Sentiment_Analysis_🤗.ipynb

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  "cells": [
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aryan-401/GoogleColabProjects/blob/main/Sentiment_Analysis_%F0%9F%A4%97.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
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        "id": "9c-Xc9s618wX",
        "outputId": "05f94ed5-9872-4f03-d281-aff5f3985c61"
      },
      "execution_count": 1,
      "outputs": [
        {
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          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m57.0 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) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m32.7 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) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m59.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.2-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[31m62.4 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) (4.66.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (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,>=0.14.1->transformers) (4.7.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.16.4 safetensors-0.3.2 tokenizers-0.13.3 transformers-4.31.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "X5VAdjA_1zda"
      },
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      "source": [
        "from transformers import pipeline\n",
        "import torch\n",
        "import torch.nn.functional as F"
      ]
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        "classifier_v0 = pipeline(\"sentiment-analysis\")\n",
        "test_many = [\"This is pretty good\", \"I dont hate it\", \"There is too much salt\"]\n",
        "test_one = \"I can't believe that I got the part\"\n",
        "result = classifier_v0(test_many)\n",
        "print(result)"
      ],
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            "463bc7c758bf4064997e804216073b69",
            "de05543344164c089af5c120bfe652b6",
            "399149f4eb3a4fea8b2a4687482663f6",
            "ebe78f2b22cd4285a73b5fe37326fb9c",
            "8d0da385479a488d868efc9d89fdbaba",
            "268d202899884584b9c0fa10a40e8713",
            "636cd82f8ed24315ac4c3b5a337ad2fc",
            "b4fbb772003540a49a40bd4122305b2e",
            "d440ab69dd0b45a896ed5583b605c421"
          ]
        },
        "id": "aQnnZDA_1783",
        "outputId": "2b6735fd-10c3-4536-baea-fda62c6efa98"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "e84446529cec4ae482e9150fdc260e95"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "6615a7deea554261b94f54435848b1f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "849c7efb03914937a9c673f211bae1bf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "82eb75be56834ec98f9fb8ffa8c75a6a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'POSITIVE', 'score': 0.9998430013656616}, {'label': 'NEGATIVE', 'score': 0.5758727192878723}, {'label': 'NEGATIVE', 'score': 0.9955743551254272}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = 'distilbert-base-uncased-finetuned-sst-2-english'\n",
        "classifier_v1 = pipeline(\"sentiment-analysis\", model = model_name)\n",
        "result = classifier_v1(test_many)\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTFl1Ojq2rZI",
        "outputId": "80b22e7d-2dc3-4233-8531-de6eb754a134"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'POSITIVE', 'score': 0.9998430013656616}, {'label': 'NEGATIVE', 'score': 0.5758727192878723}, {'label': 'NEGATIVE', 'score': 0.9955743551254272}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "classifier_v2 = pipeline(\"sentiment-analysis\", model =  model, tokenizer=tokenizer)\n",
        "result = classifier_v2(test_many)\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F9RfOhg631hY",
        "outputId": "1722bec2-0088-4103-af8b-0e012bcbbf5a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'POSITIVE', 'score': 0.9998430013656616}, {'label': 'NEGATIVE', 'score': 0.5758727192878723}, {'label': 'NEGATIVE', 'score': 0.9955743551254272}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = tokenizer.tokenize(test_one)\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "input_ids = tokenizer(test_one)\n",
        "\n",
        "\n",
        "print(f\"Tokens: {tokens}\")\n",
        "print(f\"Token IDs: {token_ids}\")\n",
        "print(f\"Input IDs: {input_ids}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sAQOnZW4owv",
        "outputId": "4f4a2015-d460-41ab-ceb4-6b4d3b6b748c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokens: ['i', 'can', \"'\", 't', 'believe', 'that', 'i', 'got', 'the', 'part']\n",
            "Token IDs: [1045, 2064, 1005, 1056, 2903, 2008, 1045, 2288, 1996, 2112]\n",
