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  },
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Shakilkhan24/Playground_DL/blob/main/playing_with_hf_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      },
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Installing collected packages: dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.16.1 dill-0.3.7 multiprocess-0.70.15\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets"
      ]
    },
    {
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      "source": [
        " MICROSOFT RESEARCH PARAPHRASE CORPUS\n",
        "\n",
        "\n",
        " GLUE --- KIND OF A TEXT CLASSIFIER DATASET (FREQUENTLY USED FOR CALC. diff MODELS' PERFORMANCE)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "_uUJ9n9ffcWS"
      }
    },
    {
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      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
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          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/75.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "02f384ec4e5945e6a2b29ef79b6c6bb2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/308k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "790e68cb919b47fd83e8e6411265e416"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/3668 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "221a130e643d447c8de4d505e47728b6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/408 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c5690f03e3e44b587356ad84796473e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1725 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8630ac6c6e1f4feb9d216ce28cefd8e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "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",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(raw_datasets)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvcVkOXPdfdL",
        "outputId": "23b9f7de-3df3-4603-a5c6-0887b66c1711"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets['train'].features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7LA0O-RbhFF-",
        "outputId": "25280136-d058-4485-9b80-2319f971b9bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence1': Value(dtype='string', id=None),\n",
              " 'sentence2': Value(dtype='string', id=None),\n",
              " 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None),\n",
              " 'idx': Value(dtype='int32', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "See what datasets looking like ____"
      ],
      "metadata": {
        "id": "exsm-16Pgqll"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets['train'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XLQETeyhgFgF",
        "outputId": "1ff66820-3011-45d3-8fe6-fc74f0d0c00c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exploring data"
      ],
      "metadata": {
        "id": "k-o5swNjejfz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets['train']  # 3668 training examples __ ROW BY ROW__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XHHMbqYbewfL",
        "outputId": "711507e7-f9a7-4517-8bdb-dff24f367d7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
              "    num_rows: 3668\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Because of long length of sentence, splitted into two, but label is one"
      ],
      "metadata": {
        "id": "T4xlAw6Jgjs2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets['train'].features['label']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2feRhKGd0EZ",
        "outputId": "a290acd7-0bd7-488d-9251-31be3da91e18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ClassLabel(names=['not_equivalent', 'equivalent'], id=None)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "Prepraing my raw dataset .\n",
        "(tokenization process)\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "poHIO8Injnyk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(example):\n",
        "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363,
          "referenced_widgets": [
            "0bd98bcc4e0b4942a7991b241c7b51a1",
            "68bd2f4a1a144b4b851fafba6f22a878",
            "9088dcd43f274c6baa8acc6d03c3ce22",
            "5e06f610505c4cccb83ff8015d393648",
            "6a55bc9f71874df9b24de023ebfbf7f9",
            "24778f2873c6491bada9e1d88dbecd51",
            "d7511cafaafb4a88a36f8748d0eacd04",
            "2bc9fbb697b341cd8c174ea8a1b35013",
            "9c9011f27e294b46b35a41a27f85fd78",
            "a980dd2f3563465fabc2e7002a810898",
            "723632ece81b4bb5aa0c4587bda27ccb",
            "15772753202245639d319b904f614ef9",
            "971631b947e84f4ebe9913923a6c93b3",
            "f0ad10dfc378428381aa25f3d6c49912",
            "a5ae7d97c4ec4d23b9c9de70471a84b5",
            "6ca80cb86a6e4c87baf37bfaa0fe61be",
            "bcdebd7207a94364b150b7d11e43f55f",
            "3395ef23630347c9b6ef5fca0d0a4e62",
            "7ea205430b644883b7bdc4dc25e273b8",
            "82be4ba51e714a24ab3b108a9a7cbf68",
            "c8e10b2da6644312b0a7583a313123e1",
            "20df3b92780c49bda697923cf2bbb8f8",
            "c8a9949f17be45c8b2301b662adec894",
            "0c63321f097f4aac86231f9d1daf5968",
            "8fddcf3e8cdb42648363ab1cab221bba",
            "3d3b6adcd80b47da9a712a97b8b8d41b",
            "a8e8bb9264934deeb6a284d7a8440c2b",
            "b964dee805714884b3069b54a57ebac4",
            "d85ae899ede54c23bf557a095cadee4a",
            "a5f618aaba064513a9111512ed7d4980",
            "e2f632c39c224c2d9c6e469e5fe57cf7",
            "99261fc274734b46ae74d7020a96bef0",
            "594ca2a5172746c189dc93f93043502b"
          ]
        },
        "id": "OY8slm8tj0iO",
