{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JEZdTSV3V7cQ",
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# Natural Language Processing Tutorial 3 - Introduction to Transformers with Huggingface \n",
    "\n",
    "![](data:image/jpeg;base64,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) \n",
    "\n",
    "Tommaso Green, Francesco Cazzaro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M4qoFZQnXrTE",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "*   What you will learn today\n",
    "  * Huggingface Hub: the ecosystem of Huggingface\n",
    "  * Huggingface 4 main libraries:\n",
    "      * [transformers](https://huggingface.co/docs/transformers)\n",
    "      * [tokenizers](https://huggingface.co/docs/tokenizers) \n",
    "      * [datasets](https://huggingface.co/docs/datasets)\n",
    "      * [evaluate](https://huggingface.co/docs/evaluate)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QWFmoUawZqrx",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Huggingface Hub\n",
    "\n",
    "*   The first thing to learn is the structure of Huggingface Hub\n",
    "*   Huggingface Hub is a place where you can find plenty of [models](https://huggingface.co/models) and [datasets](https://huggingface.co/datasets)\n",
    "* BERT is the most downloaded model and is available [here](https://huggingface.co/bert-base-uncased)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DlMj3yAnV7cZ",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Models and tokenizers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UwHuXOb-Y38Z",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "First we need to install the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c0Ax-iTbZR03",
    "outputId": "f0a2d2ac-99c0-472f-945c-2337e2a0ecf3",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.28.1)\n",
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      "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
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      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.12.0)\n",
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      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (3.12.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (4.5.0)\n",
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      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
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      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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      "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from evaluate) (23.1)\n",
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      "Requirement already satisfied: huggingface-hub>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.14.1)\n",
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     ]
    }
   ],
   "source": [
    "!pip install transformers\n",
    "!pip install datasets\n",
    "!pip install evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KRtN8l12ZjhR",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### The \"Auto\" Classes of Hugginface\n",
    "\n",
    "*  The first thing that we need to do is to download a pretrained model and its tokenizer\n",
    "* We need to use the classes\n",
    "  * ```AutoModel```\n",
    "  *  ```AutoTokenizer``` \n",
    "* We can load both using the ```from_pretrained``` function\n",
    "* The opposite function is the ```save_pretrained``` function, which can be used to save a model to disk\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "999o5U_fbHJu",
    "outputId": "97efa57e-9979-4b29-ef4b-3c3d12e1987b",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0c7d624c9684f58b6943edea35e000f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/440M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias']\n",
      "- This IS expected if you are initializing BertModel 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 BertModel 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 AutoTokenizer, AutoModel\n",
    " \n",
    "model_name = \"bert-base-uncased\"\n",
    "\n",
    "model = AutoModel.from_pretrained(\"bert-base-uncased\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uiGnf6N-V7cZ",
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Encoder-only models\n",
    "\n",
    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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F9-3lzQ7bmle",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Tokenizers outputs\n",
    "\n",
    "*  Let's focus on the tokenizer for now\n",
    "*  The tokenizer applies the following steps:\n",
    "  1. Preprocesses the text and tokenizes it in subwords\n",
    "  2. Associates to every subword an ```input_id``` which is used to fetch its embedding in the embedding layer (or layer 0)\n",
    "  3. Adds ```attention_mask``` and ```token_type_ids```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_Tf8Rlzobj-e",
    "outputId": "66199993-fe33-4971-fcb9-9278f3964782",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [101, 1045, 2293, 19204, 3989, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer(\"I love tokenization\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KmL3vXm2g6bB",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### The ```input_ids``` \n",
    "\n",
    "*   After tokenizing into subwords, each subword is associated to a ```input_id``` which tells the model which embedding to get for that subword\n",
    "* You can think of it as a row index in the embedding matrix\n",
    "    * love ➡️ 2293 ➡️ get embedding in row 2293\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jrnlEZT1V7cb",
    "outputId": "31c5632a-1a50-4445-ae80-81b6183b7f5c",
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[CLS]', 'i', 'love', 'token', '##ization', '[SEP]']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.tokenize(\"I love tokenization\", add_special_tokens=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [101, 1045, 2293, 19204, 3989, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer(\"I love tokenization\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i544ARjmif9B",
    "outputId": "ac3a040b-2a39-4c77-8680-dc33e171fc92",
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[CLS]', 'i', 'love', 'token', '##ization', '[SEP]']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = tokenizer(\"I love tokenization\")\n",
    "tokenizer.convert_ids_to_tokens(output[\"input_ids\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "vLEsRpntc55G",
    "outputId": "394d2d57-93b3-45eb-e1a2-3e1efb4f7ea8",
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'[CLS] i love tokenization [SEP]'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = tokenizer(\"I love tokenization\")\n",
    "tokenizer.decode(output[\"input_ids\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KmL3vXm2g6bB",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### The ```attention_mask``` \n",
    "\n",
    "*   Suppose that we have 2 sentences of different lengths\n",
    "*   If the sentences are in the same batch, the shortest one needs to be padded: we need to append [PAD] tokens to the shortest sentence so that they have the same length\n",
    "* However, we don't want the self-attention to operate over the [PAD] tokens\n",
