue.\"))\n",
        "print(\"________________________________________________________________________________\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqj_dy3YKk4f",
        "outputId": "e3a8902f-d3c3-4933-b0d7-6a48c2fdfab1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'score': 0.9759241342544556, 'start': 11, 'end': 15, 'answer': 'blue'}\n",
            "________________________________________________________________________________\n",
            "{'score': 0.9661651849746704, 'start': 11, 'end': 15, 'answer': 'blue'}\n",
            "________________________________________________________________________________\n",
            "{'score': 0.3147728741168976, 'start': 11, 'end': 15, 'answer': 'blue'}\n",
            "________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q1.1"
      ],
      "metadata": {
        "id": "6JrBRwQGPyZt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "question1 = \"What two railroads have railyards in the city of Fresno?\"\n",
        "context1 = \"\"\"Passenger rail service is provided by Amtrak San Joaquins. The main passenger rail station is the recently renovated historic Santa Fe Railroad Depot in Downtown Fresno. The Bakersfield-Stockton mainlines of the Burlington Northern Santa Fe Railway and Union Pacific Railroad railroads cross in Fresno, and both railroads maintain railyards within the city; the San Joaquin Valley Railroad also operates former Southern Pacific branchlines heading west and south out of the city. The city of Fresno is planned to serve the future California High Speed Rail.\"\"\"\n",
        "print(\"BERT\")\n",
        "print(model_bert(question=question1, context=context1))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"RoBERTa\")\n",
        "print(model_roberta(question=question1, context=context1))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"RoBERTa Distilled\")\n",
        "print(model_distilled(question=question1, context=context1))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "V1 = model_bert(question=question1, context=context1)\n",
        "V2 = model_roberta(question=question1, context=context1)\n",
        "V3 = model_distilled(question=question1, context=context1)\n",
        "print(round(V1['score'],4),V1['answer'])\n",
        "print(round(V2['score'],4),V2['answer'])\n",
        "print(round(V3['score'],4),V3['answer'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "isxf8NwUNvx-",
        "outputId": "bdb4a34b-5ac2-4618-f3b1-6a461ee5b30d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT\n",
            "{'score': 0.7487963438034058, 'start': 212, 'end': 275, 'answer': 'Burlington Northern Santa Fe Railway and Union Pacific Railroad'}\n",
            "________________________________________________________________________________\n",
            "RoBERTa\n",
            "{'score': 0.23243877291679382, 'start': 212, 'end': 275, 'answer': 'Burlington Northern Santa Fe Railway and Union Pacific Railroad'}\n",
            "________________________________________________________________________________\n",
            "RoBERTa Distilled\n",
            "{'score': 0.45007315278053284, 'start': 212, 'end': 275, 'answer': 'Burlington Northern Santa Fe Railway and Union Pacific Railroad'}\n",
            "________________________________________________________________________________\n",
            "0.7488 Burlington Northern Santa Fe Railway and Union Pacific Railroad\n",
            "0.2324 Burlington Northern Santa Fe Railway and Union Pacific Railroad\n",
            "0.4501 Burlington Northern Santa Fe Railway and Union Pacific Railroad\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q1.2"
      ],
      "metadata": {
        "id": "wBnizSQpWegU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "question2 = \"What would create a conflict between a problem X and problem C within the context of reduction?\"\n",
        "\n",
        "context2 = \"\"\"This motivates the concept of a problem being hard for a complexity class. A problem X is hard for a class of problems C if every problem in C can be reduced to X. Thus no problem in C is harder than X, since an algorithm for X allows us to solve any problem in C. Of course, the notion of hard problems depends on the type of reduction being used. For complexity classes larger than P, polynomial-time reductions are commonly used. In particular, the set of problems that are hard for NP is the set of NP-hard problems.\"\"\"\n",
        "\n",
        "print(\"BERT\")\n",
        "print(model_bert(question=question2, context=context2))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"RoBERTa\")\n",
        "print(model_roberta(question=question2, context=context2))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"RoBERTa Distilled\")\n",
        "print(model_distilled(question=question2, context=context2))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "V1 = model_bert(question=question2, context=context2)\n",
        "V2 = model_roberta(question=question2, context=context2)\n",
        "V3 = model_distilled(question=question2, context=context2)\n",
        "print(round(V1['score'],4),V1['answer'])\n",
        "print(round(V2['score'],4),V2['answer'])\n",
        "print(round(V3['score'],4),V3['answer'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9gXm1XPIPrAG",