            "Input IDs: {'input_ids': [101, 1045, 2064, 1005, 1056, 2903, 2008, 1045, 2288, 1996, 2112, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch = tokenizer(test_many, padding=True, truncation=True, max_length=512, return_tensors='pt')\n",
        "print(f\"Input IDs: {batch}\")\n",
        "\n",
        "with torch.no_grad():\n",
        "    outputs = model(**batch)\n",
        "    print(f\"Outputs: {outputs}\")\n",
        "    predictions = F.softmax(outputs.logits, dim=1)\n",
        "    print(f\"Predictions: {predictions}\")\n",
        "    labels = torch.argmax(predictions, dim = 1)\n",
        "    print(f\"Labels 1: {labels}\")\n",
        "    labels = [model.config.id2label[label_id] for label_id in labels.tolist()]\n",
        "    print(f\"Labels 2: {labels}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-j9xYF66fVk",
        "outputId": "43d7af46-40f8-4d1c-effb-e89a0c458eaf"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input IDs: {'input_ids': tensor([[ 101, 2023, 2003, 3492, 2204,  102,    0],\n",
            "        [ 101, 1045, 2123, 2102, 5223, 2009,  102],\n",
            "        [ 101, 2045, 2003, 2205, 2172, 5474,  102]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 0],\n",
            "        [1, 1, 1, 1, 1, 1, 1],\n",
            "        [1, 1, 1, 1, 1, 1, 1]])}\n",
            "Outputs: SequenceClassifierOutput(loss=None, logits=tensor([[-4.1858,  4.5737],\n",
            "        [ 0.2723, -0.0335],\n",
            "        [ 2.9216, -2.4943]]), hidden_states=None, attentions=None)\n",
            "Predictions: tensor([[1.5694e-04, 9.9984e-01],\n",
            "        [5.7587e-01, 4.2413e-01],\n",
            "        [9.9557e-01, 4.4256e-03]])\n",
            "Labels 1: tensor([1, 0, 0])\n",
            "Labels 2: ['POSITIVE', 'NEGATIVE', 'NEGATIVE']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.save_pretrained(\"saved_hf\")\n",
        "model.save_pretrained(\"saved_hf\")"
      ],
      "metadata": {
        "id": "fW6w4sIZ6w14"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = 'oliverguhr/german-sentiment-bert'\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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            "4e121b1e148844328caf9089b118c2c9"
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        "id": "v8av648D47Mv",
        "outputId": "be90d166-4f32-4b9c-b629-135919759f63"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ec239feb2d6f412b8d8aa40832094427"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_many_german = [\"Das ist ziemlich gut\", \"Ich hasse es nicht\", \"Es ist zu viel Salz\"]"
      ],
      "metadata": {
        "id": "33QaSiGM-j0r"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch = tokenizer(test_many_german, padding=True, truncation=True, max_length=512, return_tensors='pt')\n",
        "batch = torch.tensor(batch['input_ids'])\n",
        "print(f\"Input IDs: {batch}\")\n",
        "\n",
        "with torch.no_grad():\n",
        "    outputs = model(batch)\n",
        "    print(f\"Outputs: {outputs}\")\n",
        "    predictions = F.softmax(outputs.logits, dim=1)\n",
        "    print(f\"Predictions: {predictions}\")\n",
        "    labels = torch.argmax(predictions, dim = 1)\n",
        "    print(f\"Labels 1: {labels}\")\n",
        "    labels = [model.config.id2label[label_id] for label_id in labels.tolist()]\n",
        "    print(f\"Labels 2: {labels}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zaJ2jtz3_B99",
        "outputId": "3d8f1b38-8d9b-49b3-85bf-fc75a8fac572"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-16ee9002fba8>:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  batch = torch.tensor(batch['input_ids'])\n",
            "We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input IDs: tensor([[    3,   295,   127,  9431,  1522,     4,     0],\n",
            "        [    3,  1671,   312, 11370,   229,   149,     4],\n",
            "        [    3,   482,   127,    81,   870,  4979,     4]])\n",
            "Outputs: SequenceClassifierOutput(loss=None, logits=tensor([[ 3.6143,  0.1325, -4.7010],\n",
            "        [ 3.0149,  0.2193, -3.6925],\n",
            "        [-0.8959,  2.3623, -1.9595]]), hidden_states=None, attentions=None)\n",
            "Predictions: tensor([[9.6994e-01, 2.9826e-02, 2.3738e-04],\n",
            "        [9.4135e-01, 5.7495e-02, 1.1502e-03],\n",
            "        [3.6567e-02, 9.5081e-01, 1.2623e-02]])\n",
            "Labels 1: tensor([0, 0, 1])\n",
            "Labels 2: ['positive', 'positive', 'negative']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WgEaSI70_jdJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}