{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "joTaXUsFAwxg",
        "outputId": "58b4978a-fc71-4b78-f002-f7c0c2e56ebb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting datasets\n",
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            "\u001b[?25hCollecting multiprocess (from datasets)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.18.0 dill-0.3.8 multiprocess-0.70.16 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "# Transformers installation\n",
        "! pip install transformers datasets\n",
        "# To install from source instead of the last release, comment the command above and uncomment the following one.\n",
        "# ! pip install git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRRjAH_ZAwxi"
      },
      "source": [
        "# Text classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "hide_input": true,
        "id": "mJcVlLqoAwxj",
        "outputId": "0110dcb9-d1b2-4c73-dc83-a4d38de20c51"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/leNG9fN9FQU?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@title\n",
        "from IPython.display import HTML\n",
        "\n",
        "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/leNG9fN9FQU?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xi5wf8QxAwxk"
      },
      "source": [
        "Text classification is a common NLP task that assigns a label or class to text. Some of the largest companies run text classification in production for a wide range of practical applications. One of the most popular forms of text classification is sentiment analysis, which assigns a label like 🙂 positive, 🙁 negative, or 😐 neutral to a sequence of text.\n",
        "\n",
        "This guide will show you how to:\n",
        "\n",
        "1. Finetune [DistilBERT](https://huggingface.co./distilbert-base-uncased) on the [IMDb](https://huggingface.co./datasets/imdb) dataset to determine whether a movie review is positive or negative.\n",
        "2. Use your finetuned model for inference.\n",
        "\n",
        "<Tip>\n",
        "The task illustrated in this tutorial is supported by the following model architectures:\n",
        "\n",
        "<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->\n",
        "\n",
        "[ALBERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/albert), [BART](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/bart), [BERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/bert), [BigBird](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/big_bird), [BigBird-Pegasus](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/bigbird_pegasus), [BioGpt](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/biogpt), [BLOOM](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/bloom), [CamemBERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/camembert), [CANINE](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/canine), [ConvBERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/convbert), [CTRL](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/ctrl), [Data2VecText](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/data2vec-text), [DeBERTa](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/deberta), [DeBERTa-v2](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/deberta-v2), [DistilBERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/distilbert), [ELECTRA](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/electra), [ERNIE](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/ernie), [ErnieM](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/ernie_m), [ESM](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/esm), [FlauBERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/flaubert), [FNet](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/fnet), [Funnel Transformer](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/funnel), [GPT-Sw3](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gpt-sw3), [OpenAI GPT-2](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gpt2), [GPTBigCode](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gpt_bigcode), [GPT Neo](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gpt_neo), [GPT NeoX](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gpt_neox), [GPT-J](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/gptj), [I-BERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/ibert), [LayoutLM](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/layoutlm), [LayoutLMv2](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/layoutlmv2), [LayoutLMv3](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/layoutlmv3), [LED](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/led), [LiLT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/lilt), [LLaMA](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/llama), [Longformer](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/longformer), [LUKE](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/luke), [MarkupLM](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/markuplm), [mBART](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/mbart), [MEGA](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/mega), [Megatron-BERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/megatron-bert), [MobileBERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/mobilebert), [MPNet](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/mpnet), [MVP](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/mvp), [Nezha](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/nezha), [Nyströmformer](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/nystromformer), [OpenLlama](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/open-llama), [OpenAI