        "outputId": "4fcf2fa9-e69b-46d9-8f54-9a42f9719072"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT\n",
            "{'score': 0.043349333107471466, 'start': 169, 'end': 201, 'answer': 'no problem in C is harder than X'}\n",
            "________________________________________________________________________________\n",
            "RoBERTa\n",
            "{'score': 0.03393303230404854, 'start': 124, 'end': 162, 'answer': 'every problem in C can be reduced to X'}\n",
            "________________________________________________________________________________\n",
            "RoBERTa Distilled\n",
            "{'score': 2.2866133804200217e-05, 'start': 124, 'end': 162, 'answer': 'every problem in C can be reduced to X'}\n",
            "________________________________________________________________________________\n",
            "0.0433 no problem in C is harder than X\n",
            "0.0339 every problem in C can be reduced to X\n",
            "0.0 every problem in C can be reduced to X\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q1.3"
      ],
      "metadata": {
        "id": "_Ug36nILWg0u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "question3 = \"What Catholic Church liturgical belief did Lortie criticize openly?\"\n",
        "\n",
        "context3 = \"\"\"Both before and after the 1708 passage of the Foreign Protestants Naturalization Act, an estimated 50,000 Protestant Walloons and Huguenots fled to England, with many moving on to Ireland and elsewhere. In relative terms, this was one of the largest waves of immigration ever of a single ethnic community to Britain. Andrew Lortie (born André Lortie), a leading Huguenot theologian and writer who led the exiled community in London, became known for articulating their criticism of the Pope and the doctrine of transubstantiation during Mass.\"\"\"\n",
        "\n",
        "print(\"BERT\")\n",
        "print(model_bert(question=question3, context=context3))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"RoBERTa\")\n",
        "print(model_roberta(question=question3, context=context3))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"RoBERTa Distilled\")\n",
        "print(model_distilled(question=question3, context=context3))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "V1 = model_bert(question=question3, context=context3)\n",
        "V2 = model_roberta(question=question3, context=context3)\n",
        "V3 = model_distilled(question=question3, context=context3)\n",
        "print(round(V1['score'],4),V1['answer'])\n",
        "print(round(V2['score'],4),V2['answer'])\n",
        "print(round(V3['score'],4),V3['answer'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uvm8KAc8Rw6t",
        "outputId": "f47c2ec8-1614-4c5e-a3a3-d4ffa498e6e1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT\n",
            "{'score': 0.35396483540534973, 'start': 495, 'end': 529, 'answer': 'the doctrine of transubstantiation'}\n",
            "________________________________________________________________________________\n",
            "RoBERTa\n",
            "{'score': 0.2651766836643219, 'start': 511, 'end': 529, 'answer': 'transubstantiation'}\n",
            "________________________________________________________________________________\n",
            "RoBERTa Distilled\n",
            "{'score': 0.7593153119087219, 'start': 511, 'end': 529, 'answer': 'transubstantiation'}\n",
            "________________________________________________________________________________\n",
            "0.354 the doctrine of transubstantiation\n",
            "0.2652 transubstantiation\n",
            "0.7593 transubstantiation\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q1.4"
      ],
      "metadata": {
        "id": "sx2K__g-WjAn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "question4 = \"In considering Turing machines and alternate variables, what measurement left unaffected by conversion between machine models?\"\n",
        "context4 = \"\"\"Many machine models different from the standard multi-tape Turing machines have been proposed in the literature, for example random access machines. Perhaps surprisingly, each of these models can be converted to another without providing any extra computational power. The time and memory consumption of these alternate models may vary. What all these models have in common is that the machines operate deterministically.\"\"\"\n",
        "\n",
        "print(\"BERT\")\n",
        "print(model_bert(question=question4, context=context4))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"RoBERTa\")\n",
        "print(model_roberta(question=question4, context=context4))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"RoBERTa Distilled\")\n",
        "print(model_distilled(question=question4, context=context4))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "V1 = model_bert(question=question4, context=context4)\n",
        "V2 = model_roberta(question=question4, context=context4)\n",
        "V3 = model_distilled(question=question4, context=context4)\n",
        "print(round(V1['score'],4),V1['answer'])\n",
        "print(round(V2['score'],4),V2['answer'])\n",
        "print(round(V3['score'],4),V3['answer'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YY1ttM0xTSmL",
