huggingfaceTasks_NLP.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "amjR--CkZjJJ"
      },
      "outputs": [],
      "source": [
        "!pip -q install transformers sentence_transformers\n",
        "!pip -q install huggingface_hub langchain"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['HUGGINGFACEHUB_API_TOKEN'] = ''"
      ],
      "metadata": {
        "id": "ZrkNqescbqw1"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tasks & its models\n",
        "\n",
        "1) Conversational : facebook/blenderbot-400M-distill\n",
        "\n",
        "2) Fill-Mask: distilroberta-base\n",
        "\n",
        "\n",
        "3) Question Answering:\n",
        "deepset/roberta-base-squad2\n",
        "\n",
        "4) Sentence Similarity:sentence-transformers/all-MiniLM-L6-v2\n",
        "\n",
        "5) Summarization:sshleifer/distilbart-cnn-12-6\n",
        "\n",
        "6) Table Question Answering :\n",
        "google/tapas-base-finetuned-wtq\n",
        "\n",
        "7) Text Classification:distilbert-base-uncased-finetuned-sst-2-english\n",
        "\n",
        "8) Text Generation: gpt2\n",
        "\n",
        "\n",
        "9) Token Classification: dslim/bert-base-NER\n",
        "\n",
        "\n",
        "10) Translation :Helsinki-NLP/opus-mt-en-fr\n",
        "\n",
        "11) Zero-Shot Classification:facebook/bart-large-mnli"
      ],
      "metadata": {
        "id": "6Elo6gV91-hh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## We will see\n",
        "\n",
        "Langchain LLM can support only Text Generation Tasks. \n",
        "\n",
        "Rest of the tasks, we will look at the Transformers pipeline method."
      ],
      "metadata": {
        "id": "uK4oJoKU3xOs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Transformers Pipeline for Task-1\n",
        "\n",
        "from transformers import pipeline, Conversation\n",
        "converse = pipeline(\"conversational\",model = 'facebook/blenderbot-400M-distill')"
      ],
      "metadata": {
        "id": "euFu_sfbbrGW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conversation_1 = Conversation(\"Going to Mars - any suggestions?\")"
      ],
      "metadata": {
        "id": "iid5aaLjbrMJ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "converse(conversations=[conversation_1])"
      ],
      "metadata": {
        "id": "Prmc2a8p4wjW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#HF API inference \n",
        "import json\n",
        "import requests"
      ],
      "metadata": {
        "id": "JATHarPJ43l2"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "API_URL = \"https://api-inference.huggingface.co/models/distilroberta-base\""
      ],
      "metadata": {
        "id": "zjmKJRuC5WMY"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "API_TOKEN=''"
      ],
      "metadata": {
        "id": "XDe3Ckx-5tmu"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "headers = {\"Authorization\": f\"Bearer {API_TOKEN}\"}"
      ],
      "metadata": {
        "id": "feTNcP1x5noG"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Helper function\n",
        "def query(payload):\n",
        "    data = json.dumps(payload)\n",
        "    response = requests.request(\"POST\", \n",
        "                                API_URL, \n",
        "                                headers=headers, data=data)\n",
        "    return json.loads(response.content.decode(\"utf-8\"))"
      ],
      "metadata": {
        "id": "eDq-Vbip5yu7"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = query(\"Amsterdam is a <mask> in Netherlands\")"
      ],
      "metadata": {
        "id": "Elrl_mN5brQ8"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "abbpCoPM6FF6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Using langchain HuggingFace Hub for text-generation integrations\n",
        "### Model is gpt2\n",
        "from langchain import PromptTemplate, HuggingFaceHub, LLMChain"
      ],
      "metadata": {
        "id": "aVLaZNCF6efd"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm=HuggingFaceHub(repo_id=\"gpt2\")"
      ],
      "metadata": {
        "id": "2bwcqPdt6xu9"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm(\"Where are you now?\")"
      ],
      "metadata": {
        "id": "XXJBwtz-dnS5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#question answering\n",
        "model_repo = \"deepset/roberta-base-squad2\"\n",
        "\n",
        "qa_model = pipeline(\"question-answering\",\n",
        "                    model=model_repo)"
      ],
      "metadata": {
        "id": "sm8PL5QgdnZ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "context = \"My name is Martian Martin and I live in Mohave Desert.\"\n",
        "question = \"Where do I live?\""
      ],
      "metadata": {
        "id": "_oUVEdUhdneQ"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "qa_model(question = question, context = context)"
      ],
      "metadata": {
        "id": "rP1FAujgwGpC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Summarisation\n",
        "\n",
        "model_repo = 'sshleifer/distilbart-cnn-12-6'\n",
        "\n",
        "summary_pipe = pipeline('summarization',\n",
        "                        model=model_repo)"
      ],
      "metadata": {
        "id": "BP9KAUiIwGgU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summary_pipe(\"\"\"\n",
        "Research papers can be summarized to allow researchers to spend less time selecting which articles to read. There are several approaches you can take for a task like this:\n",
        "\n",
        "Use an existing extractive summarization model on the Hub to do inference.\n",
        "Pick an existing language model trained for academic papers. This model can then be trained in a process called fine-tuning so it can solve the summarization task.\n",
        "Use a sequence-to-sequence model like T5 for abstractive text summarization.\n",
        "\"\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0SH8t3SQ-z2A",
        "outputId": "c1ee93cd-51e6-40d5-895e-1400b5d26be2"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Your max_length is set to 142, but you input_length is only 106. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=53)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'summary_text': ' Research papers can be summarized to allow researchers to spend less time selecting which articles to read . There are several approaches you can take for a task like this: Use an existing extractive summarization model on the Hub to do this . Pick an existing language model trained for academic papers .'}]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Table QnA\n",
