Transformers_with_TensorFlow.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Transformers for TensorFlow"
      ],
      "metadata": {
        "id": "ExKImxEdClX8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Loading Dataset"
      ],
      "metadata": {
        "id": "5GLLA_G6CqRh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "# Installing the \"datasets\" library\n",
        "!pip install -q datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the necessary function to load a dataset\n",
        "from datasets import load_dataset\n",
        "\n",
        "# Loading the \"rotten_tomatoes\" dataset\n",
        "dataset = load_dataset(\"rotten_tomatoes\")"
      ],
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        "outputId": "e0a465e5-39ae-4915-96ca-8cf450ef6bb1"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/7.46k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "train.parquet:   0%|          | 0.00/699k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "validation.parquet:   0%|          | 0.00/90.0k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "4e0249c2e07341d29207c555d2783fbe"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "test.parquet:   0%|          | 0.00/92.2k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8ecf66dbe0b484dbeb3aeb02bb6e554"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/8530 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41104ad5e9b243188ee70b4fcd32591c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/1066 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e69aeab0db6f40f69c9369a5c1519672"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1066 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Displaying the loaded dataset\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGxmunY9C2hW",
        "outputId": "14a6b3ad-bd67-4b95-c996-6b6f912146bd"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 8530\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 1066\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 1066\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Accessing the first example from the test split of the dataset\n",
        "dataset[\"test\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_IlGhhsTC5KI",
        "outputId": "b69b94d4-62e7-4501-b738-2904083c2f5c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'lovingly photographed in the manner of a golden book sprung to life , stuart little 2 manages sweetness largely without stickiness .',\n",
              " 'label': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Data Preprocessing"
      ],
      "metadata": {
        "id": "Vp0lTtqoC8li"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the tokenizer for a pre-trained model\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "# Initializing the tokenizer for the \"distilbert-base-uncased\" model\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")"
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            "b1075c50c72e43db97ff321f70abd05a",
            "dfb55eb7f35c463a8a1af87fa08b0368",
            "d97a1af58abe4522af684b1198f2862f",
            "e14c4b342a044207a823a54e60f11815",
            "9563bda9719946bfbdcf2cd9d079a40b",
            "e0d6700f9108433196de72d9f3c38891",
            "f61166c1279b45c786fcdf2537c95403",
            "d16057de7f1948029d0ff6646517e16f",
            "370a15d11caa4d61af7665c6a2bf543e",
            "8afd9ca49721415d91dcb3f83a41caa0"
          ]
        },
        "id": "bcAFPJNNC_PB",
        "outputId": "8ece3dea-adbe-4779-92c8-5bfe10b8a0d8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "545b935d3d344ad78251bdb182594c8f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b12e1200e17141ca95a21fec1e742702"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ef29a1a8a0b4f8aa5854617c81f0b0c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b95f97365d1c47e68fe426d0ff7f4326"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0c34b13877046398a86f3d2be797ae3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenizing the text of the first training example\n",
        "tokenizer(dataset[\"train\"][0][\"text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lswBbDBiDCMR",
        "outputId": "9999f38c-9d8e-48e1-994b-0a234cf660b1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 1996, 2600, 2003, 16036, 2000, 2022, 1996, 7398, 2301, 1005, 1055, 2047, 1000, 16608, 1000, 1998, 2008, 2002, 1005, 1055, 2183, 2000, 2191, 1037, 17624, 2130, 3618, 2084, 7779, 29058, 8625, 13327, 1010, 3744, 1011, 18856, 19513, 3158, 5477, 4168, 2030, 7112, 16562, 2140, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing function for tokenization\n",
        "def preprocess_function(examples):\n",
        "    return tokenizer(examples[\"text\"], truncation=True)"
      ],
      "metadata": {
        "id": "M2RZPTLUDFSN"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Applying the preprocessing function to the entire dataset in batches\n",
        "dataset = dataset.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "b10040623687425cacf48001d6931ae3",
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            "febd8f9b6af9488c9bb1a446df90a965",
            "0963751259c14b999c34444b2698ab49",
            "f35f0ad947d249b0ac696cf880b4ade4",
            "e8fc48eea4f3491583ab0e2361f3768f",
            "f7dd77a3e1ed45d0ae086171bd864eb5",
            "4030493945fd4dd1aa3b0666ac6e682b",
            "4e1cae970cfe453ea9d89d25a904baec",
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            "434f57f96ab94fd697386eb2f7eedf0e",
            "0745fb1cb891448db724514708c7efd7",
            "e0dfb9c4b1f640c383667277611e519f",
            "8f74787964d5467e883700f15b211e6b",
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            "f47fc84daa85474ea3798ceac6a27cab",
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            "3646f0a0eeaf4e0499f7d20acaf191df",