GPT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/openai-gpt), [OPT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/opt), [Perceiver](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/perceiver), [PLBart](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/plbart), [QDQBert](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/qdqbert), [Reformer](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/reformer), [RemBERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/rembert), [RoBERTa](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/roberta), [RoBERTa-PreLayerNorm](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/roberta-prelayernorm), [RoCBert](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/roc_bert), [RoFormer](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/roformer), [SqueezeBERT](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/squeezebert), [TAPAS](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/tapas), [Transformer-XL](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/transfo-xl), [XLM](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/xlm), [XLM-RoBERTa](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/xlm-roberta), [XLM-RoBERTa-XL](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/xlm-roberta-xl), [XLNet](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/xlnet), [X-MOD](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/xmod), [YOSO](https://huggingface.co./docs/transformers/main/en/tasks/../model_doc/yoso)\n",
        "\n",
        "\n",
        "<!--End of the generated tip-->\n",
        "\n",
        "</Tip>\n",
        "\n",
        "Before you begin, make sure you have all the necessary libraries installed:\n",
        "\n",
        "```bash\n",
        "pip install transformers datasets evaluate\n",
        "```\n",
        "\n",
        "We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "45ca9b09528043dda2319aad7daaa6fc",
            "12a2b94c4b484408bfe25e53f82b03a2",
            "c51ec2777b5a442691e6d321c01587a7",
            "562706b999e64397a62dac2b1ecca2f4",
            "fdfff33cd4b747d5b429c686f7698109",
            "6e9ba47d02b74d79a051fd4f93003898",
            "04a92d7f92404bd6b02c7cb3f9ad9ea2",
            "e7ea0688809a4398a630625b2d55bf43",
            "5106d26986504884b35be9e9c060ffbc",
            "4f100334000443119a0b4f3c01eedb26",
            "3b840a16f81647e890f63bc1c74fcfb7",
            "e33d15b12c454bc6992432191cde43f3",
            "30b432685bad4bc3840c27f3dc9a601e",
            "d93d01bc2ac84bf2a3d2150a039b37aa",
            "41eb20b10e6444689ee24a1c5d1ea05a",
            "5e9768b2fbe74d069749f7f16ada624b",
            "62b27bad20ef4caf9fcfe689bc36c898",
            "1b8807553f544094a0766b8d0ef903ef",
            "f778710c6025428faaa800b54153c482",
            "a73001b7e4734e77a3053c5ed9cb3973",
            "4c9d73349dfc4d0ea4f5df1a06d6439f",
            "2e4d761451364692b6bef51eb73aa625",
            "11535c67ade84a1683d0b0dd34b56eaf",
            "5cd9b00387c84890ab7a80cd7191e119",
            "574bd83e55a641dba44a0aacf976f725",
            "7276bf4f09d94390a1eba8c2f59fd1fd",
            "d1909a4c3cd04d5cac53f411e99055e9",
            "aa002452216d4f819acf7e93eaa4eb8a",
            "179777e9abec4859aacbaac1c2c8b142",
            "aced0c3a2c2f4520bc278286ea97fac5",
            "035e78173d184616a5731f4333f89247",
            "46341b20b32a468f85a91a33dd7ef07c"
          ]
        },
        "id": "0zrBI8sBAwxl",
        "outputId": "089ff3a0-c92e-4954-bd58-f71784b2204b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co./front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45ca9b09528043dda2319aad7daaa6fc"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8FNCFxCtAwxl"
      },
      "source": [
        "## Load IMDb dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95K1dXgDAwxl"
      },
      "source": [
        "Start by loading the IMDb dataset from the 🤗 Datasets library:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367,
          "referenced_widgets": [
            "403ab1bb44154947b9fd2b2c1f56f6eb",
            "b191ee033d2d4d11a1ff761d92df14c7",
            "3044075fa81146c5b72af2a09da00933",
            "b1d8c0fd72a142c192a5cb3d5bfbce71",
            "908f277e3a7e4c4ab10743c191e6fa89",
            "5c15009b0b2141b5ba34681dbb9b7571",
            "72af3e1624f34b6eaf7e240ad7e0b6c8",
            "76d2697a565f42bf951174b8f858b690",
            "909e3b9fb5844aadbc96a3833ed079a1",
            "bf63752903ce4833b5eaf275618f44ff",
            "42cb3070d9bf4fe6abfb49894d9ad4a3",
            "e43a4f550c9446c8ba019015d7c75ddf",
            "6b0d29c8745848738c25789a6d3f7703",
            "7bb72377065d47edad73e4165a777f5e",
            "8d29e9ced4dc48128533eee8c986c6f7",
            "b995c44fb2ab44b6b0525963dc0d9cfa",
            "506117fce50f4bd0bc160886520ab89f",
            "6eda7cea1aa742c4877fcfc022e966f9",
            "ff9fdb555800418bba3bf07b152b177b",
            "61b3c9aa7f5242c4997094b37a91aeb9",
            "c4bfcb933fb443b6aa036bd454d5d6ba",
