{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9784c439",
   "metadata": {},
   "source": [
    "# Transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "027916b4",
   "metadata": {},
   "source": [
    "In this part we will cover Transformer based large pretrained models. \n",
    "\n",
    "This notebook focus on showing how you can use the widely known Hugging Face library to apply different types of transformer models to a different range of tasks, from NLP to CV.\n",
    "\n",
    "We hope you learn how you can levarage pretrained transformer-based models and how to fine-tune them to a specific downstream task. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da8f539",
   "metadata": {},
   "source": [
    "To dive deep into transformers, we recommend to start by reading\n",
    "    \n",
    "- http://jalammar.github.io/illustrated-transformer/\n",
    "\n",
    "This gives a step by step explanation of the original paper [Attention Is All You Need](https://arxiv.org/abs/1706.03762)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f4076c",
   "metadata": {},
   "source": [
    "#### Code implemantation\n",
    "\n",
    "It's also good pratice to try implementing yourself the original code before using a Transformers library:\n",
    "\n",
    "Code Implementation\n",
    "- http://nlp.seas.harvard.edu/2018/04/03/attention.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "184e2623",
   "metadata": {},
   "source": [
    "# Hugging Face 🤗"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87e97321",
   "metadata": {},
   "source": [
    "\"Training a transformer model and deploying these models can be quite challeging. In general these models have millions to tens of billions of parameters and requires large amount of data. \n",
    "\n",
    "This becomes very costly in terms of time and compute resources. It even translates to environmental impact. Imagine if each time a research team, a student organization, or a company wanted to train a model, it did so from scratch. This would lead to huge, unnecessary global costs!\n",
    "\n",
    "\n",
    "The 🤗 Transformers library was created to solve this problem. Its goal is to provide a single API through which any Transformer model can be loaded, trained, and saved. \n",
    "\n",
    "The Hugging Face 🤗 Transformers library provides the functionality to create and use those shared models.\"\n",
    "\n",
    "For more details look at the official [Hugging Face course](https://huggingface.co/course/chapter1/1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75759e71",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9ac93857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "      Successfully uninstalled packaging-20.8\n",
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      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.3.1 is available.\n",
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      "You should consider upgrading via the '/usr/local/opt/python@3.9/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip3 install transformers\n",
    "!pip3 install ipywidgets --user\n",
    "!pip3 install torchtext #we will also use this later on for the data, so install it if you don't have it"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "550bc2f1",
   "metadata": {},
   "source": [
    "### Important:\n",
    "\n",
    "After this instalation --> don't forget to restart the kernel of the jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e15dc4c",
   "metadata": {},
   "source": [
    "## Examples of transformer architectures available\n",
    "\n",
    "As menioned above, the Transformer architecture was introduced in the paper [Attention Is All You Need](https://arxiv.org/abs/1706.03762), 2017, in which the focus of the original research was on translation tasks using encoder-decoder blocks. \n",
    "\n",
    "This was then followed by the introduction of several influential models, including encoder only models (e.g., BERT) and decoder only models (e.g., GPT), and there have been also a variety of encoder-decoder transformer based models (e.g., BART and T5). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d609d06",
   "metadata": {},
   "source": [
    "### Encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "259a6701",
   "metadata": {},
   "source": [
    "Encoder models use only the encoder block of a Transformer model. \n",
    "\n",
    "These models are often characterized as having “bi-directional” attention, and are often called auto-encoding models.\n",
    "\n",
    "Encoder models are best suited for tasks requiring an understanding of the full sentence, such as:\n",
    "   - sentence classification\n",
    "   - named entity recognition (word classification in general), \n",
    "   - extractive question answering\n",
    "   - sentence representation (contextual embeddings)\n",
    "\n",
    "    \n",
    "The pretraining of these models usually revolves around somehow corrupting a given sentence (for instance, by masking random words in it) and tasking the model with finding or reconstructing the initial sentence.\n",
    "\n",
    "\n",
    "There a variaty of encoder models available at Hugging Face. Some examples include:\n",
    "\n",
    "- [BERT](https://huggingface.co/docs/transformers/model_doc/bert)\n",
    "- [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)\n",
    "- [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)\n",
    "\n",
    "Hugging Face also provides recent vision and multi-modal encoder models (see at the end of this notebook):\n",
    "- [Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)\n",
    "- [LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)\n",
    "- [VISUALBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)\n",
    "- [CLIP](https://huggingface.co/docs/transformers/model_doc/clip)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdcbd0be",
   "metadata": {},
   "source": [
    "### Decoder\n",
    "\n",