        "outputId": "78db6863-c824-4e34-a7fa-04269143ecf1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT\n",
            "{'score': 0.23199394345283508, 'start': 403, 'end': 420, 'answer': 'deterministically'}\n",
            "________________________________________________________________________________\n",
            "RoBERTa\n",
            "{'score': 0.173636332154274, 'start': 248, 'end': 267, 'answer': 'computational power'}\n",
            "________________________________________________________________________________\n",
            "RoBERTa Distilled\n",
            "{'score': 0.30871984362602234, 'start': 273, 'end': 300, 'answer': 'time and memory consumption'}\n",
            "________________________________________________________________________________\n",
            "0.232 deterministically\n",
            "0.1736 computational power\n",
            "0.3087 time and memory consumption\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q1.5"
      ],
      "metadata": {
        "id": "98D5qk4jWlML"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "question5 = \"How many miles across the Atlantic Ocean does Saharan dust travel?\"\n",
        "context5 = \"\"\"NASA's CALIPSO satellite has measured the amount of dust transported by wind from the Sahara to the Amazon: an average 182 million tons of dust are windblown out of the Sahara each year, at 15 degrees west longitude, across 1,600 miles (2,600 km) over the Atlantic Ocean (some dust falls into the Atlantic), then at 35 degrees West longitude at the eastern coast of South America, 27.7 million tons (15%) of dust fall over the Amazon basin, 132 million tons of dust remain in the air, 43 million tons of dust are windblown and falls on the Caribbean Sea, past 75 degrees west longitude.\"\"\"\n",
        "\n",
        "print(\"BERT\")\n",
        "print(model_bert(question=question5, context=context5))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"RoBERTa\")\n",
        "print(model_roberta(question=question5, context=context5))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"RoBERTa Distilled\")\n",
        "print(model_distilled(question=question5, context=context5))\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "V1 = model_bert(question=question5, context=context5)\n",
        "V2 = model_roberta(question=question5, context=context5)\n",
        "V3 = model_distilled(question=question5, context=context5)\n",
        "print(round(V1['score'],4),V1['answer'])\n",
        "print(round(V2['score'],4),V2['answer'])\n",
        "print(round(V3['score'],4),V3['answer'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RACcxz6EULIl",
        "outputId": "57f9b22d-6bd0-458f-e41f-3f76f19037d5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT\n",
            "{'score': 0.35243096947669983, 'start': 224, 'end': 229, 'answer': '1,600'}\n",
            "________________________________________________________________________________\n",
            "RoBERTa\n",
            "{'score': 0.6860769391059875, 'start': 224, 'end': 229, 'answer': '1,600'}\n",
            "________________________________________________________________________________\n",
            "RoBERTa Distilled\n",
            "{'score': 0.5144791603088379, 'start': 224, 'end': 229, 'answer': '1,600'}\n",
            "________________________________________________________________________________\n",
            "0.3524 1,600\n",
            "0.6861 1,600\n",
            "0.5145 1,600\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q2: Neural Summarization"
      ],
      "metadata": {
        "id": "UsH_wmU2Wp2m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "google = \"google/pegasus-xsum\"\n",
        "facebook = \"facebook/bart-large-xsum\"\n",
        "cnn = \"sysresearch101/t5-large-finetuned-xsum-cnn\""
      ],
      "metadata": {
        "id": "jLWie3pAWXFn"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_google = pipeline(\"summarization\", model=google)\n",
        "model_facebook = pipeline(\"summarization\", model=facebook)\n",
        "model_cnn = pipeline(\"summarization\", model=cnn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679,
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        "id": "sAFackFxXP2e",
        "outputId": "0b38e031-2229-44e0-f64d-2175f0f9ead0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.39k [00:00<?, ?B/s]"
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        {
          "output_type": "stream",
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          "text": [
            "Some weights of PegasusForConditionalGeneration were not initialized from the model checkpoint at google/pegasus-xsum and are newly initialized: ['model.decoder.embed_positions.weight', 'model.encoder.embed_positions.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
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    },
    {
      "cell_type": "code",
      "source": [
        "#Example\n",
        "article = \"New York (CNN)When Liana Barrientos was 23 years old, ... \"\n",
        "print(model_google(article)[0]['summary_text'])\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(model_facebook(article)[0]['summary_text'])\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(model_cnn(article)[0]['summary_text'])\n",