            "2202539907f6432694302359cf87909e",
            "aabacf5d5d904efe9ed4e7ee14709606",
            "d4e81d63e0db435f8346754fdd25d318",
            "d93808b8d0644b6d9709556f5fe7a311",
            "672bdba037f548a8967a39f1874d5e13",
            "72bd560170f9434c8eec7fbdfe26f479",
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          ]
        },
        "id": "O8bfMi1QAwxm",
        "outputId": "7aa55fff-492c-460e-fe79-e208e53b03ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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": [
              "Downloading readme:   0%|          | 0.00/9.06k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "403ab1bb44154947b9fd2b2c1f56f6eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/1.97M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e43a4f550c9446c8ba019015d7c75ddf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/248k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aabacf5d5d904efe9ed4e7ee14709606"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65e315b448f04cb48d4918ebf61d0eef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "420b824e6f10485d8026f1303ec61bda"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c41e09fdd8c441c7b13214a68e50f326"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "216670d9fb064ebc83a9436a42a6c44a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"AdamCodd/emotion-balanced\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ze_sJzQUAwxm"
      },
      "source": [
        "Then take a look at an example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OwPvrdqmAwxm",
        "outputId": "7b1862a0-a4b5-4016-cf8b-837e7c53c694"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'i feel enraged by the amount of people participating for the chance to break things or those who treat it as a tourist event',\n",
              " 'label': 3}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "dataset[\"test\"][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hdw8rs3pAwxm"
      },
      "source": [
        "There are two fields in this dataset:\n",
        "\n",
        "- `text`: the movie review text.\n",
        "- `label`: a value that is either `0` for a negative review or `1` for a positive review."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJJBsMiKAwxm"
      },
      "source": [
        "## Preprocess"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0GR3stZDAwxn"
      },
      "source": [
        "The next step is to load a DistilBERT tokenizer to preprocess the `text` field:"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "WtiEVxXTAwxn",
        "outputId": "07212ca6-6320-4c8c-8856-6a12013d57e9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "531a4f4c81f345739f22bfca78cde898"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/615 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4778ddd29fcf44e38ba3724f5b354512"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2c1fa0f582849cfb641a7eadf324e97"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/9.10M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "b0dd141ad13f4167bd23c84f66db9974"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"xlm-roberta-base\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-OO5fhaTAwxn"
      },
      "source": [
        "Create a preprocessing function to tokenize `text` and truncate sequences to be no longer than DistilBERT's maximum input length:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "1AgB6E2kAwxn"
      },
      "outputs": [],
      "source": [
        "def preprocess_function(examples):\n",
        "    return tokenizer(examples[\"text\"], truncation=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VwyLJ9jAwxn"
      },
      "source": [
        "To apply the preprocessing function over the entire dataset, use 🤗 Datasets [map](https://huggingface.co./docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.map) function. You can speed up `map` by setting `batched=True` to process multiple elements of the dataset at once:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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        },
        "id": "cem05VFtAwxn",
        "outputId": "378a6dff-77a4-4a42-bb3f-4d5aa351389b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/16000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "903d868d50a7479bae610c6a3d0b40db"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d06d79380ba8497eac664e8c80d8a26d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4349ada310654293b47f38cc2309863a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenized_data = dataset.map(preprocess_function, batched=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkfdP9FdAwxn"
      },
      "source": [
        "Now create a batch of examples using [DataCollatorWithPadding](https://huggingface.co./docs/transformers/main/en/main_classes/data_collator#transformers.DataCollatorWithPadding). It's more efficient to *dynamically pad* the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "b9TnjASRAwxn"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZGwiWmPAwxo"
      },
      "source": [
        "## Evaluate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ysfOPL-Awxo"
      },
      "source": [