    "Decoder models use only the decoder of a Transformer model. At each stage, for a given word, the self-attention layers can only access the words positioned before it in the sentence. These models are often called auto-regressive models.\n",
    "\n",
    "The pretraining of decoder models usually revolves around predicting the next word in the sentence.\n",
    "\n",
    "These models are best suited for tasks involving text generation.\n",
    "\n",
    "Representatives of this family of models include:\n",
    "\n",
    "Some examples of decoder models available in Hugging Face include:\n",
    "- [GPT](https://huggingface.co/docs/transformers/model_doc/openai-gpt)\n",
    "- [GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)\n",
    "- [Transformer XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5499b5e1",
   "metadata": {},
   "source": [
    "## Encoder-decoder models\n",
    "\n",
    "Encoder-decoder models (also called sequence-to-sequence models) use both parts of the Transformer architecture. \n",
    "\n",
    "Sequence-to-sequence models are best suited for tasks revolving around generating new sentences depending on a given input (conditional text generation), such as:\n",
    "- summarization\n",
    "- translation\n",
    "- or generative question answering\n",
    "\n",
    "Representatives of this family of models include:\n",
    "\n",
    "- [BART](https://huggingface.co/docs/transformers/model_doc/bart)\n",
    "- [Marian](https://huggingface.co/docs/transformers/model_doc/marian)\n",
    "- [T5](https://huggingface.co/docs/transformers/model_doc/t5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4413d4ed",
   "metadata": {},
   "source": [
    "# Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac0324d",
   "metadata": {},
   "source": [
    "The most basic object in the 🤗 Transformers library is the pipeline() function. It connects a model with its necessary preprocessing and postprocessing steps, allowing us to directly input any text and get an intelligible answer.\n",
    "\n",
    "See all details of pipelone here: https://huggingface.co/docs/transformers/v4.16.0/en/main_classes/pipelines#transformers.TranslationPipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f4809123",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1af0f58487864927a94884d311838610",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ce7ee2cfaa44ce9aada276688601b89",
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "2f3d6457506e4061aed2915805bf5bdd",
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       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "188f7217fbaa47c9954273e972ae0307",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9598050713539124},\n",
       " {'label': 'NEGATIVE', 'score': 0.9994558691978455}]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "import torch\n",
    "\n",
    "classifier = pipeline(\"sentiment-analysis\")\n",
    "classifier([\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"])\n",
    "\n",
    "# By default, this pipeline selects a particular pretrained model \n",
    "# that has been fine-tuned for sentiment analysis in English. \n",
    "\n",
    "# The model is downloaded and cached when you create the classifier object. \n",
    "# If you rerun the command, the cached model will be used instead \n",
    "# and there is no need to download the model again.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4241dc1",
   "metadata": {},
   "source": [
    "Try with your own text:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20093099",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This lecture is amazing!!!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9998607635498047}]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = input()\n",
    "classifier(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a904990",
   "metadata": {},
   "source": [
    "Besides sentiment analysis, some of the currently available pipelines are:\n",
    "\n",
    "- feature-extraction (get the vector representation of a text)\n",
    "- fill-mask\n",
    "- ner (named entity recognition)\n",
    "- question-answering\n",
    "- summarization\n",
    "- text-generation\n",
    "- translation\n",
    "- zero-shot-classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac938a04",
   "metadata": {},
   "source": [
    "Let’s now for instance check how to use a pipeline to generate some text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ea84dd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to gpt2 (https://huggingface.co/gpt2)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9661c3fbe8c49108c717031eae1d03d",
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "8281055b5680459cb28b3026a1fb0bcd",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "6385cf59722f41519a45e825a88bde39",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9fcb3d538324c8ebf8253c2ff0a02eb",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2db15d28b59a4851ae42caf6b39af6ab",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'In this course, we will teach you how to use transformer models and methods of programming in the Rust language.\\n\\nWhy is this course helpful?\\n\\nThis course will provide you with some valuable information to help you write the very best you can'}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator = pipeline(\"text-generation\")\n",
    "generator(\"In this course, we will teach you how to use transformer models and\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "165ccbe2",
   "metadata": {},
   "source": [
    "The previous examples used the default model for the task at hand, but you can also choose a particular model for any of the above tasks. \n",
    "\n",
    "For the specific case of the generation task, you can also control:\n",
    "- how many different sequences are generated with the argument num_return_sequences\n",