        "print(\"________________________________________________________________________________\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C504V-ObW3VV",
        "outputId": "cb8598c4-a090-4698-de21-0f0035f4803b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Your max_length is set to 64, but your input_length is only 19. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=9)\n",
            "Your max_length is set to 62, but your input_length is only 20. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=10)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A New York City woman who says she was sexually assaulted by a man she met on a dating website has been found dead.\n",
            "________________________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Your max_length is set to 200, but your input_length is only 23. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=11)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "When Liana Barrientos was 23 years old, she became the youngest person in the United States to be diagnosed with breast cancer.\n",
            "________________________________________________________________________________\n",
            "when liana barrientos was 23 years old, she was the first person in the world to have a sex fetish.\n",
            "________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q2.1"
      ],
      "metadata": {
        "id": "mqLJOrYiZ2Kz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "article1 = \"\"\"The new westbound section of the M8 between Newhouse and Baillieston opened a\n",
        "week ahead of schedule. The eastbound section is due to open next weekend.\n",
        "Commuters on the M8 and A8 have faced major disruption for the past nine weeks\n",
        "as the finishing touches were put to the new carriageway. It means the entire\n",
        "stretch between Edinburgh and Glasgow is now motorway. The works were the latest\n",
        "stage in the Â£500m M8, M73 and M74 motorway improvements project. Transport\n",
        "Scotland said the new road would cut journey times at peak times by about 20\n",
        "minutes.\"\"\"\n",
        "\n",
        "\n",
        "print(\"Model Google\")\n",
        "print()\n",
        "print(model_google(article1)[0]['summary_text'])\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"Model Facebook\")\n",
        "print()\n",
        "print(model_facebook(article1)[0]['summary_text'])\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"Model CNN\")\n",
        "print()\n",
        "print(model_cnn(article1)[0]['summary_text'])\n",
        "print(\"________________________________________________________________________________\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x8uzn3aWYGaq",
        "outputId": "4cb82d77-83d8-45ea-e081-59fed9997ff9"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Google\n",
            "\n",
            "A new section of motorway in the south west of Scotland has opened to traffic.\n",
            "________________________________________________________________________________\n",
            "Model Facebook\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Your max_length is set to 200, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A new stretch of motorway between Edinburgh and Glasgow has opened to traffic.\n",
            "________________________________________________________________________________\n",
            "Model CNN\n",
            "\n",
            "a new section of the M8 motorway between Edinburgh and Glasgow has opened to traffic a week ahead of schedule, Transport Scotland has announced.\n",
            "________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q2.2"
      ],
      "metadata": {
        "id": "YxyKjvJBaOdZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "article2 = \"\"\"Her stepson, filmmaker Carlo Alberto Pinelli, told the Hollywood Reporter that\n",
        "she had died on 1 May in Spain. In Casablanca, Lebeau plays Humphrey\n",
        "Bogart's spurned lover. In a famous scene, she tearfully shouts \"Vive\n",
        "La France\", after the clientele in Rick's CafÃ© sing La Marseillaise\n",
        "to drown out singing by German soldiers. Born in 1923 near Paris, she fled Nazi-\n",
        "occupied France with her then husband, prominent actor Marcel Dalio, in 1940.\n",
        "The couple ended up in Hollywood, and both played in Casablanca. In the film,\n",
        "Bogart plays an American cafe owner in Morocco, a territory controlled by\n",
        "France's Nazi collaborationist regime. He must choose between his love for\n",
        "a woman and helping her husband escape Casablanca to continue fighting the\n",
        "Nazis. Lebeau played in two more US films before returning to France after the\n",
        "war. Her subsequent work includes the role of a temperamental French actress in\n",
        "Fellini's 8 1/2 (1963).\"\"\"\n",
        "\n",
        "print(\"Model Google\")\n",
        "print()\n",
        "print(model_google(article2)[0]['summary_text'])\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"Model Facebook\")\n",
        "print()\n",
        "print(model_facebook(article2)[0]['summary_text'])\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"Model CNN\")\n",
        "print()\n",
        "print(model_cnn(article2)[0]['summary_text'])\n",
