HF_mulitlingunag_Ecoicop_text_classification.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ELehmann91/Thesis_Multilingual_Transferlearning/blob/master/HF_mulitlingunag_Ecoicop_text_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cdab942c-29f1-465e-ae68-033772a8d8d2"
      },
      "source": [
        "<!--<badge>--><a href=\"https://colab.research.google.com/github/huggingface/workshops/blob/main/machine-learning-tokyo/02-text-classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a><!--</badge>-->"
      ],
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      "cell_type": "markdown",
      "metadata": {
        "tags": [],
        "id": "7055f734-64bb-4577-9812-47fcac4a7c21"
      },
      "source": [
        "## Fine-tuning your first Transformer!"
      ],
      "id": "7055f734-64bb-4577-9812-47fcac4a7c21"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7a999088-e61b-4858-8d61-b5d32571fedb"
      },
      "source": [
        "In this notebook we'll take a look at fine-tuning a multilingual Transformer model called [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) for text classification. By the end of this notebook you should know how to:\n",
        "\n",
        "* Load and process a dataset from the Hugging Face Hub\n",
        "* Create a baseline with the zero-shot classification pipeline\n",
        "* Fine-tune and evaluate pretrained model on your data\n",
        "* Push a model to the Hugging Face Hub\n",
        "\n",
        "Let's get started!"
      ],
      "id": "7a999088-e61b-4858-8d61-b5d32571fedb"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": [],
        "id": "c2b4bb95-d76c-44bd-96bc-307b32a1c325"
      },
      "source": [
        "## Setup"
      ],
      "id": "c2b4bb95-d76c-44bd-96bc-307b32a1c325"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a26b0663-9333-4987-a008-e1c0cd53b729"
      },
      "source": [
        "If you're running this notebook on Google Colab or locally, you'll need a few dependencies installed. You can install them with `pip` as follows:"
      ],
      "id": "a26b0663-9333-4987-a008-e1c0cd53b729"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b454b9ec-dd54-4962-a0ea-08f85cc2352e"
      },
      "source": [
        "%%capture\n",
        "! pip install datasets transformers sentencepiece"
      ],
      "id": "b454b9ec-dd54-4962-a0ea-08f85cc2352e",
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "28c001ff-5f00-4eb1-a60d-38edae42d3bb"
      },
      "source": [
        "To be able to share your model with the community there are a few more steps to follow.\n",
        "\n",
        "First you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!) then execute the following cell and input your username and password:"
      ],
      "id": "28c001ff-5f00-4eb1-a60d-38edae42d3bb"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93553e9b-63a2-4cd7-bea3-68f0d6530988",
        "colab": {
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        "outputId": "f636f958-f8cd-45e4-f91b-e1f782b015b3"
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      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "id": "93553e9b-63a2-4cd7-bea3-68f0d6530988",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-crendential store but this isn't the helper defined on your machine.\n",
            "You will have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal to set it as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1e532cd4-40ad-4912-afbb-d534e0591c84"
      },
      "source": [
        "Then you need to install Git-LFS. Uncomment and execute the following cell:"
      ],
      "id": "1e532cd4-40ad-4912-afbb-d534e0591c84"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4961c280-aa1d-464c-871d-5675f65c4882"
      },
      "source": [
        "%%capture\n",
        "!apt install git-lfs"
      ],
      "id": "4961c280-aa1d-464c-871d-5675f65c4882",
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": [],
        "id": "0f22343b-a548-4f5e-ba74-30e813ea12e7"
      },
      "source": [
        "## The dataset"
      ],
      "id": "0f22343b-a548-4f5e-ba74-30e813ea12e7"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2d7aa10c-fc40-40ee-bedf-4b46c082b97a"
      },
      "source": [
        "In this notebook we'll be using the 🤗 Datasets to load and preprocess our data. If you're new to this library, check out the video below to get some additional context:"
      ],
      "id": "2d7aa10c-fc40-40ee-bedf-4b46c082b97a"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": [],
        "id": "95f8970e-7164-453e-9304-c546f4a97bc6"
      },
      "source": [
        "In this tutorial we'll use the [Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi) (or MARC for short). This is a large-scale collection of Amazon product reviews in several languages: English, Japanese, German, French, Spanish, and Chinese. "
      ],
      "id": "95f8970e-7164-453e-9304-c546f4a97bc6"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": [],
        "id": "127709ea-7658-428a-82f7-7883780b4ac3"
      },
      "source": [
        "We can download the dataset from the Hugging Face Hub with the 🤗 Datasets library, but first let's take a look at the available subsets (also called configs):"
      ],
      "id": "127709ea-7658-428a-82f7-7883780b4ac3"
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            "d48cba0e1f384f40a5aece61372e4e26",
            "ca222356cb5f49e18c23d04494520c61",
            "ae3a5cb9c8f54a238e66d1674374cb17",
            "06c7ef0d4be0431a939963dc65140ad9",
            "9c8ef3e19e274677aafbb78adb668b7f",
            "7a3fe5b4807b48dabe7bb4671e805a3a",
            "3657e67f8f30429caedb63783f1b8d59",
            "5d056b5590f141b5a00f727b0d83f360",
            "05342ed6fb7644d0a1a15ec7e105fa4b",
            "a2f8a36e289d4c2faec8437920af7a71"
          ]
        },
        "outputId": "a999353e-7844-4da1-dded-3fe0eacc1ef9"
      },
      "source": [
        "from datasets import get_dataset_config_names\n",
        "\n",
        "dataset_name = \"amazon_reviews_multi\"\n",
        "langs = get_dataset_config_names(dataset_name)\n",
        "langs"
      ],
      "id": "cb9c64f7-9657-4cc4-8b22-44258e8b013d",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aca6f4acda494a679133f12cfb66c741",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.74k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a50a48c2a4c4bbebca6a6501b691535",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/3.62k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['all_languages', 'de', 'en', 'es', 'fr', 'ja', 'zh']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09a31799-31df-4072-8e6a-8bcfce64f5dc"
      },
      "source": [
        "Okay, we can see the language codes associated with each language, as well as an `all_languages` subset which presumably concatenates all the languages together. Let's begin by downloading the English subset with the `load_dataset()` function from 🤗 Datasets:"
      ],
      "id": "09a31799-31df-4072-8e6a-8bcfce64f5dc"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "995c653c-5cca-4427-ba31-fac74d1198c8"
      },
      "source": [
        "from datasets import load_dataset\n",
        "#marc_en = load_dataset(path=dataset_name, name=\"all_languages\")\n",
        "#marc_en"
      ],
      "id": "995c653c-5cca-4427-ba31-fac74d1198c8",
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b02fc1cb-a476-4288-8be0-615e648147a5"
      },
      "source": [
        "One cool feature of 🤗 Datasets is that `load_dataset()` will cache the files at `~/.cache/huggingface/dataset/`, so you won't need to re-download the dataset the next time your run the notebook. "
      ],
      "id": "b02fc1cb-a476-4288-8be0-615e648147a5"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cc347cc1-040f-4029-83b1-9e9dd8c8e643"
      },
      "source": [
        "We can see that `marc_en` is a `DatasetDict` object which is similar to a Python dictionary, with each key corresponding to a different split. We can access an element of one of these splits as follows:"
      ],
      "id": "cc347cc1-040f-4029-83b1-9e9dd8c8e643"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0d03173f-f4da-4f7b-8b10-ca0f51fe0ad0"
      },
      "source": [
        "# Peek at first element\n",
        "marc_en[\"train\"][0]"
      ],
      "id": "0d03173f-f4da-4f7b-8b10-ca0f51fe0ad0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25824877-c75d-420a-87c4-bcd16a380a64"
      },
      "source": [
        "This certainly looks like an Amazon product review and we can see the number of stars associated with the review, as well as some metadata like the language and product category."
