diff --git "a/sequence_classification (1).ipynb" "b/sequence_classification (1).ipynb"
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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "joTaXUsFAwxg",
+        "outputId": "d0aac7d7-a7fb-4ce5-e8b7-2f9064461136",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.38.2)\n",
+            "Collecting datasets\n",
+            "  Downloading datasets-2.18.0-py3-none-any.whl (510 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
+            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.20.3)\n",
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+            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
+            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
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+            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2024.2.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
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+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.10.0)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
+            "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: dill, multiprocess, datasets\n",
+            "Successfully installed datasets-2.18.0 dill-0.3.8 multiprocess-0.70.16\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": {
+        "id": "0zrBI8sBAwxl",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 145,
+          "referenced_widgets": [
+            "63e5165d2ae643ea8def3b4141a39c4a",
+            "ac63993121d3452689b3575bffa5082a",
+            "256f6c6ba82f4a5bb9256b6480ac3055",
+            "f70cf9a1d5c740a78ebb8c2300564d87",
+            "f01beb3384c346d2ad62fd8d07f05b97",
+            "2f90cafa30c045ac97688be9b24e0db7",
+            "b276281712414480b6f9584d73c63371",
+            "691c60bed4944d11a6d3bd8753d7c963",
+            "1b95093f83834f54b7cc082d7fdfd0bf",
+            "0c83aecf91594b0f8485fe6d99356cb2",
+            "b311a289c96446e695b206df6024d5b4",
+            "931f58fc435a4fd9a1b57a1fa8ab6914",
+            "52e8fb18a744452fb21fa0db0efc4ffa",
+            "cd1e05bff56e4f859f1b82d26df8b3b9",
+            "590ffb14480d4152afec3792ed33008e",
+            "a9e5fdc599a743d48258d36ec893964a",
+            "34e776da3a0043bdaa319ed059a90801",
+            "33e7d9d09c2845df81bca8eccfe20e10",
+            "0e88fe140ec5438fbc1000c6d296c4bb",
+            "e2c339d675084939a7ce9ba4e3fa7517",
+            "0e01d5b07374418c95d4166a22a03bcc",
+            "26e8fc4586df459b8f21a42b5ffd78ae",
+            "71857d17de8a4f7b93edb40f791c7797",
+            "35073580241f4f60aa9c9276a69b5f3f",
+            "80e2b384bece41c995bbf4b94297ad6d",
+            "cedcf1ff9bc7485087e0e11d1d132773",
+            "9184ed5b9acf4b248ac7588e87a71932",
+            "c789bb474a7547b08c643aa76e23b447",
+            "b50a077398cb41cf9d50158632135d6a",
+            "2863107c10564057bb40a24abb33f445",
+            "dd9f1d7dfbec4312a13e19c3ac16057a",
+            "77cb887e9a924fafb4c54eed59c91ebb"
+          ]
+        },
+        "outputId": "fdc47bf8-f33f-4d43-98dc-bdfc3160e860"
+      },
+      "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": "63e5165d2ae643ea8def3b4141a39c4a"
+            }
+          },
+          "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": {
+        "id": "O8bfMi1QAwxm",
+        "outputId": "9e7a9084-f25e-484b-d152-cfc395c00725",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345,
+          "referenced_widgets": [
+            "305f87f80fd8437b99356a2492e1c6b6",
+            "a7d1660c26a241dca064192ed36dfdf0",
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+            "590ae03a47bd4beebe9c934bc1e43a46",
+            "59c5b4cb7a364496b5e4c8bc167b67eb",
+            "5781ef7b050e41d5a04c5d12ee1d5963",
+            "831ac0292b154bf7b8837b4584b3ccc6",
+            "5636460fa79f4b7fb519c9678b350ba8",
+            "29b0352c87214d6b9aa6bb9bbc997910",
+            "0973732cf7544e318f3bdbe7894a41cc",
+            "69a79fcbb6054bec9ad93736fffe017b",
+            "b68807743d2d45cd821c8f5647fcd875",
+            "7384cf92b27e40a29c91be7f3cfbcc96",
+            "c8d3f3a8b0ed48ce84a80ba6a8fce7a0",
+            "11d0183c167f42578609c783d3ed3dd2",
+            "5a1c6fb49e1b4eab959e75ef4b11d081",
+            "712ef426d0cb4b48b3ac5f0559633345",
+            "8d143c9f4c3343f685c99cef3f239340",
+            "c2fdd98739b841df90cd9b58aff28251",
+            "aee6c368420e4ea2a05c4a1ec65e8ecb",
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+          ]
+        }
+      },
+      "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": "305f87f80fd8437b99356a2492e1c6b6"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "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": "5781ef7b050e41d5a04c5d12ee1d5963"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/248k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "712ef426d0cb4b48b3ac5f0559633345"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/244k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e7cf761c45284df19dc633d93663e75a"
+            }
+          },
+          "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": "129727a4b5b948c1a5c7bef119ca4ed8"
+            }
+          },
+          "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": "3c1394fa74b84336be2364eb3bb20307"
+            }
+          },
+          "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": "823cd49eaa314d2099f0067f4778b0b2"
+            }
+          },
+          "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": {
+        "id": "OwPvrdqmAwxm",