        "print(\"________________________________________________________________________________\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYYypY_MZy88",
        "outputId": "6ccac919-b771-4720-ddb6-99702f370580"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Google\n",
            "\n",
            "French actress Jeanne Lebeau, best known for her role in World War Two film Casablanca, has died aged 93.\n",
            "________________________________________________________________________________\n",
            "Model Facebook\n",
            "\n",
            "French actress Anne Lebeau, best known for her role in World War Two film Casablanca, has died at the age of 93.\n",
            "________________________________________________________________________________\n",
            "Model CNN\n",
            "\n",
            "Frances Lebeau, who played Humphrey Bogart's lover in the 1940 film Casablanca, has died at the age of 91.\n",
            "________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q2.3"
      ],
      "metadata": {
        "id": "l0dxYx8-cEU7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "article3 = \"\"\"The 26-year-old has made just one appearance since joining Posh in November,\n",
        "scoring in the 3-2 win over former club Barnsley. Graham Westley's side\n",
        "currently have injuries to fellow defenders Callum Elder, Gabriel Zakuani and\n",
        "Kgosi Ntlhe. Posh, currently sixth in the table, are at home against struggling\n",
        "Chesterfield on Boxing Day.\"\"\"\n",
        "\n",
        "print(\"Model Google\")\n",
        "print()\n",
        "print(model_google(article3)[0]['summary_text'])\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"Model Facebook\")\n",
        "print()\n",
        "print(model_facebook(article3)[0]['summary_text'])\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "print(\"Model CNN\")\n",
        "print()\n",
        "print(model_cnn(article3)[0]['summary_text'])\n",
        "print(\"________________________________________________________________________________\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8P42zYKhblF7",
        "outputId": "8ce73cf1-f6d7-4e44-dd19-48285c227a1e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Google\n",
            "\n",
            "League One side Peterborough United have recalled defender George Taft from his loan spell at Crawley Town.\n",
            "________________________________________________________________________________\n",
            "Model Facebook\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Your max_length is set to 200, but your input_length is only 86. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=43)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "League One side Peterborough United have extended the loan spell of Middlesbrough defender Jordan Rhodes until the end of the season.\n",
            "________________________________________________________________________________\n",
            "Model CNN\n",
            "\n",
            "Posh have signed former england under-21 international defender adam taylor on loan until the end of the season.\n",
            "________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q4: Dependency Parsing"
      ],
      "metadata": {
        "id": "B9E5i-FUcqT1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy download en_core_web_sm\n",
        "!python -m spacy download en_core_web_trf\n",
        "import spacy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghP2REx_cZeA",
        "outputId": "5cf423ff-7ef8-46b4-e61b-66e9e64432e0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting en-core-web-sm==3.7.1\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl (12.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m82.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.8.0,>=3.7.2 in /usr/local/lib/python3.10/dist-packages (from en-core-web-sm==3.7.1) (3.7.5)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.10)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.8)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.9)\n",
            "Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.2.5)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.1.3)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.4.8)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.10)\n",
            "Requirement already satisfied: weasel<0.5.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.4.1)\n",
            "Requirement already satisfied: typer<1.0.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.12.5)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.66.6)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.32.3)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.9.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.1.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (75.1.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (24.1)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.4.1)\n",
            "Requirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.26.4)\n",
            "Requirement already satisfied: language-data>=1.2 in /usr/local/lib/python3.10/dist-packages (from langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.2.0)\n",
            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.23.4)\n",