      ],
      "id": "25824877-c75d-420a-87c4-bcd16a380a64"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0af156a-9c3d-4593-99be-54b1a388092d"
      },
      "source": [
        "We can also access several rows with a slice:"
      ],
      "id": "d0af156a-9c3d-4593-99be-54b1a388092d"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3cd07c0a-d36a-4df5-bde6-5c3b86357a8b"
      },
      "source": [
        "marc_en[\"train\"][:3]"
      ],
      "id": "3cd07c0a-d36a-4df5-bde6-5c3b86357a8b",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0711979f-04fd-413a-8464-2f4e5336277e"
      },
      "source": [
        "and note that now we get a list of values for each column. This is because 🤗 Datasets is based on Apache Arrow, which defines a typed columnar format that is very memory efficient."
      ],
      "id": "0711979f-04fd-413a-8464-2f4e5336277e"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bfb3805-3ebf-42be-8a9c-cea43c6ed4ba"
      },
      "source": [
        "### What if my dataset is not on the Hub?"
      ],
      "id": "4bfb3805-3ebf-42be-8a9c-cea43c6ed4ba"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "99d7ceeb-1555-47f3-8740-0f09d3d99c23"
      },
      "source": [
        "Note that altough we downloaded the dataset from the Hub, it's also possible to load datasets both locally and from custom URLs. For example, the above dataset lives at the following URL:"
      ],
      "id": "99d7ceeb-1555-47f3-8740-0f09d3d99c23"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2a0dc41f-c367-49ea-b926-3e3baa329c4c"
      },
      "source": [
        "We can then load it locally using the `json` loading script:"
      ],
      "id": "2a0dc41f-c367-49ea-b926-3e3baa329c4c"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzFD9Rrn_h6R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0434d61-dd45-450a-9742-fb10bde15865"
      },
      "source": [
        "import pandas as pd\n",
        "from google.colab import drive, files\n",
        "\n",
        "drive.mount('/content/gdrive')\n",
        "path ='/content/gdrive/My Drive/Thesis_ecb_ecoicop'\n",
        "data_path = '/data/'#"
      ],
      "id": "JzFD9Rrn_h6R",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ljfsT7DwL-be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220,
          "referenced_widgets": [
            "585e26a1725a4aad9cb22fe19335fe67",
            "c4a532c1f3ec4091999a0cb299fea53d",
            "4252231116b845d79f729645b165aba5",
            "a425d6875f0a42bc9f1b9c49142981a9",
            "9268b1944f5046b8a72f95c501cfbc29",
            "1205f7c7c12a4d3a819fc0d120c93f05",
            "c31b2675567d45fab4b568c96d1e0a01",
            "315fc0dfd79c465583eda83858cc78bc",
            "4964a8501ffb4b699213d4fc48ea506c",
            "20bc5349fe0f4220997e1ee03751b996",
            "86620dfac1514ab3bc62280d2ea643b7",
            "7293d89765dc41e29d11e570ec109128",
            "69e0e559de2b4f74896ae30b950e52f8",
            "916a2142939d47438c03bb5d4cec7486",
            "dbb9027c36564d14b31d6d2442b20d49",
            "2c237841ac1b405e839e1dd79ae85bcf",
            "a93eba0505e14888bf1651496ea9dfe8",
            "9d09afaf6b764630b949c125b493dc37",
            "136a548ccaee42b0a09b515eaea2f164",
            "a654c5f628cb47d386f1aa2dd424cfa4",
            "440615b1cc25454aaeddbfde92f2524f",
            "23b5ab65979d47dc9b1d169f97fd8747"
          ]
        },
        "outputId": "164bd525-c09b-48f1-baae-3fdb8ade53df"
      },
      "source": [
        "dataset_de = load_dataset(\"csv\", data_files=path+data_path+'german_preprocessed_s.csv', split='train')\n",
        "dataset_de"
      ],
      "id": "ljfsT7DwL-be",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-8d566bb51707f65c\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-8d566bb51707f65c/0.0.0/bf68a4c4aefa545d0712b2fcbb1b327f905bbe2f6425fbc5e8c25234acb9e14a...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "585e26a1725a4aad9cb22fe19335fe67",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7293d89765dc41e29d11e570ec109128",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-8d566bb51707f65c/0.0.0/bf68a4c4aefa545d0712b2fcbb1b327f905bbe2f6425fbc5e8c25234acb9e14a. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['Unnamed: 0', 'lang', 'cc5', 'shop', 'text'],\n",
              "    num_rows: 31463\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220,
          "referenced_widgets": [
            "ade01aa1ecba4db09e8cdafc5032e806",
            "04b1d78f1f5242098fdeb85d5af59ec4",
            "cbba16c26acc4efa90b97c2023c82717",
            "e5f6e22bba7b415da450eda681610d54",
            "ef8c0cbfe96c41a89686a546d8be401b",
            "67047ea6023e4db8b66cd470f7fe5eff",
            "88e874bf5e3e46c6be143086ac9ba92b",
            "b50311b826aa4ab9a5c47478dd4d159b",
            "9b1b111985454d648b71d38804a5b23e",
            "f72093069db44797986dde4bd6870b67",
            "ecdccfa3a12d43e797e48843930b1e76",
            "436d9686aa524ac8a20ee3d9668657d7",
            "3d736532edd149b699ed077a5c67bf9b",
            "69ca51d7f2244b1a8eb17878e05f30ae",
            "07b195d3b2b94a94911b0b6a3829b5ae",
            "efb09038e12848b7add4600bebe25d60",
            "8cf082ecf6f14a1fbde918731d1a1ef4",
            "8191d1065655492eb120e402399d0bd4",
            "15d991b5d1fe4c998531d63211d6ff57",
            "336c9d40e2b74b74865469b8e26953bd",
            "53269b2130ae4750a97ef8c24a68c834",
            "4ef8bad8f6cf47c69cf8744054ba12fc"
          ]
        },
        "id": "3g9Wn4YpO1Vt",
        "outputId": "d8192141-8e66-43c7-de0f-9e969363d9f2"
      },
      "source": [
        "dataset_fr = load_dataset(\"csv\", data_files=path+data_path+'french_preprocessed_s.csv', split='train')\n",
        "dataset_fr"
      ],
      "id": "3g9Wn4YpO1Vt",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-82a3b848dcbf4a1b\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-82a3b848dcbf4a1b/0.0.0/bf68a4c4aefa545d0712b2fcbb1b327f905bbe2f6425fbc5e8c25234acb9e14a...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ade01aa1ecba4db09e8cdafc5032e806",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "436d9686aa524ac8a20ee3d9668657d7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-82a3b848dcbf4a1b/0.0.0/bf68a4c4aefa545d0712b2fcbb1b327f905bbe2f6425fbc5e8c25234acb9e14a. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['Unnamed: 0', 'lang', 'cc5', 'shop', 'text'],\n",
              "    num_rows: 22833\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220,
          "referenced_widgets": [
            "7a5a636009454e44a9131021d4b8287e",
            "a0c95a8171994ae787f26fe04cff76fb",
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            "3294383430474227bd32fcdd38d9614f",
            "d90e7503a408462e99dfe28080b27cb2",
            "e686aa75e7cc44e88400c8d1787c337e",
            "b992966330f8431c976a0ad36043c757",
            "82b1a1064ccd4ee9833576abdfa5c745",
            "51971914b3c140d78a9e8652d59af93f",
            "c1a36f20734444e6a93a06505bb92c96",
            "4c9b4380c47a440abdcfcffe2e2a64de",
            "aa12c4277a8d4785a1d0b4f7b2e2b8af",
            "a9108b8f998e46d392c7367afd1682be",
            "2e9e91d631c64e16ac1361bce8145735",