+        "outputId": "f29b110d-2c44-4f70-9465-647e0324fd57",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "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",
+      "execution_count": 5,
+      "metadata": {
+        "id": "WtiEVxXTAwxn",
+        "outputId": "4138455d-3005-4896-aad9-3043870633db",
+        "colab": {
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+          "height": 177,
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+        }
+      },
+      "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": "8f65d512879f45569be1bc8a488ce6c6"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/480 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "cff37376793642a591971e250b41fe86"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8ec40e2ac45548bbbf54afa6933ba175"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c1ea223186b447ffb37b1c5a853289c9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4298f7d8a900485d95c6a65d866ee715"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from transformers import AutoTokenizer\n",
+        "\n",
+        "tokenizer = AutoTokenizer.from_pretrained(\"distilroberta-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": {
+        "id": "cem05VFtAwxn",
+        "outputId": "9e500f48-edaa-4881-d2bd-044fce5087de",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113,
+          "referenced_widgets": [
+            "eb21644dfc39407795d04104369f289a",
+            "2b836198b619441a84818fb7cce6768e",
+            "4d0bea5710d84ac780c28fdf528d32a7",
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+            "435961fdf2b44b15a6f53c40e9acd8fe",
+            "02c4a5eb79a740359fabbf453bc6fb9a",
+            "b56b8c8b2d314f008b16a5723b437262",
+            "3b45439f313d419dadab9fb02a3cb21c",
+            "55e2acedc2c845e2ad11c7dfc6702e8f",
+            "489cfac106a64f0fb1a1cf1cd84fef91",
+            "a131b5bca6cd46f084556293869aeb82",
+            "2e245494b841480dbb04bba9ac1c139c",
+            "dcf32a7a0112474ea00290e20dd66dc1",
+            "ced70fca346c468b8bb89cfa9afcfd86",
+            "b23bb3439c0549c1afd1947ea83232b9",
+            "c277bd81a1584150948fc99a902f293a",
+            "eeebbd4cc5cd45a68b07f2a868d687ec",
+            "186b64918cdc4c0db11a38eb39b198b7",
+            "4ed76d29d46144ae8b875b6cbb4c131a",
+            "64b5c9f9446342648108f58d22c6cd91",
+            "e7215af0967d41bcaf771912eb47d33c"
+          ]
+        }
+      },
+      "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": "eb21644dfc39407795d04104369f289a"
+            }
+          },
+          "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": "28192995e91445cdaf5e9e80eb66cb3d"
+            }
+          },
+          "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": "a131b5bca6cd46f084556293869aeb82"
+            }
+          },
+          "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",
+      "source": [
+        "pip install evaluate"
+      ],
+      "metadata": {
+        "id": "H0X85A1BBejk",
+        "outputId": "c0634bb5-d3f6-4521-9433-684be70daace",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 9,
+      "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[31m1.3 MB/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",
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+            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from evaluate) (23.2)\n",
+            "Collecting responses<0.19 (from evaluate)\n",
+            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (3.13.1)\n",
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+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (2024.2.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2023.4)\n",
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+            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (23.2.0)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.4.1)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (6.0.5)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.9.4)\n",
+            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.0.3)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->evaluate) (1.16.0)\n",
+            "Installing collected packages: responses, evaluate\n",
+            "Successfully installed evaluate-0.4.1 responses-0.18.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "OlWJIwc6Awxo",
+        "outputId": "49ad1f79-9307-4528-94d7-c851c00a2f80",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "afa70294ca064b6b8d0f47330c0a2d72",
+            "2fbceac30a814924a0006dd4b2bdbba9",
+            "94506e65c86e4654ae9ac0f640e99ce3",
+            "d9456bcd556c4d45ae53aabde62b9732",
+            "ee6756bb68914c0098f92c2fa2c152d3",
+            "675d58dffdb9455d9a6bfe337ec07283",
+            "744ca6a09e5f4b918e51843893c58c60",
+            "f6bff9e14c4b4423b6e0eb5f38d11bee",
+            "3f1889664b73405c891cf0c8ed2df3d7",
+            "6662c16f91034dbf92a8da01f37ab063",
+            "a1bb0cdbf6ff48d484ec432b5d1738f5"
+          ]
+        }
+      },
+      "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": "afa70294ca064b6b8d0f47330c0a2d72"
+            }
+          },
+          "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",
+        "\n",
+        "\n",
+        "def compute_metrics(eval_pred):\n",
+        "    predictions, labels = eval_pred\n",
+        "    predictions = np.argmax(predictions, axis=1)\n",
+        "    return accuracy.compute(predictions=predictions, references=labels)"
+      ]
+    },
+    {
+      "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": null,
+      "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": {
+        "id": "Czp_lnZRAwxp",