    "* tokenizers handles all of this for us\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "xwCpk9ozg3RP",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "sentences = [\"I love tokenization\", \"I really like the city of Padua\"]\n",
    "output = tokenizer(sentences, padding=True, return_tensors='pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "P-bCoVSSg5py",
    "outputId": "db66830a-7fe8-4143-e443-5f4bf3fd29ca",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[  101,  1045,  2293, 19204,  3989,   102,     0,     0,     0],\n",
      "        [  101,  1045,  2428,  2066,  1996,  2103,  1997, 24941,   102]])\n",
      "tensor([[1, 1, 1, 1, 1, 1, 0, 0, 0],\n",
      "        [1, 1, 1, 1, 1, 1, 1, 1, 1]])\n"
     ]
    }
   ],
   "source": [
    "print(output[\"input_ids\"])\n",
    "print(output[\"attention_mask\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "__ZJX_4xfhou",
    "outputId": "a09ace76-ff7d-471d-a3b9-01f35fb3f14b",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[CLS]', 'i', 'love', 'token', '##ization', '[SEP]', '[PAD]', '[PAD]', '[PAD]']\n",
      "['[CLS]', 'i', 'really', 'like', 'the', 'city', 'of', 'padua', '[SEP]']\n"
     ]
    }
   ],
   "source": [
    "print(tokenizer.convert_ids_to_tokens(output[\"input_ids\"][0]))\n",
    "print(tokenizer.convert_ids_to_tokens(output[\"input_ids\"][1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UkE6WeEZmXMC",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### The ```token_type_ids``` \n",
    "\n",
    "*  Recall that the input embeddings to a transformers are the result of a sum of three elements:\n",
    "  * Token embeddings: the embeddings that are extracted from the embedding matrix using ```input_ids```\n",
    "  * Positional embeddings: this are sinusoidal or learned and give the transformer the position information\n",
    "  * Segment embeddings: when we are doing sentence-pair tasks, i.e. the input consists of $ [CLS] \\; seq_A \\; [SEP] \\; seq_B \\; [SEP]$, we may want to add to each embedding the information on the originating sentence \n",
    "![](data:image/png;base64,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)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fjI5erxQqZ1e",
    "outputId": "d29b3ee7-b0c4-499e-d8bf-34c2cd77d17b",
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [101, 1996, 3103, 2003, 9716, 2651, 102, 2651, 2009, 1005, 1055, 16373, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = tokenizer(\"The sun is shining today\", \"Today it's rainy\") # sentence-pair tokenization\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "vEdVRbYJWZY2",
    "outputId": "0f38953e-310d-46ad-ad91-5b41abbe4081",
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "\"[CLS] the sun is shining today [SEP] today it's rainy [SEP]\""
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(output[\"input_ids\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DdMgarr_V7cd",
    "outputId": "5bb55012-7614-4532-da96-3cb23979eb37",
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[CLS] the sun is shining today [SEP]', \"[CLS] today it's rainy [SEP]\"]\n",
      "[CLS] the sun is shining today [SEP] today it's rainy [SEP]\n"
     ]
    }
   ],
   "source": [
    "# Notice the difference\n",
    " \n",
    "output_single_sent = tokenizer([\"The sun is shining today\", \"Today it's rainy\"]) # single-sentence tasks\n",
    "output_sent_pair = tokenizer(\"The sun is shining today\", \"Today it's rainy\") # sentence-pair tasks\n",
    "\n",
    "print(tokenizer.batch_decode(output_single_sent[\"input_ids\"]))\n",
    "print(tokenizer.decode(output_sent_pair[\"input_ids\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a71idp-3V7cd",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### The transformer model in Huggingface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iltf3f2iWqNy",
    "outputId": "503b81d9-8deb-4ae8-be7e-e45def1d6727"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertModel(\n",
       "  (embeddings): BertEmbeddings(\n",
       "    (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "    (position_embeddings): Embedding(512, 768)\n",
       "    (token_type_embeddings): Embedding(2, 768)\n",
       "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (encoder): BertEncoder(\n",
       "    (layer): ModuleList(\n",
       "      (0-11): 12 x BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (pooler): BertPooler(\n",
       "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "    (activation): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FFj_26-gV7cd",
    "outputId": "640c3a38-7723-4801-bcef-0726109fe33c",
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertLayer(\n",
       "  (attention): BertAttention(\n",
       "    (self): BertSelfAttention(\n",
       "      (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (output): BertSelfOutput(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "  )\n",
       "  (intermediate): BertIntermediate(\n",
       "    (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "    (intermediate_act_fn): GELUActivation()\n",
       "  )\n",
       "  (output): BertOutput(\n",
       "    (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.encoder.layer[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wCw67I1zV7cd",
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Feeding a batch to a transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "THYqpjgpZ3o7",
    "outputId": "6c1bf9e6-c848-49cb-b488-3aabc61fe36e",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[  101,  2478, 19081,  2003,  3243,  3722,   102,     0,     0,     0,\n",
      "             0,     0],\n",
      "        [  101,  3019,  2653,  6364,  2003,  1996,  4658,  4355,  2181,  1997,\n",
      "          9932,   102],\n",
      "        [  101, 14324,  2003,  2019,  4372, 16044,  2099,  1011,  2069,  2944,\n",
      "           102,     0]])\n",
      "\n",
      "tensor([[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n",
      "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
      "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]])\n",
      "\n",
      "tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n",
      "\n",
      "['[CLS] using transformers is quite simple [SEP] [PAD] [PAD] [PAD] [PAD] [PAD]', '[CLS] natural language processing is the coolest area of ai [SEP]', '[CLS] bert is an encoder - only model [SEP] [PAD]']\n"
     ]
    }
   ],
   "source": [
    "sequences = [\"Using transformers is quite simple\", \"Natural Language Processing is the coolest area of AI\", \"BERT is an encoder-only model\"]\n",
    "batch = tokenizer(sequences, padding=True, return_tensors='pt')\n",
    "print(batch[\"input_ids\"], batch[\"attention_mask\"], batch[\"token_type_ids\"], sep=\"\\n\\n\", end=\"\\n\\n\")\n",
    "print(tokenizer.batch_decode(batch[\"input_ids\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x3HouRwObG8e",
    "outputId": "6aae8198-fc7f-48cf-ef5f-e8e349689146",
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[-2.0453e-01,  7.0865e-02,  1.5989e-01,  ..., -5.2576e-01,\n",
      "          -8.6742e-02,  6.8585e-01],\n",
      "         [ 2.4598e-01,  4.5985e-01, -1.4832e-01,  ..., -2.9959e-01,\n",
      "          -6.3807e-02,  2.2089e-01],\n",
      "         [ 1.6290e+00, -7.5314e-02, -1.6926e-01,  ..., -5.9800e-01,\n",