            "e78bec72bdd547ecbe3ff22b0fc2503c",
            "46aa4d14f6884d5898d4b8cd1187659b",
            "2e24ad78ef5e466ba3fb0fd57ba6e991",
            "9256524c06f840d1a1b71a1b7761d9fd",
            "bba45c9f1bf84784abdb6755450345b7",
            "bbba249f745f41549003d5afd65e2c16",
            "f57963ff7f44490394f9b8f902945606",
            "91edbe79764a4321afa4a7dc5add2c98"
          ]
        },
        "id": "wpd2-LqDoWco",
        "outputId": "aa0f25e4-5e17-469b-eccd-9c1e220f1bf7"
      },
      "source": [
        "dataset_it = load_dataset(\"csv\", data_files=path+data_path+'italia_preprocessed_s.csv', split='train')\n",
        "dataset_it"
      ],
      "id": "wpd2-LqDoWco",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-c4cb4bffcdda3611\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-c4cb4bffcdda3611/0.0.0/bf68a4c4aefa545d0712b2fcbb1b327f905bbe2f6425fbc5e8c25234acb9e14a...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7a5a636009454e44a9131021d4b8287e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa12c4277a8d4785a1d0b4f7b2e2b8af",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-c4cb4bffcdda3611/0.0.0/bf68a4c4aefa545d0712b2fcbb1b327f905bbe2f6425fbc5e8c25234acb9e14a. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['Unnamed: 0', 'lang', 'cc5', 'shop', 'text'],\n",
              "    num_rows: 667\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NEGdfjY6PJfl",
        "outputId": "9a7f2cfe-7b5d-4a7b-b923-28aaceda655c"
      },
      "source": [
        "from datasets import  concatenate_datasets\n",
        "dataset_de_fr = concatenate_datasets([dataset_de,dataset_fr,dataset_it])\n",
        "dataset_de_fr"
      ],
      "id": "NEGdfjY6PJfl",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['Unnamed: 0', 'lang', 'cc5', 'shop', 'text'],\n",
              "    num_rows: 54963\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SclheJttQBX"
      },
      "source": [
        "dataset_de_fr = dataset_de_fr.shuffle()"
      ],
      "id": "2SclheJttQBX",
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrItNzFuUg1n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9dce9e5c-3829-4388-fdc9-b333ec52fd07"
      },
      "source": [
        "train_testvalid = dataset_de_fr.train_test_split()\n",
        "train_testvalid"
      ],
      "id": "QrItNzFuUg1n",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Unnamed: 0', 'lang', 'cc5', 'shop', 'text'],\n",
              "        num_rows: 41222\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Unnamed: 0', 'lang', 'cc5', 'shop', 'text'],\n",
              "        num_rows: 13741\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7a493c7-fd3e-475b-a4fb-caf25897753b"
      },
      "source": [
        "Now that we've had a quick look at the objects in 🤗 Datasets, let's explore the data in more detail by using our favourite tool - Pandas!"
      ],
      "id": "c7a493c7-fd3e-475b-a4fb-caf25897753b"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "336db506-b3b0-4d2f-8057-3f03fec2258f"
      },
      "source": [
        "## From Datasets to DataFrames and back"
      ],
      "id": "336db506-b3b0-4d2f-8057-3f03fec2258f"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2219bc99-5b4c-4c23-937d-cfc5aedbff04"
      },
      "source": [
        "🤗 Datasets is designed to be interoperable with libraries like Pandas, as well as NumPy, PyTorch, TensorFlow, and JAX. To enable the conversion between various third-party libraries, 🤗 Datasets provides a Dataset.set_format() function. This function only changes the output format of the dataset, so you can easily switch to another format without affecting the underlying data format which is Apache Arrow. The formatting is done in-place, so let’s convert our dataset to Pandas and look at a random sample:"
      ],
      "id": "2219bc99-5b4c-4c23-937d-cfc5aedbff04"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ab052e0a-b830-4af9-a121-2c58c21569f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "321f268c-4232-4cab-b668-4ffd307a8948"
      },
      "source": [
        "from IPython.display import display, HTML\n",
        "\n",
        "train_testvalid.set_format(\"pandas\")\n",
        "df = train_testvalid[\"train\"][:]\n",
        "# Create a random sample\n",
        "sample = df.sample(n=1, random_state=42)\n",
        "display(HTML(sample.to_html()))"
      ],
      "id": "ab052e0a-b830-4af9-a121-2c58c21569f1",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>lang</th>\n",
              "      <th>cc5</th>\n",
              "      <th>shop</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11635</th>\n",
              "      <td>5887</td>\n",
              "      <td>de</td>\n",
              "      <td>9999_Non-Food</td>\n",
              "      <td>Spar</td>\n",
              "      <td>Haushalt   Haushaltszubehör   Kerzen &amp; Raumdüfte &lt;sep&gt; shop lebensmittel simpex color teelicht maxi orchidee stk &lt;sep&gt; SIMPEX Color Teelicht Maxi Orchidee 12 Stk.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7be67d1-d588-4721-9e3c-536432fd6f27"
      },
      "source": [
        "We can see that the column headers are the same as we saw in the Arrow format and from the reviews we can see that negative reviews are associated with a lower star rating. Since we're now dealing with a `pandas.DataFrame` we can easily query our dataset. For example, let's see what the distribution of reviews per product category looks like: "
      ],
      "id": "d7be67d1-d588-4721-9e3c-536432fd6f27"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f464b3c1-f453-4e02-b5d8-a7d073d16e55",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c136d2a9-178a-4d95-c841-57e9481c485d"
      },
      "source": [
        "df[\"cc5\"].value_counts()"
      ],
      "id": "f464b3c1-f453-4e02-b5d8-a7d073d16e55",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9999_Non-Food                    8212\n",
              "2121_Wine from grapes            2677\n",
              "1145_Cheese and curd             2149\n",
              "1211_Coffee                      1877\n",
              "1183_Chocolate                   1733\n",
              "                                 ... \n",
              "2203_Other tobacco products        35\n",
              "1155_Other edible animal fats      34\n",
              "2112_Alcoholic soft drinks         29\n",
              "2123_Fortified wines               27\n",
              "2202_Cigars                         9\n",
              "Name: cc5, Length: 75, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZBS91IczLmxn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86993d5a-df78-4c83-c45b-ead8bad8a724"
      },
      "source": [
        "labels = df[\"cc5\"].unique()\n",
        "labels[:2]"
      ],
      "id": "ZBS91IczLmxn",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1185_Edible ices and ice cream',\n",
              "       '1116_Pasta products and couscous'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10e36dab-8aab-4982-ab79-dfac8fd00f86"
      },
      "source": [
        "Okay, the `home`, `wireless`, and `sports` categories seem to be the most popular. How about the distribution of star ratings?"