+        "outputId": "590aa84a-abfd-4c23-e401-a0ed49150d11",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 138,
+          "referenced_widgets": [
+            "a989fc391efe4517bcbf4fe67e22151e",
+            "6f243925d628405b9747b27ba28d3945",
+            "34a0a0a97c7141a1bc11e641c04710ee",
+            "af4027b7c4e04219b6096758d5cef6b2",
+            "8c42ee14fabb4312ac286a52bcd34b57",
+            "01b2e48b495f45d18e0057496f4897fc",
+            "a8bbd79368064480bd95272323fbf3cb",
+            "94106e8c53c44606972c0e75756afe5a",
+            "4ce13ce6dcb44626a4ae2e3b744baef9",
+            "6ebc58b0a6a54b2e8a2b37b709f80e53",
+            "f97566eb87ae493685c3a2a1a43bfce8"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/331M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a989fc391efe4517bcbf4fe67e22151e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "All PyTorch model weights were used when initializing TFRobertaForSequenceClassification.\n",
+            "\n",
+            "Some weights or buffers of the TF 2.0 model TFRobertaForSequenceClassification 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",
+        "    \"distilroberta-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[\"test\"],\n",
+        "    shuffle=False,\n",
+        "    batch_size=16,\n",
+        "    collate_fn=data_collator,\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)  # 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": {
+        "id": "8h2Vi6NYAwxt",
+        "outputId": "7a0a46a0-7374-4650-8bf4-3f63d44729bc",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "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/reem442/emo into local empty directory.\n",
+            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/reem442/emo into local empty directory.\n"
+          ]
+        }
+      ],
+      "source": [
+        "from transformers.keras_callbacks import PushToHubCallback\n",
+        "\n",
+        "push_to_hub_callback = PushToHubCallback(\n",
+        "    output_dir=\"./emo\",\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": 23,
+      "metadata": {
+        "id": "3-lU1kXIAwxt",
+        "outputId": "65590a39-216c-43ee-c7a4-7d228179c8cb",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/3\n",
+            "1000/1000 [==============================] - 138s 138ms/step - loss: 0.1115 - val_loss: 0.1544 - accuracy: 0.9425\n",
+            "Epoch 2/3\n",
+            "1000/1000 [==============================] - 134s 134ms/step - loss: 0.0973 - val_loss: 0.1537 - accuracy: 0.9455\n",
+            "Epoch 3/3\n",
+            "1000/1000 [==============================] - 118s 118ms/step - loss: 0.0920 - val_loss: 0.1537 - accuracy: 0.9455\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tf_keras.src.callbacks.History at 0x79e212693460>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
+      "source": [
+        "model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)"
+      ]
+    },
+    {
+      "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:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "2wadXJm5Awxu"
+      },
+      "outputs": [],
+      "source": [
+        "text = \"This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three.\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kfv3frDSAwxu"
+      },
+      "source": [
+        "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:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "cmV1hPffAwxu",
+        "outputId": "7e0ea78e-a2ca-4719-88ab-2ef8a01f4b9f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 547
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "OSError",
+          "evalue": "reem442/emo does not appear to have a file named config.json. Checkout 'https://huggingface.co/reem442/emo/main' for available files.",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    285\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m         \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mHTTPError\u001b[0m: 404 Client Error: Not Found for url: https://huggingface.co/reem442/emo/resolve/main/config.json",
+            "\nThe above exception was the direct cause of the following exception:\n",
+            "\u001b[0;31mEntryNotFoundError\u001b[0m                        Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    397\u001b[0m         \u001b[0;31m# Load from URL or cache if already cached\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m         resolved_file = hf_hub_download(\n\u001b[0m\u001b[1;32m    399\u001b[0m             \u001b[0mpath_or_repo_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, local_dir_use_symlinks, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout, endpoint)\u001b[0m\n\u001b[1;32m   1237\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1238\u001b[0;31m                 metadata = get_hf_file_metadata(\n\u001b[0m\u001b[1;32m   1239\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36mget_hf_file_metadata\u001b[0;34m(url, token, proxies, timeout, library_name, library_version, user_agent)\u001b[0m\n\u001b[1;32m   1630\u001b[0m     \u001b[0;31m# Retrieve metadata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1631\u001b[0;31m     r = _request_wrapper(\n\u001b[0m\u001b[1;32m   1632\u001b[0m         \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"HEAD\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m    384\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfollow_relative_redirects\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m         response = _request_wrapper(\n\u001b[0m\u001b[1;32m    386\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36m_request_wrapper\u001b[0;34m(method, url, follow_relative_redirects, **params)\u001b[0m\n\u001b[1;32m    408\u001b[0m     \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m     \u001b[0mhf_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    410\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"{response.status_code} Client Error.