      "          -2.4732e-01,  5.8838e-01],\n",
      "         ...,\n",
      "         [ 1.8744e-01, -1.4395e-01,  1.3122e-01,  ...,  5.4823e-02,\n",
      "          -3.0255e-01,  4.1559e-01],\n",
      "         [-4.2708e-01, -4.2683e-01,  1.7270e-01,  ...,  2.9482e-01,\n",
      "           5.4690e-02,  4.9447e-01],\n",
      "         [-9.1995e-02, -2.2785e-01,  9.8050e-02,  ...,  1.3341e-01,\n",
      "          -1.4247e-01,  4.9720e-01]],\n",
      "\n",
      "        [[-4.2831e-02,  2.8874e-02,  6.4191e-02,  ..., -1.5476e-01,\n",
      "          -1.0478e-02,  5.8216e-01],\n",
      "         [-1.0164e-01,  1.8912e-01, -5.2067e-01,  ..., -7.8897e-02,\n",
      "           1.9872e-01,  2.9997e-01],\n",
      "         [-5.4308e-01,  2.5865e-01,  5.8880e-01,  ..., -5.6930e-01,\n",
      "          -3.8819e-01, -1.1054e-01],\n",
      "         ...,\n",
      "         [-3.7056e-01,  4.8253e-01,  4.1688e-01,  ..., -2.3183e-01,\n",
      "          -4.5668e-01, -2.7996e-02],\n",
      "         [-5.4901e-01, -2.2220e-01,  1.0358e-02,  ...,  1.4678e-01,\n",
      "           4.0554e-02,  5.9835e-02],\n",
      "         [ 8.8016e-01,  2.5452e-02, -3.8448e-01,  ...,  2.0756e-01,\n",
      "          -9.9290e-01, -1.9862e-01]],\n",
      "\n",
      "        [[-4.2417e-01, -1.6140e-01,  1.3453e-01,  ..., -2.9248e-01,\n",
      "          -1.1854e-01,  5.5810e-01],\n",
      "         [ 4.2382e-01, -1.6716e-01,  1.9229e-01,  ...,  5.8446e-03,\n",
      "           2.4040e-01, -5.0844e-02],\n",
      "         [-4.8084e-01, -4.0256e-01, -2.4049e-05,  ..., -1.7063e-01,\n",
      "          -2.3191e-01,  5.1757e-01],\n",
      "         ...,\n",
      "         [ 1.6010e-02, -1.0433e-01, -2.0077e-01,  ..., -5.8289e-02,\n",
      "           1.2035e-01,  7.9889e-02],\n",
      "         [ 4.9451e-01,  3.7927e-01, -6.7176e-01,  ...,  5.2732e-02,\n",
      "          -6.8580e-01, -3.8649e-01],\n",
      "         [-4.5029e-01, -2.6872e-01,  3.4966e-01,  ...,  2.4620e-01,\n",
      "           1.3352e-02, -6.4719e-02]]], grad_fn=<NativeLayerNormBackward0>)\n",
      "batch_size x seq_len x hidden_dim\n",
      "torch.Size([3, 12, 768])\n"
     ]
    }
   ],
   "source": [
    "model_output = model(**batch) # equivalent to model(input_ids=batch['input_ids'], attention_mask=batch['attention_mask'], token_type_ids=batch['token_type_ids'])\n",
    "print(model_output.last_hidden_state) \n",
    "print(\"batch_size x seq_len x hidden_dim\", model_output.last_hidden_state.shape, sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B5fMlfuqbsh2",
    "outputId": "24878eac-5990-4248-a8a7-225b2bc87cac",
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n",
      "tensor([[[ 0.0215, -0.2639, -0.0377,  ...,  0.1970,  0.1228,  0.1301],\n",
      "         [-0.7210,  0.7983, -0.1146,  ...,  0.5685,  0.2871,  0.3625],\n",
      "         [ 1.8263,  0.6771, -1.2945,  ...,  0.0750,  0.1787,  0.8844],\n",
      "         ...,\n",
      "         [-0.0844, -0.4028,  0.3619,  ...,  0.8078, -0.0434, -0.0026],\n",
      "         [-0.5844, -0.3105,  0.3228,  ...,  0.9155,  0.0648,  0.0206],\n",
      "         [-0.4847, -0.2071,  0.3676,  ...,  0.7910,  0.0471, -0.0331]],\n",
      "\n",
      "        [[ 0.1411, -0.2476, -0.1009,  ...,  0.1248,  0.1271,  0.1129],\n",
      "         [ 0.1099,  0.1498, -0.2346,  ...,  0.0810,  0.2272, -0.7285],\n",
      "         [ 0.4657,  0.3590, -0.0722,  ..., -0.0788, -0.0577, -0.1413],\n",
      "         ...,\n",
      "         [-0.5130,  0.1590,  0.2484,  ...,  0.1736, -0.5944,  0.0723],\n",
      "         [-0.6027,  0.2167,  0.2660,  ...,  0.7422, -0.0018, -0.8578],\n",
      "         [-0.0624, -0.1068,  0.1162,  ...,  0.0308,  0.0711, -0.0221]],\n",
      "\n",
      "        [[ 0.0538, -0.4406, -0.1444,  ...,  0.3530,  0.2185,  0.1304],\n",
      "         [ 1.1208,  0.3139,  0.7051,  ...,  0.3387,  0.6092, -1.2108],\n",
      "         [-0.9731, -0.4062, -0.3032,  ...,  0.2626, -0.1010,  0.2996],\n",
      "         ...,\n",
      "         [ 0.6349,  1.0970, -0.2055,  ..., -0.3602, -0.7575, -0.9624],\n",
      "         [-0.0707, -0.1211,  0.1284,  ...,  0.0669,  0.1010, -0.0757],\n",
      "         [-0.2385, -0.6089,  0.5144,  ...,  0.7757, -0.0342, -0.2927]]],\n",
      "       grad_fn=<NativeLayerNormBackward0>)\n"
     ]
    }
   ],
   "source": [
    "model_output = model(**batch, output_hidden_states=True) # equivalent to model(input_ids=batch['input_ids'], attention_mask=batch['attention_mask'], token_type_ids=batch['token_type_ids'])\n",
    "all_states = model_output.hidden_states # list of outputs from all transformer layers, layer 0, 1, 2, ...., 12 (layer 0 is the embedding layer)\n",
    "print(len(all_states))\n",
    "print(all_states[3]) # output of 4th layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4I52s5nWV7ce",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OlAIb5ZKV7ce",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Loading a dataset from the Huggingface Hub\n",
    "\n",
    "- We are using the MRPC (Microsoft Research Paraphrasing Corpus) dataset that is part of the General Language Understanding Evaluation (GLUE) benchmark\n",
    "- Task: given two sentences, assign positive class (1) if the two sentences are paraphrases of one another (assign 0 otherwise)\n",
    "- [Link](https://huggingface.co/datasets/glue) to the dataset on the hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 326,
     "referenced_widgets": [
      "d0e37e0a9e3c4e6ab4d9a571d58f8fda",
      "984748bef2574da2a59f1bb343411a04",
      "3fba5956333043d18fe19f58d29f6317",
      "3bd17754748c401188f25067fa88b748",
      "f77cacda83fc48c3bac32041adc64900",
      "6498db99029c44c0ae90cc657769fd24",
      "69607f6f51624606be54cbe61f554124",
      "8e15fcb639134bee87e997ec88d65988",
      "fd9b88e38ab54b49b9a80c41f3733914",
      "cc62087cbfd64925a88c84a45d17baae",
      "4db83d5766564d38aa1df5ccf273b56f"
     ]
    },
    "id": "_3CkYoeSfPcZ",
    "outputId": "037ac002-7a6e-4837-c170-5f44e236d4cc",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.builder:Found cached dataset glue (/root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0e37e0a9e3c4e6ab4d9a571d58f8fda",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 3668\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 408\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 1725\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "mrpc_dataset = load_dataset(\"glue\", \"mrpc\")\n",
    "mrpc_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's check the dataset features and examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tGPUEc_Of2la",
    "outputId": "3a3a4ee2-17b1-4cbb-e6d4-a26e20100ba8",
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None), 'idx': Value(dtype='int32', id=None)}\n"
     ]
    }
   ],
   "source": [
    "print(mrpc_dataset[\"train\"].features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Wyk9hnjqfYQV",
    "outputId": "9f29638a-fb97-4cde-cb32-6d08edeee46b",
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .', 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .', 'label': 1, 'idx': 0}\n",
      "\n",
      "{'sentence1': \"Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion .\", 'sentence2': \"Yucaipa bought Dominick 's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998 .\", 'label': 0, 'idx': 1}\n"