      ],
      "id": "10e36dab-8aab-4982-ab79-dfac8fd00f86"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d68d44a1-9dce-4f80-9184-dab3c4e4a09f"
      },
      "source": [
        "train_testvalid.reset_format()"
      ],
      "id": "d68d44a1-9dce-4f80-9184-dab3c4e4a09f",
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "084a2e25-39c5-46b5-9f31-17c122d655b0"
      },
      "source": [
        "## Filtering for a product category"
      ],
      "id": "084a2e25-39c5-46b5-9f31-17c122d655b0"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "abc6a094-55a9-4c41-ae2f-e5b662f21e6d"
      },
      "source": [
        "Although we could go ahead and fine-tune a Transformer model on the whole set of 200,000 English reviews, this will take several hours on a single GPU. So instead, we'll focus on fine-tuning a model for a single product category! In 🤗 Datasets, we can filter data very quickly by using the `Dataset.filter()` method. This method expects a function that returns Boolean values, in our case `True` if the `product_category` matches the chosen category and `False` otherwise. Here's one way to implement this, and we'll pick the `sports` category as the domain to train on:"
      ],
      "id": "abc6a094-55a9-4c41-ae2f-e5b662f21e6d"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8eea2e95-e5a9-4efc-ba19-719ba81a70a8"
      },
      "source": [
        "product_category = \"grocery\"\n",
        "\n",
        "def filter_for_product(example, product_category=product_category):\n",
        "    return example[\"product_category\"] == product_category"
      ],
      "id": "8eea2e95-e5a9-4efc-ba19-719ba81a70a8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1c0d848f-f3f8-43ea-a12c-90d49dbb27f6"
      },
      "source": [
        "Now when we pass `filter_for_product()` to `Dataset.filter()` we get a filtered dataset:"
      ],
      "id": "1c0d848f-f3f8-43ea-a12c-90d49dbb27f6"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9a2aecb4-6501-487d-a38e-5fbe3681c297",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351,
          "referenced_widgets": [
            "9faf9fa0108f46769ff1f61b9cc99040",
            "cb16cde46aec49f196d3a15f82a04225",
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            "daee1a26aad341d18588ef31f18c0c5a",
            "b17acaed08c54889abeadbdff75fe527",
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            "4e77b80809914c918812ee1528a07fa2",
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            "889ec70b04574718ae5bb40e61eeb096",
            "d94e2829edb5443ea4839b5c8641932c",
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            "b4f07ff79cae448d85b7a26fb86bcc15",
            "e52e15b135014b20a4ebcaaef466fb87",
            "20e2916b95444723b82e4c329b6a8af9",
            "91e9d792c7f44d57a5d1dcd9399feb42",
            "33bef3cd855a487abd327ea8764d9ac9",
            "1b42a88a1f1a4cd1a72a48c966b8c237",
            "14aa751bf9fd48a5ba1972bf205be07d",
            "72a4754bbae74757809ec5ec19ea0b98",
            "afcbd3b1c6f64477a06d9a7f0964c4bd",
            "dc633436bf1b4007b8ddfaedb180a6f5",
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            "e142db75d2b44212810f996ddf25d576",
            "6c8a35d5969e4d1daa404c38faf4d78e",
            "565a5daa90e14dffb12011a911fb45be"
          ]
        },
        "outputId": "069326a3-09e8-4c67-ec1a-ede634bcbd50"
      },
      "source": [
        "product_dataset = marc_en.filter(filter_for_product)\n",
        "product_dataset"
      ],
      "id": "9a2aecb4-6501-487d-a38e-5fbe3681c297",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9faf9fa0108f46769ff1f61b9cc99040",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1200 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "97ba85638f6543b7ac981a95ae12760c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/30 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1b42a88a1f1a4cd1a72a48c966b8c237",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "  0%|          | 0/30 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
              "        num_rows: 23718\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
              "        num_rows: 602\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
              "        num_rows: 604\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "efa86ae7-6789-44e3-b1f8-a88b9383aec9"
      },
      "source": [
        "Yep, this looks good - we have 13,748 reviews in the train split which agrees the number we saw in the distribution of categories earlier. Let's do a quick sanity check by taking a look at a few samples. Here 🤗 Datasets provides `Dataset.shuffle()` and `Dataset.select()` functions that we can chain to get a random sample:"
      ],
      "id": "efa86ae7-6789-44e3-b1f8-a88b9383aec9"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2d74a059-6381-45ce-bc9d-736ed3bac0ae"
      },
      "source": [
        "product_dataset[\"train\"].shuffle(seed=42).select(range(3))[:]"
      ],
      "id": "2d74a059-6381-45ce-bc9d-736ed3bac0ae",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "360ddc84-b48d-4853-8abe-fac0460d8dbe"
      },
      "source": [
        "Okay, now that we have our corpus of book reviews, let's do one last bit of data preparation: creating label mappings from star ratings to human readable strings."
      ],
      "id": "360ddc84-b48d-4853-8abe-fac0460d8dbe"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": [],
        "id": "7a2420dd-b6ca-4c41-811d-e4922e400339"
      },
      "source": [
        "## Mapping the labels"
      ],
      "id": "7a2420dd-b6ca-4c41-811d-e4922e400339"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b642ae4c-5c7f-42d6-acc3-4f099b12e63a"
      },
      "source": [
        "During training, 🤗 Transformers expects the labels to be ordered, starting from 0 to N. But we've seen that our star ratings range from 1-5, so let's fix that. While we're at it, we'll create a mapping between the label IDs and names, which will be handy later on when we want to run inference with our model. First we'll define the label mapping from ID to name:"
      ],
      "id": "b642ae4c-5c7f-42d6-acc3-4f099b12e63a"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNkBnqsnLcra"
      },
      "source": [
        ""
      ],
      "id": "lNkBnqsnLcra",
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3367d29d-f52d-4500-8aca-d8182541498c"
      },
      "source": [
        "label_names = labels #[\"terrible\", \"poor\", \"ok\", \"good\", \"great\"]\n",
        "id2label = {idx:label for idx, label in enumerate(label_names)}\n",
        "#id2label"
      ],
      "id": "3367d29d-f52d-4500-8aca-d8182541498c",
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tx1aQ1oJOnJ0"
      },
      "source": [
        "label_names = labels #[\"terrible\", \"poor\", \"ok\", \"good\", \"great\"]\n",
        "label2id = {label:idx for idx, label in enumerate(label_names)}\n",
        "#label2id"
      ],
      "id": "tx1aQ1oJOnJ0",
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5495a896-5960-49bd-a09f-47954b843e7b"
      },
      "source": [
        "We can then apply this mapping to our whole dataset by using the `Dataset.map()` method. Similar to the `Dataset.filter()` method, this one expects a function which receives examples as input, but returns a Python dictionary as output. The keys of the dictionary correspond to the columns, while the values correspond to the column entries. The following function creates two new columns:\n",
        "\n",
        "* A `labels` column which is the star rating shifted down by one\n",
        "* A `label_name` column which provides a nice string for each rating"
      ],
      "id": "5495a896-5960-49bd-a09f-47954b843e7b"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fb136d26-85c8-4e54-ba42-a8e8c6339364"
      },
      "source": [
        "def map_labels(example):\n",
        "    # Shift labels to start from 0\n",
        "    label_id = label2id[example[\"cc5\"]]\n",
        "    return {\"labels\": label_id, \"label_name\": id2label[label_id]}"
      ],
