main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r-UgA6tdOuJF"
   },
   "source": [
    "# Lab | Transformers\n",
    "\n",
    "---\n",
    "\n",
    "### Section structure\n",
    "\n",
    "1. The open-source ecosystem: increasing accessibility to machine learning (ML) software and hardware\n",
    "2. Some simple code demonstrations\n",
    "3. Q&A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-AlHgXZCVNr0"
   },
   "source": [
    "## 1. Ease-of-use: Using Transformers in 3 lines of code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LpOoqdeqbCI-"
   },
   "source": [
    "\n",
    "**Overview of different tasks that can be automated with ML**\n",
    "* Key ingredients: (1) a model trained on a specific task; (2) input data (e.g. texts or images); (3) output produced by the model.\n",
    "* Transformers are currently the most popular type of deep learning algorithm. Most tasks below are solved with Transformers. There might be other types of algorithms coming up in the medium term.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QP60sINRSprG"
   },
   "source": [
    "**Install the Transformers library & dependencies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dBMBmzH0StsF",
    "outputId": "960151d8-f350-449e-9d92-eb4e6337de91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Downloading tf_keras-2.16.0-py3-none-any.whl (1.7 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m0:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: tf-keras\n",
      "Successfully installed tf-keras-2.16.0\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Requirement already satisfied: torch in /Users/joaorochaemelo/code/IH/venv_ironhack/lib/python3.11/site-packages (2.2.2)\n",
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      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers  # The Transformers library from Hugging Face\n",
    "!pip install sentencepiece\n",
    "!pip install wikipedia\n",
    "!pip install accelerate\n",
    "!pip install tf-keras\n",
    "!pip install torch\n",
    "\n",
    "# NOTE: you might need to restart you jupyter kernel after installing the libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D5FYbEIBEzEu"
   },
   "source": [
    "**The Hugging Face Pipeline**\n",
    "* Makes automation of many NLP tasks possible in 3 lines of code\n",
    "* Detailed documentation is available [here](https://huggingface.co/transformers/main_classes/pipelines.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "0U1iHK0ETBXf"
   },
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note : You might need more libraries or updates to run the cells below, if that is the case, follow the error messages and pip install accordingly. Chat gpt can help you if given the error messages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rKt3KNCBQ2fe"
   },
   "source": [
    "### 2.1 Many models tailored to specific tasks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GbZWdV3LWP2l"
   },
   "source": [
    "#### 2.1.1 Text classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "390ar7USTP30"
   },
   "source": [
    "Let's select a popular text classification model in the [HF model hub](https://huggingface.co/models?pipeline_tag=text-classification&sort=downloads).\n",
    "\n",
    "Here we chose \"cardiffnlp/twitter-roberta-base-irony\".\n",
    "\n",
    "We will classify text into ironic or non ironic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "outputId": "57cf0922-664b-4c97-98d7-d0670299a9a4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\drago\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\tf_keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pipeline_classification = pipeline(\"text-classification\", model=\"cardiffnlp/twitter-roberta-base-irony\")  # cardiffnlp/twitter-roberta-base-irony, SamLowe/roberta-base-go_emotions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the model we can pass it a string and have it give us a classification.\n",
    "\n",
    "Feel free to experiment with different sentences by changing the contents of the variable text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Uhf7MYboRdEy",
    "outputId": "50c58e9b-63a0-41e1-93f9-490ebc249306"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'label': 'irony', 'score': 0.9424387812614441}, {'label': 'non_irony', 'score': 0.057561274617910385}]\n"
     ]
    }
   ],
   "source": [
    "text = \"Well that workshop was totally worth my time...\"  # \"Well that workshop was totally worth my time...\"  \"This smells weird, I'm not sure if I should eat this ... Yikes, it tasted like old socks!\"\n",
    "output = pipeline_classification(text, top_k=10)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make the output a little cleaner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zwtj5Lm8cdh8",
    "outputId": "b7d92ba0-4559-411a-a6a6-53ab58570a45"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       label     score\n",
      "0      irony  0.942439\n",
      "1  non_irony  0.057561\n"
     ]
    }
   ],
   "source": [