    "- the total length of the output text with the argument max_length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff80c99c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39dfc261ce8a496ba7220544def96432",
       "version_major": 2,
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      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "92a2c707c82348ddafa6de1cf8c49c51",
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     },
     "metadata": {},
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    },
    {
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       "model_id": "a4888e2ec288473eab1e217169aff3a6",
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    },
    {
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       "model_id": "f6ebb326567a4879ae2c78f83379f131",
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4ae767456544ecca2170f2b808a6e83",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A group of unicorns was seated in the class.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'A group of unicorns was seated in the class. The members of the students applauded, and the students cheered as though nothing was happening.\\n\\n'},\n",
       " {'generated_text': 'A group of unicorns was seated in the class. The group was a member who was standing in line in line, holding his head and a fist'}]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
    "generator(\n",
    "    input(),\n",
    "    max_length=30,\n",
    "    num_return_sequences=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a67ac2",
   "metadata": {},
   "source": [
    "Try it yourself with different pipelines available. \n",
    "\n",
    "To check how to use one in specific see the documentation: https://huggingface.co/docs/transformers/v4.16.0/en/main_classes/pipelines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "865dfd16",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to t5-base (https://huggingface.co/t5-base)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ec0e21527b04752ae2f73c9b65e0f3a",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "ee988fc12adc403483b98aa647c53a06",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0f952d7b7f849d5b3a2d8016fa72cf1",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3182b000cde147f7bc6175f5d2651358",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'translation_text': \"J'aime beaucoup ce cours\"}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trying the machine translation pipeline\n",
    "translator = pipeline(\"translation_en_to_fr\")\n",
    "translator(\"I am liking this course a lot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b728a5f",
   "metadata": {},
   "source": [
    "\n",
    "# Pipeline with a real dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a221827",
   "metadata": {},
   "source": [
    "Lets try this pipeline with sentences from a dataset, such as the IMDB dataset\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "505e0f0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading aclImdb_v1.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 84.1M/84.1M [00:12<00:00, 6.67MB/s]\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torchtext.legacy import data, datasets\n",
    "\n",
    "TEXT = data.Field()\n",
    "LABEL = data.LabelField(dtype = torch.float)\n",
    "\n",
    "_, test_data = datasets.IMDB.splits(TEXT, LABEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a31d431e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ideally we should run all the senteces to the model and see it's performance on test (or validation set).\n",
    "#But lets just pick first some number of sentences to run faster (or you could use GPU): such as first 50 sentences and last 50 sentences\n",
    "labels=list(test_data.label)[:50]+list(test_data.label)[-50:] \n",
    "sents_with_tokens=list(test_data.text)[:50]+list(test_data.text)[-50:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c0aa957",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to have the corresponding sentences (and not tokens) \n",
    "# so that the pipeline tokenizes the text by itself according to the model tokenization\n",
    "# we can give a max nº of words to be faster (e.g., in terms of performing self attention)\n",
    "MAX_LEN=500\n",
    "sents=[\" \".join(tokens)[:MAX_LEN] for tokens in sents_with_tokens] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3c146f51",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)\n",
      "100%|██████████| 100/100 [00:14<00:00,  6.95it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "classifier = pipeline(\"sentiment-analysis\")\n",
    "preds=[]\n",
    "for sent in tqdm(sents):\n",
    "    preds.append(classifier(sent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "074f9abc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc 0.78\n"
     ]
    }
   ],
   "source": [
    "count_correct=0\n",
    "for i in range(len(preds)):\n",
    "    if labels[i] in preds[i][0].get(\"label\").lower():\n",
    "        count_correct +=1\n",
    "print(\"acc\", count_correct/len(labels))              "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "218eb114",
   "metadata": {},
   "source": [
    "# Dive deep into Hugging Face\n",
    "\n",
    "\n",
    "Besides the currently available pipelines, we can dive deep and use any model available in HG and apply it to any given task. \n",
    "\n",
    "We’ll dive into the model and configuration classes, and show you how to load a model and how it processes numerical inputs to output predictions. \n",
    "\n",
    "\n",
    "## Behind pipeline \n",
    "Lets begin with an end-to-end example where we use a model and a tokenizer together to replicate the pipeline() function of sentiment analysis introduced before.\n",
    "\n",
    "There are three main steps involved when you pass some text to a pipeline:\n",
    "1. <b> Preprocessing with a tokenizer:</b> The text is preprocessed into a format the model can understand.\n",