      "id": "fb136d26-85c8-4e54-ba42-a8e8c6339364",
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b9e600b8-c2d4-43b2-8c7e-4fc2690c3307"
      },
      "source": [
        "To apply this mapping, we simply feed it to `Dataset.map` as follows:"
      ],
      "id": "b9e600b8-c2d4-43b2-8c7e-4fc2690c3307"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a29e6267-94ec-471f-bb94-e1a1ab1b4c45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200,
          "referenced_widgets": [
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            "c24a9791091e487ab50846eb45552fe2",
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            "2bef0c200caa4d089390c347136a3280",
            "77104e4d69154f7f9500d6990b611841",
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          ]
        },
        "outputId": "3ecbc463-11c2-422a-8b4b-76b63c87c089"
      },
      "source": [
        "train_testvalid = train_testvalid.map(map_labels)\n",
        "# Peek at the first example\n",
        "train_testvalid[\"train\"][0]"
      ],
      "id": "a29e6267-94ec-471f-bb94-e1a1ab1b4c45",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "910452e0ca76445c8d2830310e79d774",
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              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/41222 [00:00<?, ?ex/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f85f30c31e1743b4ab3f830e0dba6e70",
              "version_minor": 0,
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            },
            "text/plain": [
              "  0%|          | 0/13741 [00:00<?, ?ex/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Unnamed: 0': 200810,\n",
              " 'cc5': '1185_Edible ices and ice cream',\n",
              " 'label_name': '1185_Edible ices and ice cream',\n",
              " 'labels': 0,\n",
              " 'lang': 'de',\n",
              " 'shop': 'shop.rewe.de',\n",
              " 'text': 'Tiefkühl Eiscreme & Eiswürfel Eis-Desserts <sep> snickers ice cream <sep> Snickers Ice Cream 6x53ml'}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d43aa90f-71df-4c46-b724-42f1643e2bdc"
      },
      "source": [
        "Great, it works! We'll also need the reverse label mapping later, so let's define it here: "
      ],
      "id": "d43aa90f-71df-4c46-b724-42f1643e2bdc"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7d7b295a-32fb-461a-a246-bf68fe4da147"
      },
      "source": [
        "label2id = {v:k for k,v in id2label.items()}"
      ],
      "id": "7d7b295a-32fb-461a-a246-bf68fe4da147",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": [],
        "id": "67116204-d608-4c24-aa72-9f8d169987f0"
      },
      "source": [
        "## From text to tokens"
      ],
      "id": "67116204-d608-4c24-aa72-9f8d169987f0"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a4d5ab37-4f46-4e5f-b886-8e8b046997dc"
      },
      "source": [
        "Like other machine learning models, Transformers expect their inputs in the form of numbers (not strings) and so some form of preprocessing is required. For NLP, this preprocessing step is called _tokenization_. Tokenization converts strings into atomic chunks called tokens, and these tokens are subsequently encoded as numerical vectors. \n",
        "\n",
        "For more information about tokenizers, check out the following video:"
      ],
      "id": "a4d5ab37-4f46-4e5f-b886-8e8b046997dc"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ece2f0c0-09e7-4c83-8b78-3d1146094deb",
        "outputId": "620df446-83ed-41cb-88e5-2a7169217dce"
      },
      "source": [
        "YouTubeVideo(\"VFp38yj8h3A\", width=600, height=400)"
      ],
      "id": "ece2f0c0-09e7-4c83-8b78-3d1146094deb",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/jpeg": 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",
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"600\"\n",
              "            height=\"400\"\n",
              "            src=\"https://www.youtube.com/embed/VFp38yj8h3A\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ],
            "text/plain": [
              "<IPython.lib.display.YouTubeVideo at 0x7fe826aa2040>"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "866cd04d-133b-4129-b7c2-86241735f085"
      },
      "source": [
        "Each pretrained model comes with its own tokenizer, so to get started let's download the tokenizer of XLM-RoBERTa from the Hub:"
      ],
      "id": "866cd04d-133b-4129-b7c2-86241735f085"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2de857a8-ac62-4f0b-8376-1194718014a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "bb4f9178dd8541cda11216797ac929f4",
            "04ab98aa253744e8ba31df241e0566f9",
            "64af7b285aa641a4af0792ba2a1631cb",
            "83a490a20e884d4fad9e381496e79255",
            "ce823f77aed64fb1acc891d7f7346713",
            "a124f2afe14f4e6bb58d03ff24a2df78",
            "01b1ee7fc02b4a08a7abc23ce10fe34a",
            "83ecb46a58a64577ad4865547e44aec8",
            "1074090e94954f2f83a4ece15c173853",
            "0d712dcacc6b4507b7c66bf16edc2ae3",
            "6df73a03a2be41588c2276d9739d04ec",
            "5a849c9967ed477ca044f436b4c9a6c7",
            "a92cc9bcd63549529f5a92aad9813820",
            "d0b3063f72394f77b338c678d0df236b",
            "cf9ae1ca83f44d27b3e7a38cd559aa57",
            "879f8fdea5ab4bd4bb5fd93a0e110bda",
            "3f9f600d893f4b778d594a8b07460857",
            "8e00d9d406594e5da64646d91ba4ccaa",
            "502dde12834347c9b4a9d527b9b2ad43",
            "e04396109ca9415cb6da3039ab7b3144",
            "40154fd23aa9484cb8e983c4a5218c22",
            "e66db1bcee0f4984a8103bbd42d61f02",
            "6b6b7dbabd804bd887305fa5b2b446a6",
            "bd82024be2f64d8e91700122bb2bb2c2",
            "dc2cd60dd73f40ad9ba0a0c42933a36a",
            "2cc3e1d37fa34f03b23205d87c56a21f",
            "7d6422f4b18f406eae62168f5f620f63",
            "620f40fc4e644d1f96145c6ef0b84471",
            "0e132c0db85d4d11a74ccbd4299d7048",
            "8c346f2e71c04a60a15260b5ed0d739e",
            "14cfff6922354de5a932fa21303492c7",
            "f2249b9656ee4899b1f14de181e1742d",
            "2a0be0e0e36d45d4a742b20da8a77d42"
          ]
        },
        "outputId": "f09e3691-ff2e-40ca-807b-44b431eef37e"
      },
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_checkpoint = \"xlm-roberta-base\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ],
      "id": "2de857a8-ac62-4f0b-8376-1194718014a8",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bb4f9178dd8541cda11216797ac929f4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/512 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5a849c9967ed477ca044f436b4c9a6c7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/4.83M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6b6b7dbabd804bd887305fa5b2b446a6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/8.68M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8aeec647-d755-4ee0-bf77-2b746533c130"
      },
      "source": [
        "The tokenizer has a few interesting attributes such as the vocabulary size:"
      ],
      "id": "8aeec647-d755-4ee0-bf77-2b746533c130"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42dde5dd-2d61-49c5-988a-c29aa547bafd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff16e28d-9941-48b8-c11c-3fe0848a601b"
      },
      "source": [
        "tokenizer.vocab_size"
      ],
      "id": "42dde5dd-2d61-49c5-988a-c29aa547bafd",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "250002"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "06ad877e-7376-4103-ad56-a802d2ebad49"
      },
      "source": [
        "This tells us that XLM-R has 250,002 tokens that is can use to represent text with. Some of the tokens are called _special tokens_ to indicate whether a token is the start or end of a sentence, or corresponds to the mask that is associated with language modeling. Here's what the special tokens look like for XLM-R:"
      ],
      "id": "06ad877e-7376-4103-ad56-a802d2ebad49"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a300b0c4-ee11-4bac-a398-ecfce41a504a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70aafdfa-7bf7-4462-deb4-ef9a9fff27b2"