     ]
    }
   ],
   "source": [
    "print(mrpc_dataset[\"train\"][0], end=\"\\n\\n\")\n",
    "print(mrpc_dataset[\"train\"][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x9f07mfbV7ce",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Operations on datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I_cbLikoiGfY",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The datasets library supports various operations on datasets such as slicing and column selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r8QhXZCjiTuB",
    "outputId": "a30c89c8-eabd-49c5-9209-42821524f225",
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sentence1': ['Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
       "  \"Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion .\",\n",
       "  'They had published an advertisement on the Internet on June 10 , offering the cargo for sale , he added .',\n",
       "  'Around 0335 GMT , Tab shares were up 19 cents , or 4.4 % , at A $ 4.56 , having earlier set a record high of A $ 4.57 .',\n",
       "  'The stock rose $ 2.11 , or about 11 percent , to close Friday at $ 21.51 on the New York Stock Exchange .',\n",
       "  'Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier .',\n",
       "  'The Nasdaq had a weekly gain of 17.27 , or 1.2 percent , closing at 1,520.15 on Friday .',\n",
       "  'The DVD-CCA then appealed to the state Supreme Court .',\n",
       "  'That compared with $ 35.18 million , or 24 cents per share , in the year-ago period .',\n",
       "  'Shares of Genentech , a much larger company with several products on the market , rose more than 2 percent .'],\n",
       " 'sentence2': ['Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .',\n",
       "  \"Yucaipa bought Dominick 's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998 .\",\n",
       "  \"On June 10 , the ship 's owners had published an advertisement on the Internet , offering the explosives for sale .\",\n",
       "  'Tab shares jumped 20 cents , or 4.6 % , to set a record closing high at A $ 4.57 .',\n",
       "  'PG & E Corp. shares jumped $ 1.63 or 8 percent to $ 21.03 on the New York Stock Exchange on Friday .',\n",
       "  \"With the scandal hanging over Stewart 's company , revenue the first quarter of the year dropped 15 percent from the same period a year earlier .\",\n",
       "  'The tech-laced Nasdaq Composite .IXIC rallied 30.46 points , or 2.04 percent , to 1,520.15 .',\n",
       "  'The DVD CCA appealed that decision to the U.S. Supreme Court .',\n",
       "  'Earnings were affected by a non-recurring $ 8 million tax benefit in the year-ago period .',\n",
       "  'Shares of Xoma fell 16 percent in early trade , while shares of Genentech , a much larger company with several products on the market , were up 2 percent .'],\n",
       " 'label': [1, 0, 1, 0, 1, 1, 0, 1, 0, 0],\n",
       " 'idx': [0, 1, 2, 3, 4, 5, 6, 7, 8, 10]}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrpc_dataset[\"train\"][:10] # Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z4MuhMdxiefR",
    "outputId": "19a00b3d-38c9-4ce7-d185-cad5728e84a3",
    "slideshow": {
     "slide_type": "-"
    },
    "tags": []
   },
   "outputs": [
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       " 1,\n",
       " 0,\n",
       " ...]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrpc_dataset[\"train\"][\"label\"] # Column selection "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-jIksXUlif85",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "```select``` returns rows according to a list of indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "YnDFEHabiaSM",
    "tags": []
   },
   "outputs": [],
   "source": [
    "sel_data = mrpc_dataset[\"train\"].select([0, 11, 22, 1514])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UX0CGBmwiuN0"
   },
   "source": [
    "```filter``` returns rows that match a specified condition:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KAbgeNFqiqT8",
    "outputId": "61e8c213-550b-4488-9eb0-c93787094e23",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-99cb1c197354211b.arrow\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'sentence1': ['This Palm OS smart phone is the last product the company will release before it becomes a part of palmOne .',\n",
       "  \"This week 's tour will take Bush to Senegal , South Africa , Botswana , Uganda and Nigeria , and is aimed at softening his warrior image at home and abroad .\",\n",
       "  'This was around the time Congress was debating a resolution granting the President broad authority to wage war .',\n",
       "  \"This morning , at UM 's New York office , Coen revised his expectations downward , saying that spending would instead rise 4.6 percent to $ 247 billion .\",\n",
       "  'This is the only planet that has been found in orbit around a binary star system .',\n",
       "  'This year , local health departments hired part-time water samplers and purchased testing equipment with a $ 282,355 grant from the Environmental Protection Agency .',\n",
       "  'This was double the $ 818 million reported for the first three months of 2001 .',\n",
       "  'This change in attitude gave upscale purveyors including Neiman Marcus , the parent of Bergdorf Goodman ; and Nordstrom strong sales gains in May .',\n",
       "  'This is a process and there will be other opportunities for people to participate in the rebuilding of Iraq . \" he told reporters .',\n",
       "  'This deterioration of security compounds when nearly all computers rely on a single operating system subject to the same vulnerabilities the world over , \" Geer added .',\n",
       "  'This means Berlusconi will be safe from prosecution until he leaves elected office , scheduled for 2006 .',\n",
       "  'This is a case about a woman who for 20 years dedicated her life to this country , \" said Janet Levine .',\n",
       "  'This is the first time in the United States that five whales have been released simultaneously from a single stranding incident .',\n",
       "  'This is what Dr. Dean said : \" I still want to be the candidate for guys with Confederate flags in their pickup trucks .',\n",
       "  'This decision would \" throw a monkey wrench into the FCC \\'s efforts to develop a vitally important national broadband policy , \" he said .'],\n",
       " 'sentence2': ['This was almost certainly its last full quarter before the company becomes a part of Palm .',\n",
       "  'In his first trip to sub-Saharan Africa as president , Mr. Bush will visit Senegal , South Africa , Botswana , Uganda and Nigeria before returning home on Saturday .',\n",
       "  'Within four days , the House and Senate overwhelmingly endorsed a resolution granting the president authority to go to war .',\n",
       "  \"Speaking to reporters at a New York news conference , Universal McCann 's Coen projected that total U.S. ad spending will rise 4.6 percent to $ 247.7 billion this year .\",\n",
       "  'The new found planet is the only one known to orbit such a double-star system .',\n",
       "  'This year , Peninsula health officials got the money to hire part-time water samplers and purchase testing equipment thanks to a $ 282,355 grant from the Environmental Protection Agency .',\n",