        "Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 [Evaluate](https://huggingface.co./docs/evaluate/index) library. For this task, load the [accuracy](https://huggingface.co./spaces/evaluate-metric/accuracy) metric (see the 🤗 Evaluate [quick tour](https://huggingface.co./docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0X85A1BBejk",
        "outputId": "d85fd997-d952-4340-ecd6-c19fd66ccfaf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting evaluate\n",
            "  Downloading evaluate-0.4.1-py3-none-any.whl (84 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m944.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2.18.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.25.2)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.3.8)\n",
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            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from evaluate) (4.66.2)\n",
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            "Installing collected packages: responses, evaluate\n",
            "Successfully installed evaluate-0.4.1 responses-0.18.0\n"
          ]
        }
      ],
      "source": [
        "pip install evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "8cb261a806ec4e11819c6524148d96f3",
            "ae012b210d2846e3a4b149d40981d013",
            "bfd041c9ebef482b9939e0361bd92f3c",
            "b73090259ead49b2bbd870dc8a463203",
            "f3ca415db5ad4ea8a32f4eff6fa521a8",
            "44243b4f97ac42cd95867fc8580f5db6",
            "8a784cafaef542a78a79a542503e219a",
            "6d1f4656d10c44729805d48b322ea07e",
            "738c72f878e2433e89edeebc0e01c274",
            "761108aea8a3427a9c1a81b2b7713a39",
            "de7845805e3a4ff08a45405649fe4d3d"
          ]
        },
        "id": "OlWJIwc6Awxo",
        "outputId": "92332359-65cb-46c0-f290-723158ea917e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8cb261a806ec4e11819c6524148d96f3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import evaluate\n",
        "\n",
        "accuracy = evaluate.load(\"accuracy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5Fe2iHxAwxo"
      },
      "source": [
        "Then create a function that passes your predictions and labels to [compute](https://huggingface.co./docs/evaluate/main/en/package_reference/main_classes#evaluate.EvaluationModule.compute) to calculate the accuracy:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "molPHOqrAwxo"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "\n",
        "    accuracy = accuracy_score(labels, predictions)\n",
        "    precision = precision_score(labels, predictions, average='weighted')\n",
        "    recall = recall_score(labels, predictions, average='weighted')\n",
        "    f1 = f1_score(labels, predictions, average='weighted')\n",
        "\n",
        "    return {\n",
        "        'accuracy': accuracy,\n",
        "        'precision': precision,\n",
        "        'recall': recall,\n",
        "        'f1': f1\n",
        "    }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FR3-5XWtAwxo"
      },
      "source": [
        "Your `compute_metrics` function is ready to go now, and you'll return to it when you setup your training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1FYcve-MAwxo"
      },
      "source": [
        "## Train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrrL93_2Awxo"
      },
      "source": [
        "Before you start training your model, create a map of the expected ids to their labels with `id2label` and `label2id`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "J_F65yLBAwxo"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nw24AD-fAwxo"
      },
      "source": [
        "<Tip>\n",
        "\n",
        "If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial [here](https://huggingface.co./docs/transformers/main/en/tasks/../training#train-a-tensorflow-model-with-keras)!\n",
        "\n",
        "</Tip>\n",
        "To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "nGQyRjTuAwxo"
      },
      "outputs": [],
      "source": [
        "from transformers import create_optimizer\n",
        "import tensorflow as tf\n",
        "\n",
        "batch_size = 16\n",
        "num_epochs = 5\n",
        "batches_per_epoch = len(tokenized_data[\"train\"]) // batch_size\n",
        "total_train_steps = int(batches_per_epoch * num_epochs)\n",
        "optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EjT2wNgaAwxo"
      },
      "source": [
        "Then you can load DistilBERT with [TFAutoModelForSequenceClassification](https://huggingface.co./docs/transformers/main/en/model_doc/auto#transformers.TFAutoModelForSequenceClassification) along with the number of expected labels, and the label mappings:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140,
          "referenced_widgets": [
            "eeb7cb3226964e8cb6c9ab812e508d72",
            "c787ee63a90646e0aca0e84622b2c205",
            "79c224da2ec74aa5ad9c7de14438612f",
            "939db04576a44ad9a48db8b81045bc9c",
            "630da9b7ba0c4be0a7c88f4a96c17a9a",
            "97261511cb4c4b27b30a0d2c65a12913",
            "d76cc9b02e1c402e8a6879168aac28b6",
            "f59cddd8264b4426bdb86906225fd5c0",
            "d830e86804ac41749d96ae81a846c5fc",
            "cb61dab0e460494680091550d42a1eeb",
            "b351e6b493974789aed5baf031de24b0"
          ]
        },
        "id": "Czp_lnZRAwxp",