      },
      "source": [
        "tokenizer.special_tokens_map"
      ],
      "id": "a300b0c4-ee11-4bac-a398-ecfce41a504a",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bos_token': '<s>',\n",
              " 'cls_token': '<s>',\n",
              " 'eos_token': '</s>',\n",
              " 'mask_token': '<mask>',\n",
              " 'pad_token': '<pad>',\n",
              " 'sep_token': '</s>',\n",
              " 'unk_token': '<unk>'}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "271ffa45-cafa-4818-9dd8-4706e106570f"
      },
      "source": [
        "When you feed strings to the tokenizer, you'll get at least two fields (some models have more, depending on how they're trained):\n",
        "\n",
        "* `input_ids`: These correspond to the numerical encodings that map each token to an integer\n",
        "* `attention_mask`: This indicates to the model which tokens should be ignored when computing self-attention\n",
        "\n",
        "Let's see how this works with a simple example. First we encode the string:"
      ],
      "id": "271ffa45-cafa-4818-9dd8-4706e106570f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0e6080e6-4fd2-42b2-8c82-44cf38a43537",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de069883-5b93-4bbd-9da7-888417fa5c10"
      },
      "source": [
        "encoded_str = tokenizer(\"Today I'm giving an NLP workshop at MLT\")\n",
        "encoded_str"
      ],
      "id": "0e6080e6-4fd2-42b2-8c82-44cf38a43537",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [0, 38396, 87, 25, 39, 68772, 142, 541, 37352, 42819, 99, 276, 27026, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0afa524f-afce-471a-8a5d-71a316b1160e"
      },
      "source": [
        "and then decode the input IDs to see the mapping explicitly:"
      ],
      "id": "0afa524f-afce-471a-8a5d-71a316b1160e"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9b53016-0c69-424f-8179-0608bac2df2c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9487d5ec-16e6-413a-d1ac-c034eec8ec7a"
      },
      "source": [
        "for token in encoded_str[\"input_ids\"]:\n",
        "    print(token, tokenizer.decode([token]))"
      ],
      "id": "c9b53016-0c69-424f-8179-0608bac2df2c",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 <s>\n",
            "38396 Today\n",
            "87 I\n",
            "25 '\n",
            "39 m\n",
            "68772 giving\n",
            "142 an\n",
            "541 N\n",
            "37352 LP\n",
            "42819 workshop\n",
            "99 at\n",
            "276 M\n",
            "27026 LT\n",
            "2 </s>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7277762a-1fc9-4919-8eca-91ae4cb5d3d9"
      },
      "source": [
        "So to prepare our inputs, we simply need to apply the tokenizer to each example in our corpus. As before, we'll do this with `Dataset.map()` so let's write a simple function to do so:"
      ],
      "id": "7277762a-1fc9-4919-8eca-91ae4cb5d3d9"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efffa20f-2983-4e86-afa3-9bf8a8d44aad"
      },
      "source": [
        "def tokenize_reviews(examples):\n",
        "    return tokenizer(examples[\"text\"], truncation=True, max_length=80)"
      ],
      "id": "efffa20f-2983-4e86-afa3-9bf8a8d44aad",
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4624a779-158e-4fc4-a8df-20e72a951bc3"
      },
      "source": [
        "Here we've enabled truncation, so the tokenizer will cut any inputs that are longer than 180 tokens (which is the setting used in the MARC paper). With this function we can go ahead and tokenize the whole corpus:"
      ],
      "id": "4624a779-158e-4fc4-a8df-20e72a951bc3"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0a246bc-9df9-4f80-9d68-b45a50c4ee66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251,
          "referenced_widgets": [
            "981269a99a0444d3862495b587661b52",
            "6f855d9598e14ec38da96ba7ed0fae63",
            "b8ad0532805b4df2bf825c08cf0038c2",
            "1590b4b225cd43f992672ea45e47a915",
            "7482d26a39034098be02d41c52085c0c",
            "4f414f49d1be4c2480bde8a2d3011e28",
            "36aff3a1afc344bf8109f684557bace1",
            "a28970ae2dda4b3380386a48336fdea4",
            "f1401273f27d40119cf6a7ddeb1dda05",
            "328a8d87ba834c7c9ca524da79aed45f",
            "ef40e74b153641099478b058cc905c51",
            "ab7c3bb6323a4ae4a7161ba66f5b5139",
            "06a20fb6e6f6468a95dc6babedcbe3c6",
            "bdbafead5cd2430f8d90b736a4304fc6",
            "451f1847b3d64cb180273b87c6aa072e",
            "8036a9ea9c7d44b2aed89344544c0c04",
            "e33f7a95f7d54f0cb46729ebbad2916f",
            "b852dc20d1c343349a641441021fe10e",
            "2a5cd4e8b7024873944b9b6ffc06b0be",
            "9dd654e467f0417f928c340002238734",
            "d2fc87e28b7b41679436cc21c40a0872",
            "857c2a6d9acb4d1583dc2090f349a8ca"
          ]
        },
        "outputId": "dc791466-4709-44f6-f0bc-d289a20db764"
      },
      "source": [
        "tokenized_dataset = train_testvalid.map(tokenize_reviews, batched=True)\n",
        "tokenized_dataset"
      ],
      "id": "e0a246bc-9df9-4f80-9d68-b45a50c4ee66",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "981269a99a0444d3862495b587661b52",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/42 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ab7c3bb6323a4ae4a7161ba66f5b5139",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/14 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Unnamed: 0', 'attention_mask', 'cc5', 'input_ids', 'label_name', 'labels', 'lang', 'shop', 'text'],\n",
              "        num_rows: 41222\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Unnamed: 0', 'attention_mask', 'cc5', 'input_ids', 'label_name', 'labels', 'lang', 'shop', 'text'],\n",
              "        num_rows: 13741\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08caca6f-ec76-4d8a-9e4d-5d4140dbcd68",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a25f71d-1870-44a0-8dd7-86ba1a887307"
      },
      "source": [
        "tokenized_dataset[\"train\"][0]"
      ],
      "id": "08caca6f-ec76-4d8a-9e4d-5d4140dbcd68",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Unnamed: 0': 200810,\n",
              " 'attention_mask': [1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
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              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1],\n",
              " 'cc5': '1185_Edible ices and ice cream',\n",
              " 'input_ids': [0,\n",
              "  150192,\n",
              "  92,\n",
              "  109833,\n",
              "  104331,\n",
              "  7612,\n",
              "  282,\n",
              "  619,\n",
              "  104331,\n",
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              "  104331,\n",
              "  9,\n",
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              "  4426,\n",
              "  70637,\n",
              "  2740,\n",
              "  91,\n",
              "  67127,\n",
              "  1314,\n",
              "  6,\n",
              "  2980,\n",
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              "  4426,\n",
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              "  159,\n",
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              "  65058,\n",
              "  53822,\n",
              "  305,\n",
              "  425,\n",
              "  14226,\n",
              "  7237,\n",
              "  2],\n",
              " 'label_name': '1185_Edible ices and ice cream',\n",
              " 'labels': 0,\n",
              " 'lang': 'de',\n",
              " 'shop': 'shop.rewe.de',\n",
              " 'text': 'Tiefkühl Eiscreme & Eiswürfel Eis-Desserts <sep> snickers ice cream <sep> Snickers Ice Cream 6x53ml'}"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e19eace3-d188-4290-8c80-8640cf765ac5"
      },
      "source": [
        "This looks good, so now let's load a pretrained model!"