        "\n",
        "from transformers import pipeline\n",
        "import pandas as pd\n",
        "\n",
        "# prepare table + question\n",
        "data = {\"Actors\": [\"Brad Pitt\", \n",
        "                   \"Leonardo Di Caprio\", \n",
        "                   \"George Clooney\"], \n",
        "        \"Number of movies\": [\"87\", \n",
        "                             \"53\", \n",
        "                             \"69\"]}\n",
        "\n",
        "table = pd.DataFrame.from_dict(data)"
      ],
      "metadata": {
        "id": "1PX8Q74OwGOi"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"how many movies does Leonardo Di Caprio have?\""
      ],
      "metadata": {
        "id": "NoQRNfGfwGGE"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tqa = pipeline(task=\"table-question-answering\", model=\"google/tapas-large-finetuned-wtq\")"
      ],
      "metadata": {
        "id": "bDPB-6OVwF8j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(tqa(table=table, query=question))"
      ],
      "metadata": {
        "id": "MBOEl-TodnjI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb945746-0bad-4110-c735-c984f5cd68d5"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'answer': 'SUM > 53', 'coordinates': [(1, 1)], 'cells': ['53'], 'aggregator': 'SUM'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Text Classification\n",
        "\n",
        "text_classifier = pipeline(\"text-classification\", \n",
        "                      model = \"roberta-large-mnli\")"
      ],
      "metadata": {
        "id": "scGjBb9fdnou"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "NLI model takes a premise and a hypothesis and returns a class that can either be:\n",
        "\n",
        "entailment: which means the hypothesis is true.\n",
        "\n",
        "contraction: which means the hypothesis is false.\n",
        "\n",
        "neutral: which means there's no relation between the hypothesis and the premise."
      ],
      "metadata": {
        "id": "0SbNq5-9ABCy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_query = \"Tin Tin comics are the best written by Herge?\""
      ],
      "metadata": {
        "id": "WD8hu1nMp-Lg"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_classifier(test_query)"
      ],
      "metadata": {
        "id": "eNgn1QW7uTTV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#NER pipeline\n",
        "\n",
        "ner_classifier = pipeline(\"ner\",\n",
        "                      model=\"dslim/bert-base-NER\")"
      ],
      "metadata": {
        "id": "6SxlXPo5ufLP"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ner_classifier(\"Hello I'm Omar and I live in Zürich.\")"
      ],
      "metadata": {
        "id": "1CpDQvYFbrV6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f520ca2-d18e-44c6-d97c-498ebfd489c4"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'entity': 'B-PER',\n",
              "  'score': 0.99869895,\n",
              "  'index': 5,\n",
              "  'word': 'Omar',\n",
              "  'start': 10,\n",
              "  'end': 14},\n",
              " {'entity': 'B-LOC',\n",
              "  'score': 0.99772304,\n",
              "  'index': 10,\n",
              "  'word': 'Zürich',\n",
              "  'start': 29,\n",
              "  'end': 35}]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Translation\n",
        "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
        "\n",
        "translator = pipeline(\"translation\", \n",
        "                      model=model_checkpoint)\n"
      ],
      "metadata": {
        "id": "VagKvT9H7NCv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492,
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        },
        "outputId": "0bb5c40f-0446-4678-950f-78c685a97918"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/293 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e2b3df1ecfe143c3be28f797f4d362f2"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6449e836c254388ba43c307719dc5bb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.34M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ff4dfba61f544df803e95c52ce56e97"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/models/marian/tokenization_marian.py:194: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sacremoses"
      ],
      "metadata": {
        "id": "aVlfEEIqBitc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "translator(\"Where are you living?\")"
      ],
      "metadata": {
        "id": "xC-RRSDkz1c4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1483893-8a54-420c-f495-4ca230ef3662"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'translation_text': 'Où habites-tu ?'}]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#zero-shot-classification\n",
        "\n",
        "pipe = pipeline(model=\"facebook/bart-large-mnli\")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "jeYuqRlupYbx",
        "outputId": "f1d9113e-32a7-4af7-967f-43b0e6837e7f"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7063d4b9e1c542faa0fcb62fb15b204a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/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": "9756d7cb26564f1ab388adcebff048d4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe(\"I have a problem with my iphone that needs to be resolved asap!\",\n",
        "    candidate_labels=[\"urgent\", \n",
        "                      \"not urgent\", \n",
        "                      \"phone\", \n",
        "                      \"tablet\", \n",
        "                      \"computer\"],)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmquiHEVpYrG",
        "outputId": "ba33ccf9-dccb-439d-f33e-8ea400c371a7"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sequence': 'I have a problem with my iphone that needs to be resolved asap!',\n",
              " 'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'],\n",
              " 'scores': [0.5227572321891785,\n",
              "  0.45814111828804016,\n",
              "  0.014264623634517193,\n",
              "  0.002685008803382516,\n",
              "  0.002152056200429797]}"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    }
  ]
}