        "outputId": "2b1cea42-c719-4583-fe98-f6c073a360f9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eeb7cb3226964e8cb6c9ab812e508d72"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFXLMRobertaForSequenceClassification.\n",
            "\n",
            "Some weights or buffers of the TF 2.0 model TFXLMRobertaForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import TFAutoModelForSequenceClassification\n",
        "\n",
        "label2int = {\n",
        "    \"sadness\": 0,\n",
        "    \"joy\": 1,\n",
        "    \"love\": 2,\n",
        "    \"anger\": 3,\n",
        "    \"fear\": 4,\n",
        "    \"surprise\": 5\n",
        "}\n",
        "\n",
        "# Create id2label dictionary by reversing key-value pairs of label2int\n",
        "id2label = {v: k for k, v in label2int.items()}\n",
        "\n",
        "# Create label2id dictionary from label2int\n",
        "label2id = label2int\n",
        "\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained(\n",
        "    \"xlm-roberta-base\", num_labels=6, id2label=id2label, label2id=label2id\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w294LOHxAwxp"
      },
      "source": [
        "Convert your datasets to the `tf.data.Dataset` format with [prepare_tf_dataset()](https://huggingface.co./docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "DKfFf5GBAwxp"
      },
      "outputs": [],
      "source": [
        "tf_train_set = model.prepare_tf_dataset(\n",
        "    tokenized_data[\"train\"],\n",
        "    shuffle=True,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "\n",
        "tf_validation_set = model.prepare_tf_dataset(\n",
        "    tokenized_data[\"validation\"],\n",
        "    shuffle=False,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")\n",
        "tf_test_set = model.prepare_tf_dataset(\n",
        "    tokenized_data[\"test\"],\n",
        "    shuffle=False,\n",
        "    batch_size=16,\n",
        "    collate_fn=data_collator,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8VInog4Awxp"
      },
      "source": [
        "Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "sMKZ-zqEAwxp"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "model.compile(optimizer=optimizer, metrics='accuracy')  # No loss argument!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpNo6NokAwxp"
      },
      "source": [
        "The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](https://huggingface.co./docs/transformers/main/en/tasks/../main_classes/keras_callbacks).\n",
        "\n",
        "Pass your `compute_metrics` function to [KerasMetricCallback](https://huggingface.co./docs/transformers/main/en/main_classes/keras_callbacks#transformers.KerasMetricCallback):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "ntlSYaNGAwxp"
      },
      "outputs": [],
      "source": [
        "from transformers.keras_callbacks import KerasMetricCallback\n",
        "\n",
        "metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2wDYgolAwxt"
      },
      "source": [
        "Specify where to push your model and tokenizer in the [PushToHubCallback](https://huggingface.co./docs/transformers/main/en/main_classes/keras_callbacks#transformers.PushToHubCallback):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8h2Vi6NYAwxt",
        "outputId": "a02e720d-2295-4ed2-b287-95712af12de4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:131: FutureWarning: 'Repository' (from 'huggingface_hub.repository') is deprecated and will be removed from version '1.0'. Please prefer the http-based alternatives instead. Given its large adoption in legacy code, the complete removal is only planned on next major release.\n",
            "For more details, please read https://huggingface.co./docs/huggingface_hub/concepts/git_vs_http.\n",
            "  warnings.warn(warning_message, FutureWarning)\n",
            "Cloning https://huggingface.co./rubakha/xlm-roberta into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co./rubakha/xlm-roberta into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "from transformers.keras_callbacks import PushToHubCallback\n",
        "\n",
        "push_to_hub_callback = PushToHubCallback(\n",
        "    output_dir=\"./xlm-roberta\",\n",
        "    tokenizer=tokenizer,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8594Kl5Awxt"
      },
      "source": [
        "Then bundle your callbacks together:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "1Nl892o8Awxt"
      },
      "outputs": [],
      "source": [
        "callbacks = [metric_callback, push_to_hub_callback]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qS0SgLuRAwxt"
      },
      "source": [
        "Finally, you're ready to start training your model! Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-lU1kXIAwxt",
        "outputId": "aa7f4af9-9991-4502-d10b-586c28770150"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:AutoGraph could not transform <function infer_framework at 0x7ef6195a77f0> and will run it as-is.\n",
            "Cause: for/else statement not yet supported\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING: AutoGraph could not transform <function infer_framework at 0x7ef6195a77f0> and will run it as-is.\n",
            "Cause: for/else statement not yet supported\n",
            "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