       "  'Berkshire Hathaway made profits of $ 1.7 billion in the first three months of this year alone , its best performance .',\n",
       "  'This change in attitude gave upscale purveyors including Neiman Marcus Group Inc. and Nordstrom Inc . , along with some boutique retailers , strong sales gains in May .',\n",
       "  'This is a process and there will be other opportunities for people to participate in the rebuilding of Iraq . \"',\n",
       "  '\" The deterioration of security compounds when nearly all computers rely on a single operating system subject to the same vulnerabilities the world over . \"',\n",
       "  'This means Berlusconi will be safe from prosecution until his term ends in 2006 , unless his government falls before then .',\n",
       "  '\" This case is about a woman who for over 20 years dedicated herself to this country , \" Levine said .',\n",
       "  \"Today , the experts will perform the United States ' first simultaneous release of five whales from a single stranding incident .\",\n",
       "  'He told the Register : \" I still want to be the candidate for guys with Confederate flags in their pickup trucks . \"',\n",
       "  'He added that the decision will \" throw a monkey wrench into the FCC \\'s efforts to develop a vitally important national broadband policy . \"'],\n",
       " 'label': [0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1],\n",
       " 'idx': [97,\n",
       "  482,\n",
       "  1025,\n",
       "  1431,\n",
       "  1450,\n",
       "  1478,\n",
       "  1569,\n",
       "  1783,\n",
       "  2229,\n",
       "  2319,\n",
       "  2444,\n",
       "  2567,\n",
       "  2724,\n",
       "  3247,\n",
       "  3439]}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data = mrpc_dataset[\"train\"].filter(lambda example: example[\"sentence1\"].startswith(\"This\"))\n",
    "filtered_data[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v2ROtSRrjZVj",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Train and test splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "heGSyeVHjMWV",
    "outputId": "c857cd09-4eb9-47e8-f176-be675f01631b",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 3301\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 367\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrpc_dataset[\"train\"].train_test_split(test_size=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R4uUNVWskQQL",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The map function is one of the most important functions: it applies a function to every example in the dataset. It's primary usage in NLP is for tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V677OT5fkZYN",
    "outputId": "c1a176b7-ce3f-413f-d902-6627ddb0404d",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-00d929bc88d3eb33.arrow\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .',\n",
       " 'label': 1,\n",
       " 'idx': 0,\n",
       " 'new_sentence1': 'Sentence 1: Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .'}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = mrpc_dataset[\"train\"].map(lambda example: {\"new_sentence1\": \"Sentence 1: \" + example[\"sentence1\"]}) # Create a new column as a function of the previous one\n",
    "new_dataset[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hb5oaQ7TV7cf",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Saving and loading datasets from disk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KIP2iNKBtJWK"
   },
   "source": [
    "We also can save datasets to disk and reload them at our convience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17,
     "referenced_widgets": [
      "dbb1be5e073d4920b073523b0d471733",
      "74a9884b059f4dabb44b81b477da92af",
      "3b855e7172584a2c80603490317c083e",
      "1984ff1562f34578a68fbe9f05ef3dee",
      "c8750d0e41d44074aa223a6d8a07669d",
      "bf5283775d6e4a5cb0ece90d817eccc9",
      "4cc60ecfa2d64b0b9c5742be25541577",
      "5a747082cff34b01b2f4ec356104780a",
      "64a49c644d8f42a399a296a3a1cdb3d2",
      "e34e9f82a9a349749e59654d4ebbecb2",
      "ee503ec5ecff45ed8483544498114d5c"
     ]
    },
    "id": "JzFLYfjOtI10",
    "outputId": "a6c43202-b9a7-40d7-ba63-ea3dfe82b064",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dbb1be5e073d4920b073523b0d471733",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/3668 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_from_disk\n",
    "save_path = \"datasets/my_data\"\n",
    "\n",
    "new_dataset.save_to_disk(save_path) # Saving\n",
    "reloaded_dataset = load_from_disk(save_path) # Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fdQGMct8V7cf",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Other functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nt-9MtHFlccn"
   },
   "source": [
    "Other useful functions:\n",
    "\n",
    "- [Rename, remove, cast, and flatten](https://huggingface.co/docs/datasets/v2.12.0/en/process#rename-remove-cast-and-flatten)\n",
    "- [Concatenate, interleave](https://huggingface.co/docs/datasets/v2.12.0/en/process#concatenate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4T6o2XgtmA0y"
   },
   "source": [
    "Datasets can also be exported to:\n",
    "  - csv\n",
    "  - json\n",
    "  - parquet\n",
    "  - sql\n",
    "  - In-memory objects Pandas Dataframe / Python Dictionary\n",
    " \n",
    "See [Guide to export](https://huggingface.co/docs/datasets/v2.12.0/en/process#export)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0A50SxODV7cg",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Fine-tuning a transformer model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kwAH7sk0tg1N"
   },
   "source": [
    "Fine-tuning transformer models:\n",
    "\n",
    "  - We want to fine-tune BERT on the MRPC (Microsoft Research Paraphrase Corpus) dataset\n",
    "  - The first thing to do is to preprocess the data using tokenizer:\n",
    "    - The task is a **sentence-pair classification task**\n",
    "    - We need every input to be in the format \n",
    "$ [CLS] \\; seq_1 \\; [SEP] \\; seq_2 \\; [SEP]$\n",
    "\n",
    "    - We will feed the embedding of the [CLS] to the classification head on top of BERT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "clJYUZN7V7cg",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Dataset tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "jRDnTDYckkYD",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# This is the function that we want to apply on the entire dataset\n",
    "def tokenize(example):\n",
    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mKu_nzPzuWmF",
    "outputId": "c4af11bf-b50b-4b3a-da0d-826d48b1bdea",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-a81853ae0ee75a50.arrow\n"
     ]
    }
   ],
   "source": [
    "# We can apply on a single split (train/val/test)\n",
    "train_dataset = mrpc_dataset[\"train\"].map(tokenize, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mYsopdlfvjiN",
    "outputId": "ac3dd5c5-6232-4c5b-c37e-fc1b3a51317e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
       "    num_rows: 3668\n",
       "})"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54,
     "referenced_widgets": [
      "96b78c41e3034f7588dcbf55a1cba0e5",
      "9643a3b7c7ed4589b77848eb1db5ddd9",
      "72d504cc12ad442fb457af16f40ea585",
      "4d19348622b44c43bee01db2f2fb5116",