            "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2024.8.30)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.7.11)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.5)\n",
            "Requirement already satisfied: click>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.1.7)\n",
            "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.5.4)\n",
            "Requirement already satisfied: rich>=10.11.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (13.9.3)\n",
            "Requirement already satisfied: cloudpathlib<1.0.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.20.0)\n",
            "Requirement already satisfied: smart-open<8.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (7.0.5)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.2)\n",
            "Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.2.1)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.18.0)\n",
            "Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from smart-open<8.0.0,>=5.2.1->weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.16.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.2)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_sm')\n",
            "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
            "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
            "order to load all the package's dependencies. You can do this by selecting the\n",
            "'Restart kernel' or 'Restart runtime' option.\n",
            "Collecting en-core-web-trf==3.7.3\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_trf-3.7.3/en_core_web_trf-3.7.3-py3-none-any.whl (457.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m457.4/457.4 MB\u001b[0m \u001b[31m909.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.8.0,>=3.7.2 in /usr/local/lib/python3.10/dist-packages (from en-core-web-trf==3.7.3) (3.7.5)\n",
            "Collecting spacy-curated-transformers<0.3.0,>=0.2.0 (from en-core-web-trf==3.7.3)\n",
            "  Downloading spacy_curated_transformers-0.2.2-py2.py3-none-any.whl.metadata (2.7 kB)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (1.0.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (1.0.10)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (2.0.8)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (3.0.9)\n",
            "Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (8.2.5)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (1.1.3)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (2.4.8)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (2.0.10)\n",
            "Requirement already satisfied: weasel<0.5.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (0.4.1)\n",
            "Requirement already satisfied: typer<1.0.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (0.12.5)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (4.66.6)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (2.32.3)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (2.9.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (3.1.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (75.1.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (24.1)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (3.4.1)\n",
            "Requirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (1.26.4)\n",
            "Collecting curated-transformers<0.2.0,>=0.1.0 (from spacy-curated-transformers<0.3.0,>=0.2.0->en-core-web-trf==3.7.3)\n",
            "  Downloading curated_transformers-0.1.1-py2.py3-none-any.whl.metadata (965 bytes)\n",
            "Collecting curated-tokenizers<0.1.0,>=0.0.9 (from spacy-curated-transformers<0.3.0,>=0.2.0->en-core-web-trf==3.7.3)\n",
            "  Downloading curated_tokenizers-0.0.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.9 kB)\n",
            "Requirement already satisfied: torch>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from spacy-curated-transformers<0.3.0,>=0.2.0->en-core-web-trf==3.7.3) (2.5.0+cu121)\n",
            "Requirement already satisfied: regex>=2022 in /usr/local/lib/python3.10/dist-packages (from curated-tokenizers<0.1.0,>=0.0.9->spacy-curated-transformers<0.3.0,>=0.2.0->en-core-web-trf==3.7.3) (2024.9.11)\n",
            "Requirement already satisfied: language-data>=1.2 in /usr/local/lib/python3.10/dist-packages (from langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (1.2.0)\n",
            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (2.23.4)\n",
            "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (4.12.2)\n",
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            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.12.0->spacy-curated-transformers<0.3.0,>=0.2.0->en-core-web-trf==3.7.3) (3.4.2)\n",
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            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (3.0.0)\n",
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            "Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from smart-open<8.0.0,>=5.2.1->weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (1.16.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-trf==3.7.3) (0.1.2)\n",