            "1000/1000 [==============================] - ETA: 0s - loss: 0.8384 - accuracy: 0.6678"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Adding files tracked by Git LFS: ['tokenizer.json']. This may take a bit of time if the files are large.\n",
            "WARNING:huggingface_hub.repository:Adding files tracked by Git LFS: ['tokenizer.json']. This may take a bit of time if the files are large.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1000/1000 [==============================] - 433s 336ms/step - loss: 0.8384 - accuracy: 0.9230 - val_loss: 0.2463 - val_accuracy: 0.9230 - precision: 0.9256 - recall: 0.9230 - f1: 0.9221\n",
            "Epoch 2/3\n",
            "1000/1000 [==============================] - 333s 333ms/step - loss: 0.2072 - accuracy: 0.9360 - val_loss: 0.1811 - val_accuracy: 0.9360 - precision: 0.9379 - recall: 0.9360 - f1: 0.9356\n",
            "Epoch 3/3\n",
            "1000/1000 [==============================] - 351s 351ms/step - loss: 0.1468 - accuracy: 0.9440 - val_loss: 0.1545 - val_accuracy: 0.9440 - precision: 0.9458 - recall: 0.9440 - f1: 0.9436\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf_keras.src.callbacks.History at 0x7ef5d00e3d30>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate your model on the test set\n",
        "evaluation_result = model.evaluate(tf_test_set)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WbTW9AF-Lze3",
        "outputId": "d4ea5fc9-88f4-442b-a323-19ddd7430b58"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "125/125 [==============================] - 9s 69ms/step - loss: 0.1694 - accuracy: 0.9410\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fqIYMvwAwxt"
      },
      "source": [
        "Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!\n",
        "\n",
        "<Tip>\n",
        "\n",
        "For a more in-depth example of how to finetune a model for text classification, take a look at the corresponding\n",
        "[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb)\n",
        "or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb).\n",
        "\n",
        "</Tip>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1VBMsD3Awxu"
      },
      "source": [
        "## Inference"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q1tsnVT4Awxu"
      },
      "source": [
        "Great, now that you've finetuned a model, you can use it for inference!\n",
        "\n",
        "Grab some text you'd like to run inference on:"
      ]
    },
    {
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        "text = \"This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three.\""
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        "The simplest way to try out your finetuned model for inference is to use it in a [pipeline()](https://huggingface.co./docs/transformers/main/en/main_classes/pipelines#transformers.pipeline). Instantiate a `pipeline` for sentiment analysis with your model, and pass your text to it:"
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            ]
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          "metadata": {},
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        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\"sentiment-analysis\", model=\"stevhliu/my_awesome_model\")\n",
        "classifier(text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUIAd73mAwxu"
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      "source": [
        "You can also manually replicate the results of the `pipeline` if you'd like:\n",
        "\n",
        "Tokenize the text and return TensorFlow tensors:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "MnjMuYlLAwxu"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"stevhliu/my_awesome_model\")\n",
        "inputs = tokenizer(text, return_tensors=\"tf\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghPFR3W3Awxu"
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      "source": [
        "Pass your inputs to the model and return the `logits`:"
      ]
    },
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      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "2ls931AvAwxu",
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        "outputId": "d993c106-4f85-449b-90b9-fe2944aeeb77"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFDistilBertForSequenceClassification.\n",
            "\n",
            "All the weights of TFDistilBertForSequenceClassification were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertForSequenceClassification for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "from transformers import TFAutoModelForSequenceClassification\n",
        "\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained(\"stevhliu/my_awesome_model\")\n",
        "logits = model(**inputs).logits"
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    },
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      "source": [
        "Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a text label:"
      ]
    },
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      "execution_count": 26,
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          "data": {
            "text/plain": [
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        }
      ],
      "source": [
        "predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])\n",
        "model.config.id2label[predicted_class_id]"
      ]
    }
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