    "# make output a bit cleaner\n",
    "df_output = pd.DataFrame(output)\n",
    "print(df_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, in a few lines of code and by leveraging an existing model we can classify text as ironic or non ironic. Now you have one more tool in your machine learning toolbox.\n",
    "\n",
    "Remember that : 'when you only have a hammer everything is a nail'. But if we want to build a house (perform machine learning the right way), we need to use the right tool for the right job."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HIOYqJZ9kOtk"
   },
   "source": [
    "#### 2.1.2 Machine Translation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vk69-EpwvlbH"
   },
   "source": [
    "* Open source machine translation (MT) models enable you to translate between many different languages without Google Translate.\n",
    "* [University of Helsinki](https://huggingface.co/Helsinki-NLP) uploaded models for more than 1000 language pairs to the Hugging Face hub\n",
    "* [Facebook AI](https://huggingface.co/models?search=facebook+m2m) open-sourced several multi-lingual models\n",
    "* The [EasyNMT library](https://github.com/UKPLab/EasyNMT), provides an easy wrapper for all these models\n",
    "* Most machine translation models translate between two languages in one direction (e.g. German to English, but not English to German), some can translate in multiple directions.\n"
   ]
  },
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    "id": "hKtdnG1FkRQt",
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   "outputs": [],
   "source": [
    "# translation pipeline docs: https://huggingface.co/transformers/main_classes/pipelines.html#transformers.TranslationPipeline\n",
    "pipeline_translate = pipeline(\"translation\", model=\"facebook/m2m100_418M\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation.\n",
    "\n",
    "The model that can directly translate between the 9,900 directions of 100 languages.\n",
    "\n",
    "Here we specify to translate from German 'de' to English 'en'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p7pra41KlFwf",
    "outputId": "d5513401-2879-42ca-d2c7-31d006e16ffe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'translation_text': 'I am a fish'}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = \"Ich bin ein Fisch\"\n",
    "pipeline_translate(text, src_lang=\"de\", tgt_lang=\"en\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do the same but with and entire wikipedia page in german."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "v7w6BRcOsjt7",
    "outputId": "9d4e075b-41b4-4032-af44-b980705b3156"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original text:\n",
      "Donald John Trump [ˈdɑn.əld dʒɑn tɹɐmp] (* 14. Juni 1946 in New York City) ist ein US-amerikanischer Unternehmer, Entertainer und Politiker der Republikanischen Partei. Von 2017 bis 2021 war er der 45. Präsident der Vereinigten Staaten. Er gilt als einer der umstrittensten Politiker der US-Geschichte und ist der erst\n",
      "\n",
      "Translated text:\n",
      "Donald John Trump [ˈdɑn.əld dʒɑn tɔmp] (* 14 June 1946 in New York City) is an American entrepreneur, entertainer and politician of the Republican Party. From 2017 to 2021 he was the 45th President of the United States.\n"
     ]
    }
   ],
   "source": [
    "# download any text from wikipedia, via  https://pypi.org/project/wikipedia/\n",
    "import wikipedia\n",
    "wikipedia.set_lang(\"de\")\n",
    "\n",
    "text = wikipedia.summary(\"Donald Trump\").replace('\\n', ' ')[:318]\n",
    "print(f\"Original text:\\n{text}\\n\")\n",
    "\n",
    "# translate the text from wikipedia\n",
    "text_translated = pipeline_translate(text, src_lang=\"de\", tgt_lang=\"en\")\n",
    "print(f\"Translated text:\\n{text_translated[0]['translation_text']}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YK9-BIv-WdyR"
   },
   "source": [
    "#### 2.1.3 Text Summarization"
   ]
  },
  {
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    "id": "IuzEFpiQ2JuS",
    "outputId": "3876203c-5200-4732-bbbd-d56591fc1c4c"
   },
   "outputs": [
    {
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      "text/plain": [
       "config.json:   0%|          | 0.00/1.80k [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "output_type": "display_data"
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    }
   ],
   "source": [
    "# docs for summarisation pipeline: https://huggingface.co/transformers/main_classes/pipelines.html#summarizationpipeline\n",
    "pipeline_summarize = pipeline(\"summarization\", model=\"sshleifer/distilbart-cnn-12-6\")  # sshleifer/distilbart-cnn-12-6 , google/pegasus-cnn_dailymail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VJWNGmGe2MUt",
    "outputId": "c8eef094-5a6f-4323-ac37-eae0811fde71"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original text:\n",