            "Downloading spacy_curated_transformers-0.2.2-py2.py3-none-any.whl (236 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.3/236.3 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading curated_tokenizers-0.0.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (731 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m731.6/731.6 kB\u001b[0m \u001b[31m25.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading curated_transformers-0.1.1-py2.py3-none-any.whl (25 kB)\n",
            "Installing collected packages: curated-tokenizers, curated-transformers, spacy-curated-transformers, en-core-web-trf\n",
            "Successfully installed curated-tokenizers-0.0.9 curated-transformers-0.1.1 en-core-web-trf-3.7.3 spacy-curated-transformers-0.2.2\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_trf')\n",
            "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
            "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
            "order to load all the package's dependencies. You can do this by selecting the\n",
            "'Restart kernel' or 'Restart runtime' option.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_sm = spacy.load(\"en_core_web_sm\")\n",
        "model_trf = spacy.load(\"en_core_web_trf\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FnR-E-Lfc_iE",
        "outputId": "576d2571-f3e3-4095-c957-e9c4cef29771"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/thinc/shims/pytorch.py:253: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  model.load_state_dict(torch.load(filelike, map_location=device))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc = model_sm(\"This is a sentence.\")\n",
        "for token in doc:\n",
        "  print(token.text, token.dep_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7OI_LPnkdslJ",
        "outputId": "2162398b-878c-44e8-fec7-b0d94aeb740a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This nsubj\n",
            "is ROOT\n",
            "a det\n",
            "sentence attr\n",
            ". punct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc = model_trf(\"This is a sentence.\")\n",
        "for token in doc:\n",
        "  print(token.text, token.dep_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQmBp8gXd6jH",
        "outputId": "4b84948a-3c1b-40c5-c13d-41140a9e17b4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/thinc/shims/pytorch.py:114: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
            "  with torch.cuda.amp.autocast(self._mixed_precision):\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This nsubj\n",
            "is ROOT\n",
            "a det\n",
            "sentence attr\n",
            ". punct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q4.A"
      ],
      "metadata": {
        "id": "s_-TDP1peNcS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "doc11 = model_sm(\"Financial analysts continue to predict a recession .\")\n",
        "for token in doc11:\n",
        "  print(token.text, token.dep_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ETWWzfiBeM4O",
        "outputId": "14e9286c-add8-41d3-9b17-9231398e7c79"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Financial amod\n",
            "analysts nsubj\n",
            "continue ROOT\n",
            "to aux\n",
            "predict xcomp\n",
            "a det\n",
            "recession dobj\n",
            ". punct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc12 = model_trf(\"Financial analysts continue to predict a recession .\")\n",
        "for token in doc12:\n",
        "  print(token.text, token.dep_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40J8LqDteD66",
        "outputId": "e09d1312-614c-4ca4-c504-6d35fd399fff"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Financial amod\n",
            "analysts nsubj\n",
            "continue ROOT\n",
            "to aux\n",
            "predict xcomp\n",
            "a det\n",
            "recession dobj\n",
            ". punct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q4.B"
      ],
      "metadata": {
        "id": "1Y4objMbe8aj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "doc21 = model_sm(\"Dow falls as recession indicator flashed red and economical worries continue through the month .\")\n",
        "for token in doc21:\n",
        "  print(token.text, token.dep_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3vNW78lewzX",
        "outputId": "3f4dd8c3-4898-48ee-ba1c-1089a1c4a2c2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dow nsubj\n",
            "falls ROOT\n",
            "as mark\n",
            "recession compound\n",
            "indicator nsubj\n",
            "flashed advcl\n",
            "red amod\n",
            "and cc\n",
            "economical conj\n",
            "worries nsubj\n",
            "continue ccomp\n",
            "through prep\n",
            "the det\n",
            "month pobj\n",
            ". punct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc22 = model_trf(\"Dow falls as recession indicator flashed red and economical worries continue through the month .\")\n",
        "for token in doc22:\n",
        "  print(token.text, token.dep_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssEDOH66fCiu",
        "outputId": "dc3b63b1-cef1-4144-dfb6-533b4f4fe516"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dow nsubj\n",
            "falls ROOT\n",
            "as mark\n",