      ],
      "id": "e19eace3-d188-4290-8c80-8640cf765ac5"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dab0ef8f-95e8-4574-9a6e-4c5d9e638ca2"
      },
      "source": [
        "## Loading a pretrained model"
      ],
      "id": "dab0ef8f-95e8-4574-9a6e-4c5d9e638ca2"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9cbf7d53-e59d-4653-b589-f3939c456b27"
      },
      "source": [
        "To load a pretrained model from the Hub is quite simple: just select the appropriate `AutoModelForXxx` class and use the `from_pretrained()` function with the model checkpoint. In our case, we're dealing with 5 classes (one for each star) so to initialise the model we'll provide this information along with the label mappings:"
      ],
      "id": "9cbf7d53-e59d-4653-b589-f3939c456b27"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gL57e5YtPKn1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6b2e0b7-225b-4cf0-facb-214b188e0071"
      },
      "source": [
        "len(labels)"
      ],
      "id": "gL57e5YtPKn1",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "75"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9765ff12-5395-446e-ac5a-6929e12098ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154,
          "referenced_widgets": [
            "a553e6a0682d4b0195659bb701e7aa72",
            "fb59e3bd25694423a0c61a9b0ebaec40",
            "3aff56570e6940bcbf8e50d1eeb25ae3",
            "5d161137b33e4c239862db213a5a2e01",
            "7e12db5b82b54a6bbe854406ea89017d",
            "0767dd892fea4a27bee2f0630e0c0695",
            "75c3520e27a5430ea6cc34c088590f13",
            "c18524ac58594641a7fba3c029855913",
            "84a9ff55bcac4aebaf005fdce176556c",
            "9305f92cb1cf42aeb88053f511d16a82",
            "7e42310697d5433ab380a7bf974f7ee3"
          ]
        },
        "outputId": "2ab2983a-d819-47c1-87af-e08057ffe0d3"
      },
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "num_labels = len(labels)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels, label2id=label2id, id2label=id2label)"
      ],
      "id": "9765ff12-5395-446e-ac5a-6929e12098ef",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a553e6a0682d4b0195659bb701e7aa72",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.04G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.decoder.weight', 'lm_head.dense.bias', 'lm_head.bias', 'lm_head.dense.weight', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.bias']\n",
            "- This IS expected if you are initializing XLMRobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing XLMRobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of XLMRobertaForSequenceClassification were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.dense.bias', '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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "abd5f926-ee3b-40e1-9fb9-c5f415c1f909"
      },
      "source": [
        "These warnings are perfectly normal - they are telling us that the weights in the head of the network are randomly initialised and so we should fine-tune the model on a downstream task.\n",
        "\n",
        "Now that we have a model, the next step is to initialise a `Trainer` that will take care of the training loop for us. Let's do that next."
      ],
      "id": "abd5f926-ee3b-40e1-9fb9-c5f415c1f909"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "432c48c2-e7ee-4e0f-a31c-70a23c800530"
      },
      "source": [
        "## Creating a Trainer"
      ],
      "id": "432c48c2-e7ee-4e0f-a31c-70a23c800530"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0628a74-9102-4f88-90a9-c9badb166cd4"
      },
      "source": [
        "To create a `Trainer`, we usually need a few basic ingredients:\n",
        "\n",
        "* A `TrainingArguments` class to define all the hyperparameters\n",
        "* A `compute_metrics` function to compute metrics during evaluation\n",
        "* Datasets to train and evaluate on"
      ],
      "id": "b0628a74-9102-4f88-90a9-c9badb166cd4"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fc748960-fa50-41e0-ab1d-169ed6f60fd7"
      },
      "source": [
        "For more information about the `Trainer` check out the following video:"
      ],
      "id": "fc748960-fa50-41e0-ab1d-169ed6f60fd7"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6277c27c-50cc-449a-a761-702077e3c47f",
        "outputId": "95b0a5f5-6189-4f6b-d613-9d874f5d3c86"
      },
      "source": [
        "YouTubeVideo(\"nvBXf7s7vTI\", width=600, height=400)"
      ],
      "id": "6277c27c-50cc-449a-a761-702077e3c47f",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/jpeg": 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",
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"600\"\n",
              "            height=\"400\"\n",
              "            src=\"https://www.youtube.com/embed/nvBXf7s7vTI\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ],
            "text/plain": [
              "<IPython.lib.display.YouTubeVideo at 0x7fe82005e670>"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fc54f0a2-9a14-41c5-b6a8-62255e5f576a"
      },
      "source": [
        "Let's start with the `TrainingArguments`:"
      ],
      "id": "fc54f0a2-9a14-41c5-b6a8-62255e5f576a"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8b2d9f66-525e-4960-9860-1a8e7fb6f149"
      },
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "batch_size = 16\n",
        "num_train_epochs = 5\n",
        "logging_steps = len(tokenized_dataset[\"train\"]) // (batch_size * num_train_epochs)\n",
        "\n",
        "args = TrainingArguments(\n",
        "    output_dir=f\"{model_name}-finetuned-ecoicop\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    weight_decay=0.01,\n",
        "    logging_steps=logging_steps,\n",
        "    push_to_hub=True,\n",
        ")"
      ],
      "id": "8b2d9f66-525e-4960-9860-1a8e7fb6f149",
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8e93dd9e-0634-4c42-a9d1-096565312de7"
      },
      "source": [
        "Here we've defined `output_dir` to save our checkpoints and tweaked some of the default hyperparameters like the learning rate and weight decay. The `push_to_hub` argument will push each checkpoint to the Hub automatically for us, so we can reuse the model at any point in the future!\n",
        "\n",
        "Now that we've defined the hyperparameters, the next step is to define the metrics. In the MARC paper, the authors point out that one should use the mean absolute error (MAE) for star ratings because:\n",
        "\n",
        "> star ratings for each review are ordinal, and a 2-star prediction for a 5-star review should be penalized more heavily than a 4-star prediction for a 5-star review.\n",
        "\n",
        "We'll take the same approach here and we can get the metric easily from Scikit-learn as follows:"
      ],
      "id": "8e93dd9e-0634-4c42-a9d1-096565312de7"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b605f30b-9fba-48dd-8d26-b96c09d1004b"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import mean_absolute_error, accuracy_score\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return {\"Acc\": accuracy_score(labels, predictions)}"
      ],
      "id": "b605f30b-9fba-48dd-8d26-b96c09d1004b",
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "012e8203-3b83-41f7-a6bc-2dfd11973b1c"
      },
      "source": [
        "With these ingredients we can now instantiate a `Trainer`:"
      ],
      "id": "012e8203-3b83-41f7-a6bc-2dfd11973b1c"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsWZc6zHP92r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4bf255f9-6618-45fe-f722-552d5c2c88f2"
      },
      "source": [
        "tokenized_dataset['train']"
      ],
      "id": "rsWZc6zHP92r",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['Unnamed: 0', 'attention_mask', 'cc5', 'input_ids', 'label_name', 'labels', 'lang', 'shop', 'text'],\n",
              "    num_rows: 41222\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kgnORglBQFDK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6f028e0-3fc4-4bce-f5d7-21f4c0e40c6a"
      },
      "source": [
        "tokenized_dataset[\"test\"]"
      ],
      "id": "kgnORglBQFDK",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['Unnamed: 0', 'attention_mask', 'cc5', 'input_ids', 'label_name', 'labels', 'lang', 'shop', 'text'],\n",
              "    num_rows: 13741\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d45d568c-c230-41c2-b73b-a1da577c9e43",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d82a46fe-c3a2-43ac-8aa3-d14dc7dc3d7e"
      },
      "source": [
        "from transformers import Trainer \n",
        "\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "id": "d45d568c-c230-41c2-b73b-a1da577c9e43",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop into local empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ef08cb10-7c78-41d8-b8ba-815726192513"
      },
      "source": [