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            "73f0f92fd1ee488e84a209922809ccab",
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            "3d6eca1066b64e049c07658d8e43004b",
            "1089d0e587cf4641add73e837946460d",
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            "6daec27b67464763bd5e2f809a00f64e"
          ]
        },
        "id": "JvA-zmIKDH25",
        "outputId": "e9892ef9-6cae-4ff7-a780-f301fc41c3dc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/8530 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b10040623687425cacf48001d6931ae3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1066 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "352fbbd0179e41858c5e861f5c7462c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1066 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3646f0a0eeaf4e0499f7d20acaf191df"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the data collator with padding\n",
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "# Initializing the data collator with padding\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
      ],
      "metadata": {
        "id": "X0XO9p-zDLEe"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Model Loading"
      ],
      "metadata": {
        "id": "FGS4m8a5DNc5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the TensorFlow version of the model for sequence classification\n",
        "from transformers import TFAutoModelForSequenceClassification\n",
        "\n",
        "# Initializing a model for sequence classification using \"distilbert-base-uncased\"\n",
        "my_model = TFAutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "9407a0a7ff0f48789dcf54f8dd4c5dc3",
            "923e664514eb4b8faa68765a66f905ff",
            "f826dff5a845451aa5cb7fcd1a1a654b",
            "2242ed3bd4f84d5fa7a98d1b7ff2f2a9",
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            "0bb50464da0f476482eb6c445ddb4f74",
            "97ddf828ac284fe6b39dd90e0a4e3fcd"
          ]
        },
        "id": "m4I45X0vDQJA",
        "outputId": "9b426cd4-eb5a-493e-98d3-a47061b555de"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9407a0a7ff0f48789dcf54f8dd4c5dc3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForSequenceClassification: ['vocab_projector.bias', 'vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_layer_norm.weight', 'vocab_transform.bias']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights or buffers of the TF 2.0 model TFDistilBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', '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": "code",
      "source": [
        "# Preparing the training dataset as a TensorFlow dataset\n",
        "tf_train_set = my_model.prepare_tf_dataset(\n",
        "    dataset[\"train\"],\n",
        "    shuffle=True,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "\n",
        "# Preparing the validation dataset as a TensorFlow dataset\n",
        "tf_validation_set = my_model.prepare_tf_dataset(\n",
        "    dataset[\"validation\"],\n",
        "    shuffle=False,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")"
      ],
      "metadata": {
        "id": "fUR61Pe_DT8S"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Model Training"
      ],
      "metadata": {
        "id": "BAuaxrPBDbNP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "IQUIPBEhDaJm"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model\n",
        "my_model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MTilV_4ADfGT",
        "outputId": "2cd2021b-fbc9-4af9-9d78-ebfcc6ba28fa"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "533/533 [==============================] - 83s 110ms/step - loss: 0.7081 - accuracy: 0.5057 - val_loss: 0.7012 - val_accuracy: 0.5000\n",
            "Epoch 2/2\n",
            "533/533 [==============================] - 52s 97ms/step - loss: 5.9467 - accuracy: 0.5021 - val_loss: 7.6686 - val_accuracy: 0.5000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf_keras.src.callbacks.History at 0x7ec5c6235f90>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Prediction"
      ],
      "metadata": {
        "id": "AmqwwPeqDhcd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining a text for inference\n",
        "text = \"I love AI. It's fun to analyze the NLP tasks with Hugging Face.\""
      ],
      "metadata": {
        "id": "8OkXqcbWDlG9"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenizing the text for inference\n",
        "tokenized_text = tokenizer(text, return_tensors=\"tf\")\n",
        "tokenized_text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJn1HHBuDqCK",
        "outputId": "6b253f4b-f18a-401e-ebb0-47bfd363d1e2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': <tf.Tensor: shape=(1, 20), dtype=int32, numpy=\n",
              "array([[  101,  1045,  2293,  9932,  1012,  2009,  1005,  1055,  4569,\n",
              "         2000, 17908,  1996, 17953,  2361,  8518,  2007, 17662,  2227,\n",
              "         1012,   102]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(1, 20), dtype=int32, numpy=\n",
              "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],\n",
              "      dtype=int32)>}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtaining model logits for the tokenized text\n",
        "logits = my_model(**tokenized_text).logits"
      ],
      "metadata": {
        "id": "X2Hx-ALCDtHQ"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the math module from TensorFlow\n",
        "from tensorflow import math\n",
        "\n",
        "# Finding the index of the class with the highest logit score\n",
        "int(math.argmax(logits, axis=-1)[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nbifDz_DwLZ",
        "outputId": "841c0bd2-c3a8-47c8-bbda-a6b5c4805461"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2GE3H3SOGQdq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}