    "2. <b> Going through the model:</b> The preprocessed inputs are passed to the model.\n",
    "3. <b> Postprocessing the output\n",
    ": </b> The predictions of the model are post-processed, so you can make sense of them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "585fd991",
   "metadata": {},
   "source": [
    "<img src=\"HG_img\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "673759c2",
   "metadata": {},
   "source": [
    "### 1. Preprocessing with a tokenizer:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e504c406",
   "metadata": {},
   "source": [
    "Like other neural networks, Transformer models can’t process raw text directly.\n",
    "\n",
    "So the first step of our pipeline is to convert the text inputs into numbers that the model can make sense of. \n",
    "\n",
    "To do this we use a <b> tokenizer</b>. They serve the purpose to translate text into data that can be processed by the model.\n",
    "\n",
    "Tokenizer will be responsible for:\n",
    "\n",
    "- Splitting the input into words, subwords, or symbols (like punctuation) that are called tokens\n",
    "- Mapping each token to an integer\n",
    "- Adding additional inputs that may be useful to the model (e.g., attention mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84fad112",
   "metadata": {},
   "source": [
    "#### Define tokenizer "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd978608",
   "metadata": {},
   "source": [
    "The tokenizer and the model should always be from the same checkpoint. Therefore, we need to define the tokenizer with the checkpoint name of the corresponding model. \n",
    "\n",
    "\n",
    "To do this, we use the <b> AutoTokenizer class </b> and its <b> from_pretrained() </b> method with the checkpoint name of the corresponding model inside it.\n",
    "\n",
    "This will automatically fetch the data associated with the model’s tokenizer (as the vocabulary) and cache it (so it’s only downloaded the first time you run the code below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c67a0e36",
   "metadata": {},
   "outputs": [
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     "data": {
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       "model_id": "75a185b140a84136b07d032cb7314fd6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/455k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c704020",
   "metadata": {},
   "source": [
    "Alternatively to the wrapper AutoModel class, you can use directly the class of the corresponding tokenizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "17a28b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import BertTokenizer\n",
    "\n",
    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d2f00f",
   "metadata": {},
   "source": [
    "You can use either one of the two. But note that the AutoTokenizer produces checkpoint-agnostic code\n",
    "\n",
    "- The AutoTokenizer will works for other checkpoints besides BERT (e.g.: distilbert-base-uncased-finetuned-sst-2-english), \n",
    "- Whereas the BertTokenizer just works for checkpoints related to BertTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "40cbf630",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
      "The tokenizer class you load from this checkpoint is 'DistilBertTokenizer'. \n",
      "The class this function is called from is 'BertTokenizer'.\n"
     ]
    }
   ],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09f7d03c",
   "metadata": {},
   "source": [
    "To mimic our sentiment analysis pipeline, let's actually use the DistilBERT model and thus use the corresponding DistilBERT tokenizer (with checkpoint \"bert-base-uncased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1ad6f20e",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d33a5ee",
   "metadata": {},
   "source": [
    "#### Encode the sentences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35e72689",
   "metadata": {},
   "source": [
    "Once we have the tokenizer, we can directly pass our sentences to it.\n",
    "\n",
    "Translating text to numbers is known as encoding. Encoding is done in a two-step process: \n",
    "- the tokenization\n",
    "- followed by the conversion to input IDs.\n",
    "\n",
    "---------\n",
    "To know more about each of the 2 steps:\n",
    "The first step is to split the text into words (or parts of words, punctuation symbols, etc.), usually called tokens. There are multiple rules that can govern that process, which is why we need to instantiate the tokenizer using the name of the model, to make sure we use the same rules that were used when the model was pretrained.\n",
    "\n",
    "The second step is to convert those tokens into numbers, so we can build a tensor out of them and feed them to the model. To do this, the tokenizer has a vocabulary, which is the part we download when we instantiate it with the from_pretrained() method. Again, we need to use the same vocabulary used when the model was pretrained.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a446de4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input_ids': tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
      "          2607,  2026,  2878,  2166,  1012,   102],\n",
      "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0],\n",
      "        [  101,  1045,  2424,  2023,  2200,  3733,   999,   102,     0,     0,\n",
      "             0,     0,     0,     0,     0,     0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
      "        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]])}\n"
     ]
    }
   ],
   "source": [
    "raw_inputs = [\n",
    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
    "    \"I hate this so much!\",\n",
    "    \"I find this very easy!\"\n",
    "]\n",
    "inputs = tokenizer(\n",
    "    raw_inputs,\n",
    "    padding=True,        #since the sentences might not have the same size, don't forget to padding. \n",
    "    return_tensors=\"pt\"  #to return with pytorch tensors\n",
    ")\n",