      "e2b8b9195ef84edd82b6e20b9cb423c3",
      "75bd2e4719784d8e91ba2740c187da85",
      "07a1757272544f2b97d95b3fb160d59e",
      "b14aec1e673c4a38bc5cd5f282f52874",
      "ccdb54dc7d894312881ec05e3f488021",
      "41c83908323745efb3528fa73714142a",
      "92a42ce5ddd248f6aac841d1139c3060"
     ]
    },
    "id": "aWPqbltxvpGc",
    "outputId": "6a5fa234-fe45-47b4-9fd8-588020213388",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-a81853ae0ee75a50.arrow\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96b78c41e3034f7588dcbf55a1cba0e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/408 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-8495274d07cdf26e.arrow\n"
     ]
    }
   ],
   "source": [
    "# We can also apply the tokenization on the DatasetDict, so we will tokenize train, val and test with a single line\n",
    "mrpc_dataset = mrpc_dataset.map(tokenize, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pous7gRGV7cg",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Dynamic padding and collators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-xxyj79HwAjv"
   },
   "source": [
    "Before training, there are two things we need to take care of.\n",
    "\n",
    "- Padding (two options):\n",
    "  - padding to the longest sentence in the **entire** dataset\n",
    "    - not reccomended: if the longest sequence in the dataset has a lenght of 300 subwords, a sequence made of 100 subwords will have 200 [PAD] tokens\n",
    "  - dynamic padding: pad to the longest sentence in the **batch**\n",
    "    - much less padding ➡️ faster forward pass\n",
    "- Conversion to PyTorch tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "HtLtA2Fwv5uZ"
   },
   "outputs": [],
   "source": [
    "from transformers import DataCollatorWithPadding\n",
    "\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors='pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bPy5p8cXw-G6",
    "outputId": "6098937c-ebcd-448b-9c92-7e26656f73ea"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'input_ids': tensor([[  101,  2572,  3217,  5831,  5496,  2010,  2567,  1010,  3183,  2002,\n",
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       "             0,     0,     0,     0,     0,     0,     0],\n",
       "        [  101,  2027,  2018,  2405,  2019, 15147,  2006,  1996,  4274,  2006,\n",
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       "          1037,  1002,  1018,  1012,  5401,  1012,   102],\n",
       "        [  101,  1996,  4518,  3123,  1002,  1016,  1012,  2340,  1010,  2030,\n",
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       "          1012,  4868,  2006,  1996,  2047,  2259,  4518,  3863,  1012,   102,\n",
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       "        [  101,  6599,  1999,  1996,  2034,  4284,  1997,  1996,  2095,  3333,\n",
       "          2321,  3867,  2013,  1996,  2168,  2558,  1037,  2095,  3041,  1012,\n",
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       "             0,     0,     0,     0,     0,     0,     0],\n",
       "        [  101,  1996, 17235,  2850,  4160,  2018,  1037,  4882,  5114,  1997,\n",
       "          2459,  1012,  2676,  1010,  2030,  1015,  1012,  1016,  3867,  1010,\n",
       "          5494,  2012,  1015,  1010, 19611,  1012,  2321,  2006,  5958,  1012,\n",
       "           102,  1996,  6627,  1011, 17958, 17235,  2850,  4160, 12490,  1012,\n",
       "         11814,  2594, 24356,  2382,  1012,  4805,  2685,  1010,  2030,  1016,\n",
       "          1012,  5840,  3867,  1010,  2000,  1015,  1010, 19611,  1012,  2321,\n",
       "          1012,   102,     0,     0,     0,     0,     0],\n",
       "        [  101,  1996,  4966,  1011, 10507,  2050,  2059, 12068,  2000,  1996,\n",
       "          2110,  4259,  2457,  1012,   102,  1996,  4966, 10507,  2050, 12068,\n",
       "          2008,  3247,  2000,  1996,  1057,  1012,  1055,  1012,  4259,  2457,\n",
       "          1012,   102,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "             0,     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, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "         1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "        [1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "         1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n",
       "        [1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "         1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 1, 1, 1, 1, 1, 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, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 1, 1, 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, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "         1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 1, 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, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'labels': tensor([1, 0, 1, 0, 1, 1, 0, 1])}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = mrpc_dataset[\"train\"][:8]\n",
    "samples = {\"input_ids\":samples[\"input_ids\"], \"attention_mask\": samples[\"attention_mask\"], \"token_type_ids\": samples[\"token_type_ids\"], \"label\":samples[\"label\"]}\n",
    "data_collator(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dfwfbNdn1DVe",
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Different models for different tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VBwGJTV_18Ix"
   },
   "source": [
    "- We need to define the model that we want to train\n",
    "- Before, we instatiated a ```AutoModel``` object\n",
    "  - This one is unsuitable for classification tasks: it only contains the BERT encoder but no classification head\n",
    "- The classification head is a feed-forward neural network placed on top of BERT to solve the classification task\n",
    "- For our task we will use ```AutoModelForSequenceClassification```\n",
    "- Other tasks require different models: ```AutoModelForTokenClassification```, ```AutoModelForQuestionAnswering```\n",
    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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PcLNNCsw3aVq",
    "outputId": "1e985825-274f-4561-8689-ec494c40f22d",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
      "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BertForSequenceClassification(\n",
      "  (bert): BertModel(\n",
      "    (embeddings): BertEmbeddings(\n",
      "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
      "      (position_embeddings): Embedding(512, 768)\n",
      "      (token_type_embeddings): Embedding(2, 768)\n",
      "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (encoder): BertEncoder(\n",
      "      (layer): ModuleList(\n",
      "        (0-11): 12 x BertLayer(\n",
      "          (attention): BertAttention(\n",
      "            (self): BertSelfAttention(\n",
      "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
      "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
      "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
      "              (dropout): Dropout(p=0.1, inplace=False)\n",
      "            )\n",
      "            (output): BertSelfOutput(\n",
      "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
      "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
      "              (dropout): Dropout(p=0.1, inplace=False)\n",