\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"\\n\\n\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34mf\"Entry Not Found for url: {response.url}.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mEntryNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mEntryNotFoundError\u001b[0m: 404 Client Error. (Request ID: Root=1-65eca511-6d5ac2aa41436e6476b129cf;f55c45bc-cab3-4874-a0a5-8b1197525f5a)\n\nEntry Not Found for url: https://huggingface.co/reem442/emo/resolve/main/config.json.",
+            "\nThe above exception was the direct cause of the following exception:\n",
+            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-22-8ce4da5d0a02>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mclassifier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sentiment-analysis\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"reem442/emo\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m    813\u001b[0m                     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter_config\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"base_model_name_or_path\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    814\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 815\u001b[0;31m         config = AutoConfig.from_pretrained(\n\u001b[0m\u001b[1;32m    816\u001b[0m             \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_from_pipeline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode_revision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcode_revision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhub_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    817\u001b[0m         )\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m   1109\u001b[0m         \u001b[0mcode_revision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"code_revision\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1111\u001b[0;31m         \u001b[0mconfig_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munused_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPretrainedConfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_config_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1112\u001b[0m         \u001b[0mhas_remote_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"auto_map\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconfig_dict\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"AutoConfig\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconfig_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"auto_map\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1113\u001b[0m         \u001b[0mhas_local_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"model_type\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconfig_dict\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mconfig_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"model_type\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mCONFIG_MAPPING\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py\u001b[0m in \u001b[0;36mget_config_dict\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m    631\u001b[0m         \u001b[0moriginal_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    632\u001b[0m         \u001b[0;31m# Get config dict associated with the base config file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 633\u001b[0;31m         \u001b[0mconfig_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_config_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    634\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m\"_commit_hash\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconfig_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    635\u001b[0m             \u001b[0moriginal_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"_commit_hash\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"_commit_hash\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py\u001b[0m in \u001b[0;36m_get_config_dict\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m    686\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    687\u001b[0m                 \u001b[0;31m# Load from local folder or from cache or download from model Hub and cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m                 resolved_config_file = cached_file(\n\u001b[0m\u001b[1;32m    689\u001b[0m                     \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m                     \u001b[0mconfiguration_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    450\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrevision\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m             \u001b[0mrevision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"main\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m         raise EnvironmentError(\n\u001b[0m\u001b[1;32m    453\u001b[0m             \u001b[0;34mf\"{path_or_repo_id} does not appear to have a file named {full_filename}. Checkout \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m             \u001b[0;34mf\"'https://huggingface.co/{path_or_repo_id}/{revision}' for available files.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mOSError\u001b[0m: reem442/emo does not appear to have a file named config.json. Checkout 'https://huggingface.co/reem442/emo/main' for available files."