    "\n",
    "# - Feeding your raw_sentences to the tokenizer will give the corresponding input_id\n",
    "# - As well as the attention_mask, usufel so that there is no self attention between padding words. \n",
    "\n",
    "# - In case of BERT it will also produce \"token_type_ids\" \n",
    "#   useful when the model receives sentence A and B together (e.g., for text similarity tasks) \n",
    "      # (0 corresponds to a sentence A token,\n",
    "      # 1 corresponds to a sentence B token.)\n",
    "\n",
    "print(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9c13ee5",
   "metadata": {},
   "source": [
    "Note that each word can correspond to more than one id, since DistilBERT uses subword tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "647e7dc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', \"'\", 've', 'been', 'waiting', 'for', 'a', 'hugging', '##face', 'course', 'my', 'whole', 'life', '.']\n"
     ]
    }
   ],
   "source": [
    "tokens = tokenizer.tokenize(raw_inputs[0])\n",
    "print(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30fe8a56",
   "metadata": {},
   "source": [
    "Besides encoding the corresponding input text, we can also decode, going the other way around: from vocabulary indices to get the corresponding string.\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e3a6123",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokens id: [101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
      "\n",
      "Decoded inputs: [CLS] i've been waiting for a huggingface course my whole life. [SEP]\n",
      "\n",
      "Without special tokens: i've been waiting for a huggingface course my whole life.\n"
     ]
    }
   ],
   "source": [
    "tokens_id = tokenizer.encode(raw_inputs[0])\n",
    "print(\"Tokens id:\", tokens_id)\n",
    "\n",
    "decode_inputs = tokenizer.decode(tokens_id)\n",
    "print(\"\\nDecoded inputs:\", decode_inputs)\n",
    "\n",
    "decode_inputs = tokenizer.decode(tokens_id, skip_special_tokens=True)\n",
    "print(\"\\nWithout special tokens:\", decode_inputs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db31d329",
   "metadata": {},
   "source": [
    "# Going through the model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5e83f4",
   "metadata": {},
   "source": [
    "#### Define model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddebf7dc",
   "metadata": {},
   "source": [
    "We can download our model that is already trained in the same way that we did with our tokenizer:\n",
    "\n",
    "- You can use AutoModel class which also has from_pretrained() method or use directly\n",
    "- You could replace AutoModel directly with the corresponding model class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f446632f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased-finetuned-sst-2-english were not used when initializing DistilBertModel: ['pre_classifier.bias', 'pre_classifier.weight', 'classifier.weight', 'classifier.bias']\n",
      "- This IS expected if you are initializing DistilBertModel 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 DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModel\n",
    "\n",
    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
    "model = AutoModel.from_pretrained(checkpoint)\n",
    "\n",
    "# we have downloaded the same checkpoint we used in our pipeline before\n",
    "# threfore it should actually have been cached already"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "10afd11c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased-finetuned-sst-2-english were not used when initializing DistilBertModel: ['pre_classifier.bias', 'pre_classifier.weight', 'classifier.weight', 'classifier.bias']\n",
      "- This IS expected if you are initializing DistilBertModel 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 DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "from transformers import DistilBertModel\n",
    "\n",
    "model = DistilBertModel.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0d8fb32",
   "metadata": {},
   "source": [
    "It's also possible to load a model from scratch without trained weights:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc5a1d0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import DistilBertConfig\n",
    "\n",
    "config = DistilBertConfig()\n",
    "model_without_pretrained = DistilBertModel(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "335bd47e",
   "metadata": {},
   "source": [
    "#### Feed the inputs\n",
    "\n",
    "This model is now initialized with all the weights of the checkpoint. It can be used directly for inference on the tasks it was trained on, and it can also be fine-tuned on a new task. By training with pretrained weights rather than from scratch, we can quickly achieve good results.\n",
    "\n",
    "We can now feed the inputs we preprocessed before (with the tokenizer) to our model:\n",
    "    \n",
    "    {'input_ids': \n",
    "    \n",
    "        tensor([\n",
    "            [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
    "              2607,  2026,  2878,  2166,  1012,   102],\n",
    "            [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,\n",
    "                 0,     0,     0,     0,     0,     0],\n",
    "            [  101,  1045,  2424,  2023,  2200,  3733,   999,   102,     0,     0,\n",
    "                 0,     0,     0,     0,     0,     0]]),\n",
    "             \n",
    "     'attention_mask': \n",
    "     \n",
    "         tensor(\n",
    "            [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
    "            [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "            [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]])}\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ffc01e2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BaseModelOutput(last_hidden_state=tensor([[[-0.1798,  0.2333,  0.6321,  ..., -0.3017,  0.5008,  0.1481],\n",