      "Donald John Trump (born June 14, 1946) is an American politician, media personality, and businessman who served as the 45th president of the United States from 2017 to 2021. Trump received a Bachelor of Science in economics from the University of Pennsylvania in 1968. His father named him president of his real estate business in 1971. Trump renamed it the Trump Organization and reoriented the company toward building and renovating skyscrapers, hotels, casinos, and golf courses. After a series of business failures in the late 1990s, he launched successful side ventures, mostly licensing the Trump name. From 2004 to 2015, he co-produced and hosted the reality television series The Apprentice. He and his businesses have been plaintiffs or defendants in more than 4,000 legal actions, including six business bankruptcies. Trump won the 2016 presidential election as the Republican Party nominee against Democratic Party candidate Hillary Clinton while losing the popular vote. A special counsel investigation established that Russia had interfered in the election to favor Trump. During the campaign, his political positions were described as populist, protectionist, isolationist, and nationalist. His election and policies sparked numerous protests. He was the only U.S. president without prior military or government experience. Trump promoted conspiracy theories and made many false and misleading statements during his campaigns and presidency, to a degree unprecedented in American politics. Many of his comments and actions have been characterized as racially charged, racist, and misogynistic. As president, Trump ordered a travel ban on citizens from several Muslim-majority countries, diverted military funding toward building a wall on the U.S.–Mexico border, and implemented a family separation policy. He rolled back more than 100 environmental policies and regulations. He signed the Tax Cuts and Jobs Act of 2017, which cut taxes and eliminated the individual health insurance mandate penalty of the Affordable Care Act. He appointed Neil Gorsuch, Brett Kavanaugh, and Amy Coney Barrett to the U.S. Supreme Court. He reacted slowly to the COVID-19 pandemic, ignored or contradicted many recommendations from health officials, used political pressure to interfere with testing efforts, and spread misinformation about unproven treatments. Trump initiated a trade war with China and withdrew the U.S. from the proposed Trans-Pacific Partnership trade agreement, the Paris Agreement on climate change, and the Iran nuclear deal. He met with North Korean leader Kim Jong Un three times but made no progress on denuclearization. Trump is the only U.S. president to have been impeached twice, in 2019 for abuse of power and obstruction of Congress after he pressured Ukraine to investigate Joe Biden, and in 2021 for incitement of insurrection. The Senate acquitted him in both cases. Trump lost the 2020 presidential election to Biden but refused to concede, falsely claiming widespread electoral fraud and attempting to overturn the results. On January 6, 2021, he urged his supporters to march to the U.S. Capitol, which many of them attacked. Scholars and historians rank Trump as one of the worst presidents in American history. Since leaving office, Trump has continued to dominate the Republican Party and is their candidate again in the 2024 presidential election. In May 2024, a jury in New York found Trump guilty on 34 felony counts of falsifying business records related to a hush money payment to Stormy Daniels in an attempt to influence the 2016 election, making him the first former U.S. president to be convicted of a crime. He has been indicted in three other jurisdictions on 54 other felony counts related to his mishandling of classified documents and efforts to overturn the 2020 presidential election. In civil proceedings, Trump was found liable for sexual abuse and defamation in 2023, defamation in 2024, and financial fraud in 2024.\n",
      "\n",
      "Summarized text:\n",
      " Donald John Trump is the 45th president of the United States . He won the 2016 presidential election as the Republican Party nominee against Hillary Clinton\n"
     ]
    }
   ],
   "source": [
    "# download any long text from wikipedia, via  https://pypi.org/project/wikipedia/\n",
    "import wikipedia\n",
    "wikipedia.set_lang(\"en\")\n",
    "\n",
    "text_long = wikipedia.summary(\"Donald Trump\").replace('\\n', ' ')\n",
    "print(f\"Original text:\\n{text_long}\\n\")\n",
    "\n",
    "# translate the text from wikipedia\n",
    "text_summarized = pipeline_summarize(text_long, min_length=5, max_length=30)\n",
    "print(f\"Summarized text:\\n{text_summarized[0]['summary_text']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2qVNMGy4ZQMU"
   },
   "source": [
    "#### 2.1.4 Named Entity Recognition\n",
    "\n",
    "NER is a task that involves identifying and classifying specific entities in text into predefined categories, such as names of people, organizations, locations, dates, and more. \n",
    "\n",
    "For example, in the sentence \"Apple Inc. was founded by Steve Jobs in California,\" NER would recognize \"Apple Inc.\" as an organization, \"Steve Jobs\" as a person, and \"California\" as a location."