        "outputId": "94bdfea6-639f-4402-aac7-213d8a4964a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3668 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0bd98bcc4e0b4942a7991b241c7b51a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/408 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15772753202245639d319b904f614ef9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1725 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8a9949f17be45c8b2301b662adec894"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 3668\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 408\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 1725\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenized_datasets)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gvb6pDtDkFuB",
        "outputId": "b4f61e8d-ba96-4d5b-a2b2-490b2a8e22f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
            "        num_rows: 3668\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
            "        num_rows: 408\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
            "        num_rows: 1725\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertConfig, BertModel\n",
        "\n",
        "# Building the config\n",
        "config = BertConfig()\n",
        "\n",
        "# Building the model from the config\n",
        "model = BertModel(config)\n"
      ],
      "metadata": {
        "id": "qLolz0wEOCwS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "er245Ni0OFb1",
        "outputId": "24d2bd8f-ff13-4fb2-c4ea-7f3e20cd6a79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUvXnyq8P1Ol",
        "outputId": "5d3d9e97-ee13-4e1b-ee48-99f918f4ed9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BertConfig {\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.35.2\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(config.hidden_size)\n",
        "print(config.vocab_size)\n",
        "print(config.num_attention_heads)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "snRMQtg4VHn_",
        "outputId": "2baafb58-947f-4420-8774-849969082f43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "768\n",
            "30522\n",
            "12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "config.attention_probs_dropout_prob=0.2     # in this way , we can change the config also ..\n",
        "# then building model in this way , and traing in my way on my custom dataset"
      ],
      "metadata": {
        "id": "Kva_lQbhViTQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TE2fdyD4VrjO",
        "outputId": "ce3d2934-72d3-4ee5-a104-1ec5c3f090f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BertConfig {\n",
            "  \"attention_probs_dropout_prob\": 0.2,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.35.2\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer=AutoTokenizer.from_pretrained('bert-base-cased')"
      ],
      "metadata": {
        "id": "mIuEu6v7QoBz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "txt='hey there are you?'\n",
        "tokens=tokenizer.tokenize(txt)"
      ],
      "metadata": {
        "id": "C6pP62SFTozL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs_overall=tokenizer(txt,padding=True,truncation=True,return_tensors='pt')"
      ],
      "metadata": {
        "id": "FDm2x-1pUlGM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(inputs_overall.keys())\n",
        "print(inputs_overall.values())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLrje5gnUudN",
        "outputId": "08e974b4-b9f5-4388-f728-eda246d95055"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['input_ids', 'token_type_ids', 'attention_mask'])\n",
            "dict_values([tensor([[  101,  4931,  2045,  2024,  2017, 27427, 13699, 10497,  4630,  2000,\n",
            "          2831,  2055,  2505,  1999,  2115,  2406,   102]]), tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "token_type_ids differs tokens , are they from the\n",
        "same sentence or not. For sentence one if 0, then sentence two is 1.\n",
        "\n",
        "```\n",
        "['sentence1','sentence2']\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "8Le67w5sia7m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(inputs_overall['token_type_ids'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vqO88I4-h0ll",
        "outputId": "be5fafe9-bfb5-4a84-e6f6-af3fadfd7756"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokens)    # sub_word_tokenization"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IaP74LPVT1CF",
        "outputId": "f64207ed-9cfc-4540-9ae9-9ff9f79fc958"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['hey', 'there', 'are', 'you', 'ind', '##ep', '##end', '##ant', 'to', 'talk', 'about', 'anything', 'in', 'your', 'country']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ids=tokenizer.convert_tokens_to_ids(tokens)\n",
        "print(ids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q5d1eKZaT-sv",
        "outputId": "ff61a696-ce42-497f-92b9-92dab30e4491"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4931, 2045, 2024, 2017, 27427, 13699, 10497, 4630, 2000, 2831, 2055, 2505, 1999, 2115, 2406]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "return_tokens=tokenizer.decode(ids)\n",
        "print(return_tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GElD0pyAUOzs",
        "outputId": "a8c23559-3731-4f15-9075-94ecee5d2f85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hey there are you independant to talk about anything in your country\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer, BertForSequenceClassification\n",
        "import torch\n",
        "\n",
        "# Load pre-trained BERT tokenizer and model\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Input text for prediction\n",
        "text = \"This is a sample text for prediction.\"\n",
        "\n",
        "# Tokenize input text and convert to PyTorch tensors\n",
        "inputs = tokenizer(text, return_tensors=\"pt\")\n",
        "\n",
        "# Forward pass through the model\n",
        "outputs = model(**inputs)\n",
        "\n",
        "# Get predicted probabilities for each class\n",
        "probs = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
        "\n",
        "# Get the predicted class index (argmax of the probabilities)\n",
        "predicted_class = torch.argmax(probs, dim=-1).item()\n",
        "\n",
        "# Print the predicted class and probabilities\n",