      "         [ 0.2758,  0.6497,  0.3200,  ..., -0.0760,  0.5136,  0.1329],\n",
      "         [ 0.9046,  0.0985,  0.2950,  ...,  0.3352, -0.1407, -0.6464],\n",
      "         ...,\n",
      "         [ 0.1466,  0.5661,  0.3235,  ..., -0.3376,  0.5100, -0.0561],\n",
      "         [ 0.7500,  0.0487,  0.1738,  ...,  0.4684,  0.0030, -0.6084],\n",
      "         [ 0.0519,  0.3729,  0.5223,  ...,  0.3584,  0.6500, -0.3883]],\n",
      "\n",
      "        [[-0.2937,  0.7283, -0.1497,  ..., -0.1187, -1.0227, -0.0422],\n",
      "         [-0.2206,  0.9384, -0.0951,  ..., -0.3643, -0.6605,  0.2407],\n",
      "         [-0.1536,  0.8987, -0.0728,  ..., -0.2189, -0.8528,  0.0710],\n",
      "         ...,\n",
      "         [-0.3017,  0.9002, -0.0200,  ..., -0.1082, -0.8412, -0.0861],\n",
      "         [-0.3338,  0.9674, -0.0729,  ..., -0.1952, -0.8181, -0.0634],\n",
      "         [-0.3454,  0.8824, -0.0426,  ..., -0.0993, -0.8329, -0.1065]],\n",
      "\n",
      "        [[-0.3841, -0.1072,  0.3243,  ...,  0.2156,  0.2593,  0.0866],\n",
      "         [ 0.3034,  0.3026,  0.2005,  ...,  0.4373,  0.5169, -0.0845],\n",
      "         [-0.4797,  0.1210,  0.5126,  ...,  0.1014,  0.4086,  0.2696],\n",
      "         ...,\n",
      "         [-0.3598, -0.0655,  0.4618,  ...,  0.3586,  0.2877,  0.0428],\n",
      "         [-0.3949, -0.0937,  0.4477,  ...,  0.3372,  0.2124,  0.0469],\n",
      "         [-0.4066, -0.0487,  0.4370,  ...,  0.3481,  0.2206,  0.1476]]],\n",
      "       grad_fn=<NativeLayerNormBackward0>), hidden_states=None, attentions=None)\n"
     ]
    }
   ],
   "source": [
    "outputs = model(**inputs) #you give further arguments: output_attentions=True and output_hidden_states:True\n",
    "print(outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb6905f",
   "metadata": {},
   "source": [
    "Note that the outputs of 🤗 Transformers models behave like named tuples or dictionaries. \n",
    "\n",
    "You can access the elements by:\n",
    "- attributes (like we did) \n",
    "- or by key (outputs[\"last_hidden_state\"])\n",
    "- or even by index if you know exactly where the thing you are looking for is (outputs[0]).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7dc44cc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last hidden state tensor([[[-0.1798,  0.2333,  0.6321,  ..., -0.3017,  0.5008,  0.1481],\n",
      "         [ 0.2758,  0.6497,  0.3200,  ..., -0.0760,  0.5136,  0.1329],\n",
      "         [ 0.9046,  0.0985,  0.2950,  ...,  0.3352, -0.1407, -0.6464],\n",
      "         ...,\n",
      "         [ 0.1466,  0.5661,  0.3235,  ..., -0.3376,  0.5100, -0.0561],\n",
      "         [ 0.7500,  0.0487,  0.1738,  ...,  0.4684,  0.0030, -0.6084],\n",
      "         [ 0.0519,  0.3729,  0.5223,  ...,  0.3584,  0.6500, -0.3883]],\n",
      "\n",
      "        [[-0.2937,  0.7283, -0.1497,  ..., -0.1187, -1.0227, -0.0422],\n",
      "         [-0.2206,  0.9384, -0.0951,  ..., -0.3643, -0.6605,  0.2407],\n",
      "         [-0.1536,  0.8987, -0.0728,  ..., -0.2189, -0.8528,  0.0710],\n",
      "         ...,\n",
      "         [-0.3017,  0.9002, -0.0200,  ..., -0.1082, -0.8412, -0.0861],\n",
      "         [-0.3338,  0.9674, -0.0729,  ..., -0.1952, -0.8181, -0.0634],\n",
      "         [-0.3454,  0.8824, -0.0426,  ..., -0.0993, -0.8329, -0.1065]],\n",
      "\n",
      "        [[-0.3841, -0.1072,  0.3243,  ...,  0.2156,  0.2593,  0.0866],\n",
      "         [ 0.3034,  0.3026,  0.2005,  ...,  0.4373,  0.5169, -0.0845],\n",
      "         [-0.4797,  0.1210,  0.5126,  ...,  0.1014,  0.4086,  0.2696],\n",
      "         ...,\n",
      "         [-0.3598, -0.0655,  0.4618,  ...,  0.3586,  0.2877,  0.0428],\n",
      "         [-0.3949, -0.0937,  0.4477,  ...,  0.3372,  0.2124,  0.0469],\n",
      "         [-0.4066, -0.0487,  0.4370,  ...,  0.3481,  0.2206,  0.1476]]],\n",
      "       grad_fn=<NativeLayerNormBackward0>)\n"
     ]
    }
   ],
   "source": [
    "print(\"last hidden state\", outputs.last_hidden_state)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c240be2",
   "metadata": {},
   "source": [
    "Note also that given the input to the model, it will output a high-dimensional vector representing the contextual understanding of that input by the Transformer model.\n",
    "\n",
    "The vector output by the Transformer module generally has three dimensions:\n",
    "\n",
    "- Batch size: The number of sequences processed at a time (2 in our example).\n",
    "- Sequence length: The length of the numerical representation of the sequence (16 in our example).\n",
    "- Hidden size: The vector dimension of each model input.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "68774b73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size torch.Size([3, 16, 768])\n"
     ]
    }
   ],
   "source": [
    "print(\"size\", outputs.last_hidden_state.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eb55929",
   "metadata": {},
   "source": [
    "## Postprocessing the output\n",
    " \n",
    "While the hidden states that were outputed can be useful on their own, for instance: \n",
    "\n",
    "- You can have a <b> contextual representation </b> for the entire sentence deriving a fixed sized vector by averaging the outputs. Instead of having static embeddings like in part 1)\n",
    "\n",
    "- You can also give this hidden states of the encoder to an auto-regressive decoder that is equipped with cross-attention layers to perform condional text generation (such as MT). \n",
    "\n",
    "\n",
    "In this case for sentiment analysis we are more interesting in using the corresponding classification head. \n",
    "\n",
    "For our example, we will use the DistilBERT model with a sequence classification head (to be able to classify the sentences as positive or negative). So, we won’t actually use the DistilBertModel class but the DistilBertForSequenceClassification (or AutoModelForSequenceClassification class). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8fe22dcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import DistilBertForSequenceClassification, DistilBertTokenizer\n",