      "            )\n",
      "          )\n",
      "          (intermediate): BertIntermediate(\n",
      "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (intermediate_act_fn): GELUActivation()\n",
      "          )\n",
      "          (output): BertOutput(\n",
      "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
      "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
      "            (dropout): Dropout(p=0.1, inplace=False)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (pooler): BertPooler(\n",
      "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
      "      (activation): Tanh()\n",
      "    )\n",
      "  )\n",
      "  (dropout): Dropout(p=0.1, inplace=False)\n",
      "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForSequenceClassification\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-uncased\",num_labels=2)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GXrKRnP51Dpl",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For training, we will be using the Trainer API from Huggingface.\n",
    "\n",
    "-  It will handle all details of training and validation\n",
    "-  The [```Trainer```](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Trainer) object requires a [```TrainingArguments```](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) object that contains all the specifications of the training procedure\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "GoNA7yR231Gg"
   },
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments\n",
    "\n",
    "training_args = TrainingArguments(output_dir=\"bert_mrpc\", # where to save checkpoints and model predictions\n",
    "                                  per_device_train_batch_size=8, # training batch size\n",
    "                                  per_device_eval_batch_size=16, # validation/test batch sizie\n",
    "                                  num_train_epochs=3, # number of epochs\n",
    "                                  save_strategy='epoch', # checkpoint saving frequency\n",
    "                                  evaluation_strategy=\"epoch\",# how frequently to run validation, can be epoch or steps (in this case you need to specify eval_steps)\n",
    "                                  metric_for_best_model=\"f1\", # metric used to pick checkpoints\n",
    "                                  greater_is_better=True, # whether the metric for checkpoint needs to be maximized or minimized\n",
    "                                  learning_rate=3e-5, # learning rate or peak learning rate if scheduler is used\n",
    "                                  optim=\"adamw_torch\", # which optimizer to use\n",
    "                                  lr_scheduler_type=\"linear\", # which scheduler to use\n",
    "                                  warmup_ratio=0.1, # % of steps for which to do warmup\n",
    "                                  seed=33, # setting seed for reproducibility\n",
    "                                  load_best_model_at_end=True) # after training, load the best checkpoint according to metric_for_best_model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ojyujh9PtOaE",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "\n",
    "- Other parameters to consider for your projects:\n",
    "  - ```gradient_accumulation_steps``` and ```gradient_checkpointing``` if you have memory problems (batch doesn't fit in memory)\n",
    "      - See [here](https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-accumulation) for a full explanation of what they do \n",
    "  - ```report_to```  if you want to report your metrics, train loss to an external logger such as Weights & Biases (highly reccomended for your projects, see [wandb.ai](https://wandb.ai))\n",
    "  - ```resume_from_checkpoint```: you can restart training from a checkpoint by passing the save path here\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's define an evaluation function to be used during validation (and later for test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "S5-_JDNBdEUY",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import evaluate\n",
    "import numpy as np\n",
    "def compute_metrics_mrpc(eval_preds):\n",
    "    metric = evaluate.load(\"glue\", \"mrpc\")\n",
    "    logits, labels = eval_preds\n",
    "    predictions = np.argmax(logits, axis=-1)\n",
    "    return metric.compute(predictions=predictions, references=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "zelpSPxf3yqu",
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from transformers import Trainer\n",
    "\n",
    "trainer = Trainer(\n",
    "    model,\n",
    "    training_args,\n",
    "    train_dataset=mrpc_dataset[\"train\"],\n",
    "    eval_dataset=mrpc_dataset[\"validation\"],\n",
    "    data_collator=data_collator,\n",
    "    tokenizer=tokenizer,\n",
    "    compute_metrics=compute_metrics_mrpc\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 220,
     "referenced_widgets": [
      "a99a155215424214ae608ac6f2cba2c5",
      "93b72792f15d405fa312f0e3dd48a326",
      "9daea4e33cf2482db66e2f888754e04d",
      "da57ad57fbd340da8e576812cbc4e823",
      "73703e950fea4af7b8b31264eb06e557",
      "77165ebd16c64c1cb38f8aa6860c04f8",
      "46a4f8a3fcce431ca357a55bf6a009cb",
      "30034ea38624485a8030fd2a0f46e669",
      "cdcd337b5cfa44278a52dec7f7aad31a",
      "2e36fa02e90f495b9121ce82c4459924",
      "22db216dd8c44046b4e0e10a681e1871"
     ]
    },
    "id": "MpC2mnJW5NSu",
    "outputId": "b4c6eed9-5f43-453f-8772-4cc92542a59d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1377' max='1377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1377/1377 03:32, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.472064</td>\n",
       "      <td>0.811275</td>\n",
       "      <td>0.875203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.505200</td>\n",
       "      <td>0.488288</td>\n",
       "      <td>0.845588</td>\n",
       "      <td>0.894118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.256400</td>\n",
       "      <td>0.607004</td>\n",
       "      <td>0.860294</td>\n",
       "      <td>0.902896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a99a155215424214ae608ac6f2cba2c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/5.75k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1377, training_loss=0.30870552035285326, metrics={'train_runtime': 215.0972, 'train_samples_per_second': 51.158, 'train_steps_per_second': 6.402, 'total_flos': 404712080911440.0, 'train_loss': 0.30870552035285326, 'epoch': 3.0})"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Computing performance on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "7aAuuFS3dVfV",
    "outputId": "b5656556-a90c-4327-90d8-49c3c839dcac",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'test_loss': 0.6957690119743347, 'test_accuracy': 0.847536231884058, 'test_f1': 0.8896349139739823, 'test_runtime': 8.9659, 'test_samples_per_second': 192.395, 'test_steps_per_second': 12.046}\n"
     ]
    }
   ],
   "source": [
    "test_predictions = trainer.predict(mrpc_dataset[\"test\"])\n",
    "print(test_predictions.metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "d3mR9vS5diJj",