        "print(f\"Predicted Class: {predicted_class}\")\n",
        "print(\"Class Probabilities:\", probs.tolist())\n"
      ],
      "metadata": {
        "id": "MsM_NimZRHD4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model(input_ids,attention_mask)"
      ],
      "metadata": {
        "id": "y4tr6oNDWxpq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "inHj5KYUTzwk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4qX6ay68QOMU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "class BertSelfAttention(nn.Module):\n",
        "    def __init__(self, hidden_size):\n",
        "        super(BertSelfAttention, self).__init__()\n",
        "        self.query = nn.Linear(hidden_size, hidden_size)\n",
        "        self.key = nn.Linear(hidden_size, hidden_size)\n",
        "        self.value = nn.Linear(hidden_size, hidden_size)\n",
        "        self.dropout = nn.Dropout(0.1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        query = self.query(x)\n",
        "        key = self.key(x)\n",
        "        value = self.value(x)\n",
        "\n",
        "        attention_scores = torch.matmul(query, key.transpose(-1, -2)) / torch.sqrt(x.size(-1))\n",
        "        attention_probs = nn.functional.softmax(attention_scores, dim=-1)\n",
        "        attention_output = torch.matmul(self.dropout(attention_probs), value)\n",
        "\n",
        "        return attention_output\n",
        "\n",
        "\n",
        "class BertLayer(nn.Module):\n",
        "    def __init__(self, hidden_size):\n",
        "        super(BertLayer, self).__init__()\n",
        "        self.attention = BertSelfAttention(hidden_size)\n",
        "        self.intermediate = nn.Sequential(\n",
        "            nn.Linear(hidden_size, 4 * hidden_size),\n",
        "            nn.GELU()\n",
        "        )\n",
        "        self.output = nn.Sequential(\n",
        "            nn.Linear(4 * hidden_size, hidden_size),\n",
        "            nn.LayerNorm(hidden_size, eps=1e-12, elementwise_affine=True),\n",
        "            nn.Dropout(0.1)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        attention_output = self.attention(x)\n",
        "        intermediate_output = self.intermediate(attention_output)\n",
        "        layer_output = self.output(intermediate_output + attention_output)\n",
        "\n",
        "        return layer_output\n",
        "\n",
        "\n",
        "class BertEncoder(nn.Module):\n",
        "    def __init__(self, hidden_size, num_layers):\n",
        "        super(BertEncoder, self).__init__()\n",
        "        self.layer = nn.ModuleList([BertLayer(hidden_size) for _ in range(num_layers)])\n",
        "\n",
        "    def forward(self, x):\n",
        "        for layer in self.layer:\n",
        "            x = layer(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class BertPooler(nn.Module):\n",
        "    def __init__(self, hidden_size):\n",
        "        super(BertPooler, self).__init__()\n",
        "        self.dense = nn.Linear(hidden_size, hidden_size)\n",
        "        self.activation = nn.Tanh()\n",
        "\n",
        "    def forward(self, x):\n",
        "        pooled_output = self.activation(self.dense(x[:, 0]))\n",
        "        return pooled_output\n",
        "\n",
        "\n",
        "class BertModel(nn.Module):\n",
        "    def __init__(self, hidden_size=768, num_layers=12):\n",
        "        super(BertModel, self).__init__()\n",
        "        self.embeddings = nn.Sequential(\n",
        "            nn.Embedding(30522, hidden_size, padding_idx=0),\n",
        "            nn.Embedding(512, hidden_size),\n",
        "            nn.Embedding(2, hidden_size),\n",
        "            nn.LayerNorm(hidden_size, eps=1e-12, elementwise_affine=True),\n",
        "            nn.Dropout(0.1)\n",
        "        )\n",
        "        self.encoder = BertEncoder(hidden_size, num_layers)\n",
        "        self.pooler = BertPooler(hidden_size)\n",
        "\n",
        "    def forward(self, input_ids):\n",
        "        embedded = self.embeddings(input_ids)\n",
        "        encoder_output = self.encoder(embedded)\n",
        "        pooled_output = self.pooler(encoder_output)\n",
        "\n",
        "        return encoder_output, pooled_output\n",
        "\n",
        "# Instantiate the model\n",
        "model = BertModel()\n"
      ],
      "metadata": {
        "id": "6pYHjlKyOHC0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained('bert-base-cased', num_labels=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "faf378578e1c4e7da7e7b1ccbdf3f08b",
            "5ee9eb8fd72c4a1883899c477cc67531",
            "8543402968c04838bed9b1c380e053df",
            "53a1a6ac170a4cf0aa69f711a3b12426",
            "bb2c55d04be94480b31d5940c3c6b090",
            "c77a6a6c8bcd43fabeb371862f7352c0",
            "802dd1d8444f4985b06824f99d5de9fe",
            "669894cff2e94e5fb5203db82d1f2d24",
            "8230b5f71cbf4129b89dd25be89b06fd",
            "78c840746742421bac70f34c180d34ee",
            "0079e6eec85c458fb6a1a385dca8ed48"
          ]
        },
        "id": "9Q5T65e9mWga",
        "outputId": "69d7462f-9df9-49ca-b267-47087639698b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "faf378578e1c4e7da7e7b1ccbdf3f08b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized:\n",
        "['classifier.weight', 'classifier.bias']\n",
        "\n",
        "You should probably TRAIN this model on a down-stream task\n",
        " to be able to use it for predictions and inference.\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "dXh6K-6Emtxh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "since we are going to say , num_labels=2 ,\n",
        "that's why the classification head is reset to classify num_of_classes\n",
        "that's mean, it's no longer pre-trained .\n",
        "You need to train this on your custom dataset\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "myMAw360nVrR"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aOcv-uJ8m0-t"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}