    "\n",
    "tokenizer = DistilBertTokenizer.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\")\n",
    "raw_inputs = [\n",
    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
    "    \"I hate this so much!\",\n",
    "    \"I find this very easy!\"\n",
    "]\n",
    "inputs = tokenizer(\n",
    "    raw_inputs,\n",
    "    padding=True,        #since the sentences might not have the same size, don't forget to padding. \n",
    "    return_tensors=\"pt\"  #to return with pytorch tensors\n",
    ")\n",
    "\n",
    "model = DistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\")\n",
    "outputs = model(**inputs)\n",
    "logits = outputs.logits.detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cf4f2a94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.5607,  1.6123],\n",
       "        [ 4.1692, -3.3464],\n",
       "        [-3.0649,  3.0434]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be44f7f8",
   "metadata": {},
   "source": [
    "Those are not probabilities but logits, the raw, unnormalized scores outputted by the last layer of the model. \n",
    "\n",
    "To be converted to probabilities, they need to go through a SoftMax layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "560642cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[4.0195e-02, 9.5981e-01],\n",
      "        [9.9946e-01, 5.4419e-04],\n",
      "        [2.2193e-03, 9.9778e-01]])\n"
     ]
    }
   ],
   "source": [
    "predictions = torch.nn.functional.softmax(logits, dim=-1)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "08542ca7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I've been waiting for a HuggingFace course my whole life. POSITIVE\n",
      "I hate this so much! NEGATIVE\n",
      "I find this very easy! POSITIVE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_inputs\n",
    "for i in range(len(predictions)):\n",
    "    if predictions[i][1].item()>=0.5:\n",
    "        print(raw_inputs[i],model.config.id2label[1])\n",
    "    else:\n",
    "        print(raw_inputs[i],model.config.id2label[0])\n",
    "\n",
    "model.config.id2label"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd7c0c1e",
   "metadata": {},
   "source": [
    "We have successfully reproduced the three steps of the pipeline: preprocessing with tokenizers, passing the inputs through the model, and postprocessing! 🤗\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6517b868",
   "metadata": {},
   "source": [
    "# Fine-tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3586063b",
   "metadata": {},
   "source": [
    "To fine-tuning a transformer model to our specific dataset (using corresponding labels), you can just use the core of the training loop that we've been using since the Pytorch pratical:\n",
    "\n",
    "1. Put the model and the corresponding data (batches) into the corresponding device (\"cpu\" or \"gpu\")\n",
    "2. When iterate over the corresponding batches, each update to the model involves the same general pattern comprised of:\n",
    "    - Clearing the last error gradient. ( stop gradient accumulation )\n",
    "    - A forward pass of the input through the model\n",
    "    - Calculating the loss for the model output.\n",
    "    - Backpropagating the error through the model.\n",
    "    - Update the model in an effort to reduce loss.\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92669e3a",
   "metadata": {},
   "source": [
    "There's just a slightly difference: transformer models are usually trained with a learning rate scheduler.\n",
    "\n",
    "For instance, a default learning rate scheduler is a linear decay from the maximum value (5e-5) to 0. To properly define it, we need to know the number of training steps we will take, which is the number of epochs we want to run multiplied by the number of training batches (which is the length of our training dataloader). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b92a15f7",
   "metadata": {},
   "source": [
    "\n",
    "    from transformers import get_scheduler\n",
    "\n",
    "    num_epochs = 3\n",
    "    num_training_steps = num_epochs * len(train_dataloader)\n",
    "    lr_scheduler = get_scheduler(\n",
    "        \"linear\",\n",
    "        optimizer=optimizer,\n",
    "        num_warmup_steps=0,\n",
    "        num_training_steps=num_training_steps,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abcf3024",
   "metadata": {},
   "source": [
    "Summing up, to fine-tune or training or model from scratch we could simply use the core of the training loop that we've been using:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd21efe4",
   "metadata": {},
   "source": [
    "    model.train()\n",
    "    for epoch in range(num_epochs):\n",
    "        for batch in train_dataloader:\n",
    "        \n",
    "            outputs = model(**batch)\n",
    "            \n",
    "            # you can use a given pytorch loss (torch.nn.CrossEntropyLoss)\n",
    "            # or you can also use outputs.loss if you give \"labels\" directly to your model\n",
    "            loss = criterion(outputs) \n",
    "            \n",
    "            loss.backward()\n",
    "            \n",
    "            optimizer.step()\n",
    "            \n",
    "            lr_scheduler.step()\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "           "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889cdcd3",
   "metadata": {},
   "source": [
    "Alternative, in case you would prefer to avoid defining manually this training loop, Hugging Face Transformers also provides a [Trainer API](https://huggingface.co/course/chapter3/3?fw=pt) that does that for you."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73427319",
   "metadata": {},
   "source": [
    "# Vision and Multi-modal Transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4b0f3a7",