    "outputId": "618295df-0b4d-4fdf-c3ab-f71786c94898",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'bert_mrpc/checkpoint-1377'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.state.best_model_checkpoint # folder where best model is saved"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decoder models\n",
    "\n",
    "![]( 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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoder-decoder models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [27, 333, 14145, 1707, 1], 'attention_mask': [1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_name = \"t5-small\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModel.from_pretrained(model_name)\n",
    "\n",
    "tokenizer(\"I love tokenization\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decoder-only model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': [40, 1842, 11241, 1634], 'attention_mask': [1, 1, 1, 1]}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM\n",
    "\n",
    "model_name = \"openai-community/gpt2\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
    "\n",
    "tokenizer(\"I love tokenization\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.getLogger(\"transformers\").setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"In a world where AI robots rule, humans wouldn't be as happy as robots, and when AI human-like beings find\""
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_text = \"In a world where AI robots rule, humans\"\n",
    "\n",
    "\n",
    "input_ids = tokenizer.encode(input_text, return_tensors=\"pt\")\n",
    "\n",
    "output = model.generate(input_ids, \n",
    "                        max_length=25,\n",
    "                        num_beams=1,\n",
    "                        num_return_sequences=1, \n",
    "                        temperature=1,\n",
    "                        top_k=50,\n",
    "                        top_p=1.0,\n",
    "                        do_sample=True)\n",
    "tokenizer.decode(output.tolist()[0], skip_special_tokens=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Temperature\n",
    "The temperature parameter controls the randomness of the generated text by scaling the logits before applying the softmax function during text generation. A higher temperature leads to more diverse and unpredictable text generation, as it increases the likelihood of selecting lower probability tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Temperature: 0.5\n",
      "In a world where AI robots rule, humans are unlikely to make good on their promise. But that's not the point.\n",
      "\n",
      "Temperature: 1.0\n",
      "In a world where AI robots rule, humans may still have to get used to interacting with machines in ways that are just plain\n",
      "\n",
      "Temperature: 1.5\n",
      "In a world where AI robots rule, humans cannot play by the same rule.\n",
      "\n",
      "What we need in a social world\n",
      "\n",
      "Temperature: 3.0\n",
      "In a world where AI robots rule, humans are forced instead under these systems onto many increasingly hard decisions. In his seminal The\n",
      "\n",
      "Temperature: 10.0\n",
      "In a world where AI robots rule, humans on this board make room inside that human bubble which can form artificial systems even further\n"
     ]
    }
   ],
   "source": [
    "print('\\nTemperature: 0.5')\n",
    "output = model.generate(input_ids, temperature=0.5, max_length=25, do_sample=True)\n",
    "print(tokenizer.decode(output.tolist()[0], skip_special_tokens=True))\n",
    "\n",
    "print('\\nTemperature: 1.0')\n",
    "output = model.generate(input_ids, temperature=1, max_length=25, do_sample=True)\n",
    "print(tokenizer.decode(output.tolist()[0], skip_special_tokens=True))\n",
    "\n",
    "print('\\nTemperature: 1.5')\n",
    "output = model.generate(input_ids, temperature=1.5, max_length=25, do_sample=True)\n",
    "print(tokenizer.decode(output.tolist()[0], skip_special_tokens=True))\n",
    "\n",
    "print('\\nTemperature: 3.0')\n",
    "output = model.generate(input_ids, temperature=3.0, max_length=25, do_sample=True)\n",
    "print(tokenizer.decode(output.tolist()[0], skip_special_tokens=True))\n",
    "\n",
    "print('\\nTemperature: 10.0')\n",
    "output = model.generate(input_ids, temperature=10.0, max_length=25, do_sample=True)\n",
    "print(tokenizer.decode(output.tolist()[0], skip_special_tokens=True))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### top_k and top_p\n",
    "\n",
    "The top_k parameter limits the vocabulary of candidate tokens to the top k most probable ones at each step.\n",
    "\n",
    "The top_p parameter in text generation dynamically selects tokens until the cumulative probability mass exceeds the specified threshold.\n",
    "\n",
    "![]( 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    "### Training under low resources\n",
    "- Load the model in lower precision (e.g. bf16)\n",
    "- Use AdaFactor instead of Adam as optimizer\n",
    "- Reduce the batch size and use accumulation steps\n",
    "- Consider Parameter-Efficient Fine Tuning (PEFT) instead of full fine-tuning"
   ]
  },
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    "## LLMs Landscape\n",
    "The landscape of LLMs is currently flooded with models and their derivative variants. There isn't a single best model; rather, different models may excel at different tasks and goals. [Leaderboards](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) have been created to compare models, however do not rely on them completely.\n",
    "\n",
    "Here are some examples of base models you wight want to experiment with:\n",
    "  - LLama 2\n",
    "  - Mistral-7B\n",
    "  - Gemma-7B\n",
    "  - Qwen1.5\n",
    "  - OpenChat\n",
    "  \n",
    "\n"
   ]
  },
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   "metadata": {},
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    "### Prompting\n",
    "\n",
    "Building a prompt is an iterative refinement process that can have great effects on the final LLM output. While prompt engineering is best learned through experimentation, here are some tips:\n",
    "\n",
    "  - Explain clearly what you want\n",
    "  - Give structure to your prompt (e.g. xml tags &lt;format&gt;, &lt;instructions&gt;, &lt;instructions&gt;)\n",
    "  - Prefill the LLM answer\n",
    "  - In-context learning is very powerful (even with very few examples)\n",
    "  - Prompts for roles if you want specific styles\n",
    "  - Use strong words to empathize specifc aspects of the prompt\n",
    " \n",
    "[Here](https://github.com/snwfdhmp/awesome-gpt-prompt-engineering?tab=readme-ov-file) and [here](https://docs.anthropic.com/claude/docs/prompt-engineering) you can find further insights on prompting."
   ]
  },
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   "metadata": {},
   "source": [
    "## Further reading/learning\n",
    "\n",
    "- In this tutorial, we have covered parts of chapter 1-3 of the HuggingFace Course\n",
    "    - Check the entire course [here](https://huggingface.co/learn/nlp-course/chapter1/1) to have a full understading of the library\n",
    "    - Particularly, check how you can solve other tasks with HuggingFace transformers (NER, QA, Summarization) [here](https://huggingface.co/learn/nlp-course/chapter7/1?fw=pt)\n"
   ]
  }
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