   ]
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at dslim/bert-base-NER-uncased were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
      "- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
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    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipeline_ner = pipeline(\"token-classification\", model=\"dslim/bert-base-NER-uncased\", aggregation_strategy=\"simple\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
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     "height": 1000
    },
    "id": "QW99M5wiZmaL",
    "outputId": "149f26db-6829-4d5b-806b-4b9e312b2b36"
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   "outputs": [
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       "      <td>0.992383</td>\n",
       "      <td>trump</td>\n",
       "      <td>828</td>\n",
       "      <td>833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MISC</td>\n",
       "      <td>0.873182</td>\n",
       "      <td>republican</td>\n",
       "      <td>876</td>\n",
       "      <td>886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MISC</td>\n",
       "      <td>0.978373</td>\n",
       "      <td>democratic</td>\n",
       "      <td>909</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PER</td>\n",
       "      <td>0.994271</td>\n",
       "      <td>hillary clinton</td>\n",
       "      <td>936</td>\n",
       "      <td>951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LOC</td>\n",
       "      <td>0.997165</td>\n",
       "      <td>russia</td>\n",
       "      <td>1032</td>\n",
       "      <td>1038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PER</td>\n",
       "      <td>0.982982</td>\n",
       "      <td>trump</td>\n",
       "      <td>1079</td>\n",
       "      <td>1084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LOC</td>\n",
       "      <td>0.849478</td>\n",
       "      <td>u. s.</td>\n",
       "      <td>1274</td>\n",
       "      <td>1278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PER</td>\n",
       "      <td>0.986752</td>\n",
       "      <td>trump</td>\n",
       "      <td>1338</td>\n",
       "      <td>1343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MISC</td>\n",
       "      <td>0.994762</td>\n",
       "      <td>american</td>\n",
       "      <td>1485</td>\n",
       "      <td>1493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PER</td>\n",
       "      <td>0.980122</td>\n",
       "      <td>trump</td>\n",
       "      <td>1622</td>\n",
       "      <td>1627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MISC</td>\n",
       "      <td>0.987600</td>\n",
       "      <td>muslim</td>\n",
       "      <td>1674</td>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LOC</td>\n",
       "      <td>0.977429</td>\n",
       "      <td>u. s.</td>\n",
       "      <td>1757</td>\n",
       "      <td>1761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LOC</td>\n",
       "      <td>0.997740</td>\n",
       "      <td>mexico</td>\n",
       "      <td>1762</td>\n",
       "      <td>1768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MISC</td>\n",
       "      <td>0.891977</td>\n",
       "      <td>tax cuts and jobs act of</td>\n",
       "      <td>1904</td>\n",
       "      <td>1928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MISC</td>\n",
       "      <td>0.960363</td>\n",
       "      <td>affordable care act</td>\n",
       "      <td>2021</td>\n",
       "      <td>2040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PER</td>\n",
       "      <td>0.996589</td>\n",
       "      <td>neil gorsuch</td>\n",
       "      <td>2055</td>\n",
       "      <td>2067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PER</td>\n",
       "      <td>0.997994</td>\n",
       "      <td>brett kavanaugh</td>\n",
       "      <td>2069</td>\n",
       "      <td>2084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PER</td>\n",
       "      <td>0.997711</td>\n",
       "      <td>amy coney barrett</td>\n",
       "      <td>2090</td>\n",
       "      <td>2107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LOC</td>\n",
       "      <td>0.901173</td>\n",
       "      <td>u. s.</td>\n",
       "      <td>2115</td>\n",
       "      <td>2119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ORG</td>\n",
       "      <td>0.821682</td>\n",
       "      <td>supreme court</td>\n",
       "      <td>2120</td>\n",
       "      <td>2133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>MISC</td>\n",
       "      <td>0.937786</td>\n",
       "      <td>covid - 19</td>\n",
       "      <td>2160</td>\n",
       "      <td>2168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PER</td>\n",
       "      <td>0.980800</td>\n",
       "      <td>trump</td>\n",
       "      <td>2359</td>\n",