   "metadata": {},
   "source": [
    "Transformer architecture are currently achieving state-of-the-art results not just on a variety of language processing, but also on computer vision.\n",
    "\n",
    "Hugging Face contains the very recent [Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit) model from Google. \n",
    "\n",
    "Lets try it out for image classification with our own images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2d104bca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape torch.Size([480, 640, 3])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torchvision.transforms.functional as TF\n",
    "from PIL import Image\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Choose your image here\n",
    "url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n",
    "image = Image.open(requests.get(url, stream=True).raw)\n",
    "\n",
    "image_tensor = TF.to_tensor(image)\n",
    "image_tensor = image_tensor.permute(\n",
    "    [1,2,0]) # make the color dimention the last one.\n",
    "print(\"shape\", image_tensor.shape)\n",
    "plt.imshow(image_tensor)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38b1f225",
   "metadata": {},
   "source": [
    "We can predict the class of the chosen image with the same logic that we saw before:\n",
    "   1. define \"tokenizer\" \n",
    "       - Actually a feature extractor since we are processing images\n",
    "   2. define the model\n",
    "   3. postprocessing the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f73c56f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "101b3ef70975461c92934aae0244a66e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/160 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc8ef7e4336e42a79bba3e03293b1675",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class: Egyptian cat\n"
     ]
    }
   ],
   "source": [
    "from transformers import ViTFeatureExtractor, ViTForImageClassification\n",
    "\n",
    "feature_extractor = ViTFeatureExtractor.from_pretrained(\"google/vit-base-patch16-224\")\n",
    "model = ViTForImageClassification.from_pretrained(\"google/vit-base-patch16-224\")\n",
    "\n",
    "inputs = feature_extractor(images=image, return_tensors=\"pt\")\n",
    "outputs = model(**inputs)\n",
    "logits = outputs.logits\n",
    "# model predicts one of the 1000 ImageNet classes\n",
    "predicted_class_idx = logits.argmax(-1).item()\n",
    "print(\"Predicted class:\", model.config.id2label[predicted_class_idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a07f2fd",
   "metadata": {},
   "source": [
    "It correctly predicted \"Egyptian cat\". You can also try with your own images.\n",
    "\n",
    "In this case, we wanted to use ViT for classification hence ViTForImageClassification, but if we wanted the corresponding hidden states we could have use instead ViTModel, as we saw before with the DistilBERT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cf3611d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "64025b7975a84b7bad17da268ab22d50",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3efe1adb3915492981d2145745b4f81b",
       "version_major": 2,
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "from transformers import ViTModel\n",
    "model = ViTModel.from_pretrained(\"google/vit-base-patch16-224-in21k\")\n",
    "outputs = model(**inputs) \n",
    "last_hidden_states = outputs.last_hidden_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b108f8dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last_hidden_states tensor([[[ 0.1559,  0.0914,  0.1518,  ..., -0.3180, -0.0859, -0.0903],\n",
      "         [-0.2254,  0.0864,  0.4752,  ..., -0.1781,  0.1726,  0.1334],\n",
      "         [ 0.0444,  0.0677,  0.4199,  ..., -0.2576,  0.1191,  0.0130],\n",
      "         ...,\n",
      "         [-0.0153, -0.0396,  0.1684,  ..., -0.1672,  0.1869,  0.1025],\n",
      "         [ 0.0249, -0.0382,  0.2046,  ...,  0.0517,  0.1489,  0.1320],\n",
      "         [-0.1748, -0.0254,  0.2523,  ..., -0.1474,  0.1627,  0.1325]]],\n",
      "       grad_fn=<NativeLayerNormBackward0>)\n"
     ]
    }
   ],
   "source": [
    "print(\"last_hidden_states\", last_hidden_states) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1fb5cab",
   "metadata": {},
   "source": [
    "Hugging Face has also been incorporating multi-modal encoders (vision and text), including VisualBERT, LXMERT and even the CLIP model from openai.\n",
    "\n",
    "With CLIP we can for instance predict image-text similarity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a239ca36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af91cdc333934fba9134b79818c6ea28",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8150b25db6b74b64a71577ee08e76b41",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import CLIPProcessor, CLIPModel\n",
    "\n",
    "model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
    "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
    "\n",
    "inputs = processor(text=[\"a photo of a cat\", \"a photo of a dog\"], images=image, return_tensors=\"pt\", padding=True)\n",
    "\n",
    "outputs = model(**inputs)\n",
    "logits_per_image = outputs.logits_per_image  # this is the image-text similarity score\n",
    "probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities\n",
    "\n",
    "print(\"probs\",probs) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4de9d1c",
   "metadata": {},
   "source": [
    "It correctly predicted that the image of the cat has more similarity with the sentence: \"a photo of a cat\" than \"a photo of a dog\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d87661",
   "metadata": {},
   "source": [
    "______\n",
    "See the complete course to better understand: [Hugging Face course](https://huggingface.co/course/chapter1/1?fw=pt).\n",
    "\n",
    "You can attempt to answer their quizzes to make sure you fully understand it."
   ]
  }
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