+          ]
+        }
+      ],
+      "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"
+      },
+      "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": null,
+      "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"
+      },
+      "source": [
+        "Pass your inputs to the model and return the `logits`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2ls931AvAwxu"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import TFAutoModelForSequenceClassification\n",
+        "\n",
+        "model = TFAutoModelForSequenceClassification.from_pretrained(\"stevhliu/my_awesome_model\")\n",
+        "logits = model(**inputs).logits"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ne4I6WwwAwxu"
+      },
+      "source": [
+        "Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a text label:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aXi3dcUPAwxu",
+        "outputId": "4ea8345e-103a-4958-edd0-3886c50950ee"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'POSITIVE'"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])\n",
+        "model.config.id2label[predicted_class_id]"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "63e5165d2ae643ea8def3b4141a39c4a": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "VBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "VBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "VBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_0e01d5b07374418c95d4166a22a03bcc",
+              "IPY_MODEL_26e8fc4586df459b8f21a42b5ffd78ae",
+              "IPY_MODEL_71857d17de8a4f7b93edb40f791c7797",
+              "IPY_MODEL_35073580241f4f60aa9c9276a69b5f3f"
+            ],
+            "layout": "IPY_MODEL_b276281712414480b6f9584d73c63371"
+          }
+        },
+        "ac63993121d3452689b3575bffa5082a": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_691c60bed4944d11a6d3bd8753d7c963",
+            "placeholder": "​",
+            "style": "IPY_MODEL_1b95093f83834f54b7cc082d7fdfd0bf",
+            "value": "<center> <img\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.svg\nalt='Hugging Face'> <br> Copy a token from <a\nhref=\"https://huggingface.co/settings/tokens\" target=\"_blank\">your Hugging Face\ntokens page</a> and paste it below. <br> Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. </center>"
+          }
+        },
+        "256f6c6ba82f4a5bb9256b6480ac3055": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "PasswordModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "PasswordModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "PasswordView",
+            "continuous_update": true,
+            "description": "Token:",
+            "description_tooltip": null,
+            "disabled": false,
+            "layout": "IPY_MODEL_0c83aecf91594b0f8485fe6d99356cb2",
+            "placeholder": "​",
+            "style": "IPY_MODEL_b311a289c96446e695b206df6024d5b4",
+            "value": ""
+          }
+        },
+        "f70cf9a1d5c740a78ebb8c2300564d87": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "CheckboxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "CheckboxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "CheckboxView",
+            "description": "Add token as git credential?",
+            "description_tooltip": null,
+            "disabled": false,
+            "indent": true,
+            "layout": "IPY_MODEL_931f58fc435a4fd9a1b57a1fa8ab6914",
+            "style": "IPY_MODEL_52e8fb18a744452fb21fa0db0efc4ffa",
+            "value": true
+          }
+        },
+        "f01beb3384c346d2ad62fd8d07f05b97": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ButtonModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ButtonModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ButtonView",
+            "button_style": "",
+            "description": "Login",
+            "disabled": false,
+            "icon": "",
+            "layout": "IPY_MODEL_cd1e05bff56e4f859f1b82d26df8b3b9",
+            "style": "IPY_MODEL_590ffb14480d4152afec3792ed33008e",
+            "tooltip": ""
+          }
+        },
+        "2f90cafa30c045ac97688be9b24e0db7": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_a9e5fdc599a743d48258d36ec893964a",
+            "placeholder": "​",
+            "style": "IPY_MODEL_34e776da3a0043bdaa319ed059a90801",
+            "value": "\n<b>Pro Tip:</b> If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. </center>"
+          }
+        },
+        "b276281712414480b6f9584d73c63371": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": "center",
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": "flex",
+            "flex": null,
+            "flex_flow": "column",
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": "50%"
+          }
+        },
+        "691c60bed4944d11a6d3bd8753d7c963": {
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