            "recession compound\n",
            "indicator nsubj\n",
            "flashed advcl\n",
            "red oprd\n",
            "and cc\n",
            "economical amod\n",
            "worries nsubj\n",
            "continue conj\n",
            "through prep\n",
            "the det\n",
            "month pobj\n",
            ". punct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q4.C"
      ],
      "metadata": {
        "id": "mnehLJzEfq27"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "doc31 = model_sm(\"Figure skater lands historic quadruple jump in senior international competition at the 2019 World Figure Skating Championships on Day 3 but could only clinch a silver medal .The company is expected to report a loss of billions .\")\n",
        "for token in doc31:\n",
        "  print(token.text, token.dep_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C0crJ2b3fTWu",
        "outputId": "0fa61daf-28b7-4f19-cab6-0151bc664a4b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Figure nsubj\n",
            "skater advmod\n",
            "lands ROOT\n",
            "historic amod\n",
            "quadruple compound\n",
            "jump dobj\n",
            "in prep\n",
            "senior amod\n",
            "international amod\n",
            "competition pobj\n",
            "at prep\n",
            "the det\n",
            "2019 nummod\n",
            "World compound\n",
            "Figure compound\n",
            "Skating compound\n",
            "Championships pobj\n",
            "on prep\n",
            "Day pobj\n",
            "3 nummod\n",
            "but cc\n",
            "could aux\n",
            "only advmod\n",
            "clinch conj\n",
            "a det\n",
            "silver amod\n",
            "medal dobj\n",
            ".The punct\n",
            "company nsubjpass\n",
            "is auxpass\n",
            "expected conj\n",
            "to aux\n",
            "report xcomp\n",
            "a det\n",
            "loss dobj\n",
            "of prep\n",
            "billions pobj\n",
            ". punct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc32 = model_trf(\"Figure skater lands historic quadruple jump in senior international competition at the 2019 World Figure Skating Championships on Day 3 but could only clinch a silver medal .The company is expected to report a loss of billions .\")\n",
        "for token in doc32:\n",
        "  print(token.text, token.dep_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KKJR_EWXf4qg",
        "outputId": "209a36a9-2d32-4b3a-f286-69e8d27b9ce4"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Figure compound\n",
            "skater nsubj\n",
            "lands ccomp\n",
            "historic amod\n",
            "quadruple amod\n",
            "jump dobj\n",
            "in prep\n",
            "senior amod\n",
            "international amod\n",
            "competition pobj\n",
            "at prep\n",
            "the det\n",
            "2019 nummod\n",
            "World compound\n",
            "Figure compound\n",
            "Skating compound\n",
            "Championships pobj\n",
            "on prep\n",
            "Day pobj\n",
            "3 nummod\n",
            "but cc\n",
            "could aux\n",
            "only advmod\n",
            "clinch conj\n",
            "a det\n",
            "silver compound\n",
            "medal dobj\n",
            ".The det\n",
            "company nsubjpass\n",
            "is auxpass\n",
            "expected ROOT\n",
            "to aux\n",
            "report xcomp\n",
            "a det\n",
            "loss dobj\n",
            "of prep\n",
            "billions pobj\n",
            ". punct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Q4.D"
      ],
      "metadata": {
        "id": "rZC0VOO2gZR0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "doc41 = model_sm(\"The investment in the new product is expected to pay off .\")\n",
        "for token in doc41:\n",
        "  print(token.text, token.dep_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhGynYMYgJKe",
        "outputId": "64b0d569-684b-4a9d-8a6d-9d425b0849d7"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The det\n",
            "investment nsubjpass\n",
            "in prep\n",
            "the det\n",
            "new amod\n",
            "product pobj\n",
            "is auxpass\n",
            "expected ROOT\n",
            "to aux\n",
            "pay xcomp\n",
            "off prt\n",
            ". punct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc42 = model_trf(\"The investment in the new product is expected to pay off .\")\n",
        "for token in doc42:\n",
        "  print(token.text, token.dep_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-wa5Ga-gzhn",
        "outputId": "5a9bf72c-578d-4dd1-db22-2bc9f69a999c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The det\n",
            "investment nsubjpass\n",
            "in prep\n",
            "the det\n",
            "new amod\n",
            "product pobj\n",
            "is auxpass\n",
            "expected ROOT\n",
            "to aux\n",
            "pay xcomp\n",
            "off prt\n",
            ". punct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nOQ7-8eeg5zR"
      },
      "execution_count": 28,
      "outputs": []
    }
  ]
}