       "      <td>2364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>LOC</td>\n",
       "      <td>0.996960</td>\n",
       "      <td>china</td>\n",
       "      <td>2392</td>\n",
       "      <td>2397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>LOC</td>\n",
       "      <td>0.991840</td>\n",
       "      <td>u. s</td>\n",
       "      <td>2415</td>\n",
       "      <td>2418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>MISC</td>\n",
       "      <td>0.908778</td>\n",
       "      <td>trans - pacific partnership</td>\n",
       "      <td>2438</td>\n",
       "      <td>2463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>MISC</td>\n",
       "      <td>0.889306</td>\n",
       "      <td>paris agreement</td>\n",
       "      <td>2485</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>MISC</td>\n",
       "      <td>0.697912</td>\n",
       "      <td>climate change</td>\n",
       "      <td>2504</td>\n",
       "      <td>2518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>LOC</td>\n",
       "      <td>0.906911</td>\n",
       "      <td>iran</td>\n",
       "      <td>2528</td>\n",
       "      <td>2532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>MISC</td>\n",
       "      <td>0.987541</td>\n",
       "      <td>north korean</td>\n",
       "      <td>2559</td>\n",
       "      <td>2571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>PER</td>\n",
       "      <td>0.993055</td>\n",
       "      <td>kim jong un</td>\n",
       "      <td>2579</td>\n",
       "      <td>2590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>PER</td>\n",
       "      <td>0.989296</td>\n",
       "      <td>trump</td>\n",
       "      <td>2645</td>\n",
       "      <td>2650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>LOC</td>\n",
       "      <td>0.874581</td>\n",
       "      <td>u. s.</td>\n",
       "      <td>2663</td>\n",
       "      <td>2667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   entity_group     score                         word  start   end\n",
       "0           PER  0.989843            donald john trump      0    17\n",
       "1          MISC  0.991848                     american     45    53\n",
       "2           LOC  0.992675                united states    141   154\n",
       "3           PER  0.989312                        trump    174   179\n",
       "4           ORG  0.697325   university of pennsylvania    233   259\n",
       "5           PER  0.969404                        trump    337   342\n",
       "6           ORG  0.712140           trump organization    358   376\n",
       "7           PER  0.913634                        trump    597   602\n",
       "8          MISC  0.953361               the apprentice    684   698\n",
       "9           PER  0.992383                        trump    828   833\n",
       "10         MISC  0.873182                   republican    876   886\n",
       "11         MISC  0.978373                   democratic    909   919\n",
       "12          PER  0.994271              hillary clinton    936   951\n",
       "13          LOC  0.997165                       russia   1032  1038\n",
       "14          PER  0.982982                        trump   1079  1084\n",
       "15          LOC  0.849478                        u. s.   1274  1278\n",
       "16          PER  0.986752                        trump   1338  1343\n",
       "17         MISC  0.994762                     american   1485  1493\n",
       "18          PER  0.980122                        trump   1622  1627\n",
       "19         MISC  0.987600                       muslim   1674  1680\n",
       "20          LOC  0.977429                        u. s.   1757  1761\n",
       "21          LOC  0.997740                       mexico   1762  1768\n",
       "22         MISC  0.891977     tax cuts and jobs act of   1904  1928\n",
       "23         MISC  0.960363          affordable care act   2021  2040\n",
       "24          PER  0.996589                 neil gorsuch   2055  2067\n",
       "25          PER  0.997994              brett kavanaugh   2069  2084\n",
       "26          PER  0.997711            amy coney barrett   2090  2107\n",
       "27          LOC  0.901173                        u. s.   2115  2119\n",
       "28          ORG  0.821682                supreme court   2120  2133\n",
       "29         MISC  0.937786                   covid - 19   2160  2168\n",
       "30          PER  0.980800                        trump   2359  2364\n",
       "31          LOC  0.996960                        china   2392  2397\n",
       "32          LOC  0.991840                         u. s   2415  2418\n",
       "33         MISC  0.908778  trans - pacific partnership   2438  2463\n",
       "34         MISC  0.889306              paris agreement   2485  2500\n",
