Assignment_Solution_Transformers_HAAI_Course.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Sequence Classification**"
      ],
      "metadata": {
        "id": "zpXr7C0ggzC-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers[sentencepiece]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzVyV0XIcyyl",
        "outputId": "aa060af0-6d4f-410c-f81a-b2b5faf06409"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers[sentencepiece]\n",
            "  Downloading transformers-4.34.1-py3-none-any.whl (7.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.7/7.7 MB\u001b[0m \u001b[31m44.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.12.4)\n",
            "Collecting huggingface-hub<1.0,>=0.16.4 (from transformers[sentencepiece])\n",
            "  Downloading huggingface_hub-0.18.0-py3-none-any.whl (301 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.0/302.0 kB\u001b[0m \u001b[31m34.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (1.23.5)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (2.31.0)\n",
            "Collecting tokenizers<0.15,>=0.14 (from transformers[sentencepiece])\n",
            "  Downloading tokenizers-0.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m100.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers[sentencepiece])\n",
            "  Downloading safetensors-0.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m83.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (4.66.1)\n",
            "Collecting sentencepiece!=0.1.92,>=0.1.91 (from transformers[sentencepiece])\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m76.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]) (3.20.3)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers[sentencepiece]) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers[sentencepiece]) (4.5.0)\n",
            "Collecting huggingface-hub<1.0,>=0.16.4 (from transformers[sentencepiece])\n",
            "  Downloading huggingface_hub-0.17.3-py3-none-any.whl (295 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m34.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[sentencepiece]) (3.3.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[sentencepiece]) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[sentencepiece]) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[sentencepiece]) (2023.7.22)\n",
            "Installing collected packages: sentencepiece, safetensors, huggingface-hub, tokenizers, transformers\n",
            "Successfully installed huggingface-hub-0.17.3 safetensors-0.4.0 sentencepiece-0.1.99 tokenizers-0.14.1 transformers-4.34.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "e1GBUsXbcY2H"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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        "id": "AJ-DL22hdB66",
        "outputId": "b091fc2c-571f-4cfe-db48-1c2f362b5e92"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        "id": "F5CgkaMldFEH",
        "outputId": "3642afca-02da-4757-ae27-019c3f650ca9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_inputs = [\n",
        "    \"I get irritated during the winter.\",\n",
        "    \"Ravi received the director's gold medal for being the topper.\",\n",
        "    \"As expected, Sumana received her promotion letter today.\"\n",
        "]\n",
        "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
        "print(inputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "34rUyf8DdQew",
        "outputId": "789dd54b-3692-46f6-d85e-db34025b2ede"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[  101,  1045,  2131, 15560,  2076,  1996,  3467,  1012,   102,     0,\n",
            "             0,     0,     0,     0,     0,     0],\n",
            "        [  101, 16806,  2363,  1996,  2472,  1005,  1055,  2751,  3101,  2005,\n",
            "          2108,  1996,  2327,  4842,  1012,   102],\n",
            "        [  101,  2004,  3517,  1010,  7680,  5162,  2363,  2014,  4712,  3661,\n",
            "          2651,  1012,   102,     0,     0,     0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],\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, 1, 1, 1, 1, 1, 0, 0, 0]])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = model(**inputs)\n",
        "print(outputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVXOx_7Ddjk6",
        "outputId": "206e6cf2-cc99-4a50-ee7a-1ad891f2769b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SequenceClassifierOutput(loss=None, logits=tensor([[ 4.0595, -3.3044],\n",
            "        [-3.9833,  4.3278],\n",
            "        [-2.4997,  2.6847]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(outputs.logits.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yg6Fm8_sfBtG",
        "outputId": "0cb888ae-87d4-442e-a339-017f4e11dfd8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([3, 2])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Find the label / class probabilities\n",
        "import torch\n",
        "outputs = torch.nn.functional.softmax(outputs.logits, dim = -1)\n",
        "print(outputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3lQ9muRYfEE2",
        "outputId": "163efab8-e8f4-47e8-e72d-f50f3b7a2ab9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[9.9937e-01, 6.3328e-04],\n",
            "        [2.4571e-04, 9.9975e-01],\n",
            "        [5.5720e-03, 9.9443e-01]], grad_fn=<SoftmaxBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We get [0.99, 0.0006] as the output for the first input, [0.0002, 0.99] as the output for the second input, and finally [0.005, 0.99] as the output for the third input sample.\n",
        "\n",
        "Here we observe that our model is 99% confident that the first input sample belongs to the NEGATIVE class, 99% confident that the second input sample belongs to the POSITIVE class, and 99% confident that the third input sample belongs to the POSITIVE class. We observe that the model’s output is quite accurate.\n",
        "\n",
        "We can check the labels of the model in the following way:"
      ],
      "metadata": {
        "id": "AT17fHQJfXa3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.id2label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iBGW5JKQfPIy",
        "outputId": "f640372a-fec6-421f-8cb0-ea857a814402"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'NEGATIVE', 1: 'POSITIVE'}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}