       "35         MISC  0.697912               climate change   2504  2518\n",
       "36          LOC  0.906911                         iran   2528  2532\n",
       "37         MISC  0.987541                 north korean   2559  2571\n",
       "38          PER  0.993055                  kim jong un   2579  2590\n",
       "39          PER  0.989296                        trump   2645  2650\n",
       "40          LOC  0.874581                        u. s.   2663  2667"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wikipedia\n",
    "wikipedia.set_lang(\"en\")\n",
    "\n",
    "text_long = wikipedia.summary(\"Donald Trump\").replace('\\n', ' ')\n",
    "\n",
    "output = pipeline_ner(text_long)\n",
    "\n",
    "pd.DataFrame(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TXPa9hHmP8vJ"
   },
   "source": [
    "### 2.2. Universal models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lEGnV9KabQg4"
   },
   "source": [
    "The models mentioned above are designed to excel at a single specific task on a particular dataset. The key advantage of these models is their high performance and accuracy on that specific task and dataset. \n",
    "\n",
    "However, in real-world applications, the problems you'll face often require solving slightly different tasks, possibly with varied category definitions or applied to different types of texts.\n",
    "\n",
    "Universal models can help address this challenge. Although they also focus on one task, the task is general or universal enough that many other tasks can be reformulated into it. Two examples of universal tasks are:\n",
    "\n",
    "- Natural Language Inference (NLI): A task that can effectively solve a wide range of classification tasks by determining whether a given premise supports, contradicts, or is neutral with respect to a hypothesis.\n",
    "\n",
    "- Token Generation: An even more universal task that can be applied to solve virtually any text-related task, including translation, summarization, and text completion.\n",
    "\n",
    "These universal tasks enable the models to be versatile and adaptable to various problems beyond the specific ones they were initially trained on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ocJsfbLcQPpO"
   },
   "source": [
    "#### Zero-shot classification\n",
    "\n",
    "\n",
    "Zero-shot classification is a technique where a model can categorize data into classes it has never seen before. \n",
    "\n",
    "Instead of relying on labeled examples for each class, the model understands the relationship between the input and the class descriptions, allowing it to make accurate predictions without needing specific training on those classes."
   ]
  },
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   "source": [
    "pipeline_zeroshot_classification = pipeline(\"zero-shot-classification\", model=\"MoritzLaurer/mDeBERTa-v3-base-mnli-xnli\")"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will give the model a list of classes ('payment issues', 'travel advice', 'bug report') for it to classify our string."
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       "            class probability\n",
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    "text = \"Customer: I have not received my reimbursement yet. What the hell is going on?\"\n",
    "classes = ['payment issues', 'travel advice', 'bug report']  # \"account opening\", \"customer complaint\"\n",
    "\n",
    "#text = \"I do not think the government is trustworthy anymore. We need to mobilize and resist!\"\n",
    "#classes = [\"civil disobedience\", \"praise of the government\", \"travel advice\"]  # \"collective action\"\n",
    "\n",
    "output = pipeline_zeroshot_classification(text, classes, multi_label=True)\n",
    "\n",
    "pd.DataFrame(data=[output[\"labels\"], output[\"scores\"]], index=[\"class\", \"probability\"]).T\n"
   ]
  },
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   "source": [
    "## Exercise\n",
    "\n",
    "Now it is your turn to go to the hugging face library https://huggingface.co/models?pipeline_tag=text-classification&sort=downloads\n",
    "\n",
    "(you can select on the left menu of the website the type of NLP tasks you want models to perform)\n",
    "\n",
    "- Find an NLP model that we have not used previously.\n",
    "\n",
    "- Get some data from wikipedia or elsewhere.\n",
    "\n",
    "- Perform inference with the model and print the result!\n",
    "\n",
    "- Comment your code along the way, describe what your model does and what your end goal is from input to output.\n",
    "\n",
    "Have fun!"
   ]
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   "execution_count": null,
   "metadata": {},
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   "source": [
    "# Your code here :"
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