        "Note that here we've also provided the tokenizer to the `Trainer`: doing so will ensure that all of our examples are automatically padded to the longest example in each batch. This is needed so that matrix operations in the forward pass of the model can be computed. \n",
        "\n",
        "With our `Trainer`, it is then a simple matter to train the model:"
      ],
      "id": "ef08cb10-7c78-41d8-b8ba-815726192513"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6b1f0d7-e1a1-4658-813a-18f25d2aea25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "daadbebf-9df8-4fea-af7f-8771268e636b"
      },
      "source": [
        "trainer.train()"
      ],
      "id": "c6b1f0d7-e1a1-4658-813a-18f25d2aea25",
      "execution_count": 43,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: label_name, text, lang, cc5, shop, Unnamed: 0.\n",
            "***** Running training *****\n",
            "  Num examples = 41222\n",
            "  Num Epochs = 5\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 12885\n"
          ]
        },
        {
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              "      \n",
              "      <progress value='12744' max='12885' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [12744/12885 40:03 < 00:26, 5.30 it/s, Epoch 4.94/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>0.361159</td>\n",
              "      <td>0.913180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.231300</td>\n",
              "      <td>0.250984</td>\n",
              "      <td>0.944109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.174600</td>\n",
              "      <td>0.192829</td>\n",
              "      <td>0.956917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
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        {
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            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: label_name, text, lang, cc5, shop, Unnamed: 0.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 13741\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to xlm-roberta-base-finetuned-ecoicop/checkpoint-2577\n",
            "Configuration saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-2577/config.json\n",
            "Model weights saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-2577/pytorch_model.bin\n",
            "tokenizer config file saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-2577/tokenizer_config.json\n",
            "Special tokens file saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-2577/special_tokens_map.json\n",
            "tokenizer config file saved in xlm-roberta-base-finetuned-ecoicop/tokenizer_config.json\n",
            "Special tokens file saved in xlm-roberta-base-finetuned-ecoicop/special_tokens_map.json\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: label_name, text, lang, cc5, shop, Unnamed: 0.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 13741\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to xlm-roberta-base-finetuned-ecoicop/checkpoint-5154\n",
            "Configuration saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-5154/config.json\n",
            "Model weights saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-5154/pytorch_model.bin\n",
            "tokenizer config file saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-5154/tokenizer_config.json\n",
            "Special tokens file saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-5154/special_tokens_map.json\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: label_name, text, lang, cc5, shop, Unnamed: 0.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 13741\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to xlm-roberta-base-finetuned-ecoicop/checkpoint-7731\n",
            "Configuration saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-7731/config.json\n",
            "Model weights saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-7731/pytorch_model.bin\n",
            "tokenizer config file saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-7731/tokenizer_config.json\n",
            "Special tokens file saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-7731/special_tokens_map.json\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: label_name, text, lang, cc5, shop, Unnamed: 0.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 13741\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to xlm-roberta-base-finetuned-ecoicop/checkpoint-10308\n",
            "Configuration saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-10308/config.json\n",
            "Model weights saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-10308/pytorch_model.bin\n",
            "tokenizer config file saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-10308/tokenizer_config.json\n",
            "Special tokens file saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-10308/special_tokens_map.json\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='12885' max='12885' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [12885/12885 41:22, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>0.944109</td>\n",
              "    </tr>\n",
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              "      <td>0.192829</td>\n",
              "      <td>0.956917</td>\n",
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              "</table><p>"
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: label_name, text, lang, cc5, shop, Unnamed: 0.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 13741\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to xlm-roberta-base-finetuned-ecoicop/checkpoint-12885\n",
            "Configuration saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-12885/config.json\n",
            "Model weights saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-12885/pytorch_model.bin\n",
            "tokenizer config file saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-12885/tokenizer_config.json\n",
            "Special tokens file saved in xlm-roberta-base-finetuned-ecoicop/checkpoint-12885/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=12885, training_loss=0.35500334654383575, metrics={'train_runtime': 2481.0878, 'train_samples_per_second': 83.072, 'train_steps_per_second': 5.193, 'total_flos': 6925689139051584.0, 'train_loss': 0.35500334654383575, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "808c701a-b01f-489a-bf20-9ded4b6cbaf2"
      },
      "source": [
        "Nice, with just a few mintues of training, we've managed to halve our error compared to the zero-shot baseline! After training is complete, we can push the commits to our repository on the Hub:"
      ],
      "id": "808c701a-b01f-489a-bf20-9ded4b6cbaf2"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48f9dae8-894c-4427-b2fd-8ac4a45d168b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354,
          "referenced_widgets": [
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            "9db5190e7895452e82cbaff8b88296fd"
          ]
        },
        "outputId": "b80ed9e8-d76f-47a0-d2e2-ca4724c35c5e"
      },
      "source": [
        "trainer.push_to_hub(commit_message=\"Training complete!\")"
      ],
      "id": "48f9dae8-894c-4427-b2fd-8ac4a45d168b",
      "execution_count": 44,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Saving model checkpoint to xlm-roberta-base-finetuned-ecoicop\n",
            "Configuration saved in xlm-roberta-base-finetuned-ecoicop/config.json\n",
            "Model weights saved in xlm-roberta-base-finetuned-ecoicop/pytorch_model.bin\n",
            "tokenizer config file saved in xlm-roberta-base-finetuned-ecoicop/tokenizer_config.json\n",
            "Special tokens file saved in xlm-roberta-base-finetuned-ecoicop/special_tokens_map.json\n",
            "Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bc627cd07a4b4cb88ac22593ce5b65b1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 3.37k/1.04G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "16daf4d52f6c4f13914ae73281712955",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file runs/Oct27_07-48-28_cb8cbc9ba39d/events.out.tfevents.1635320949.cb8cbc9ba39d.75.0:  23%|##3       …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop\n",
            "   2c88581..cdc3a09  main -> main\n",
            "\n",
            "Dropping the following result as it does not have all the necessary field:\n",
            "{'task': {'name': 'Text Classification', 'type': 'text-classification'}}\n",
            "To https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop\n",
            "   cdc3a09..4702ced  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/commit/cdc3a0983affba750f5ba428a844805bdf955af8'"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59472032-1c4a-400c-9eb4-324c40515c0c"
      },
      "source": [
        "## Evaluating cross-lingual transfer"
      ],
      "id": "59472032-1c4a-400c-9eb4-324c40515c0c"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02d5d652-5d56-4bae-a444-03c8a7f1e584"
      },
      "source": [
        "Now that we're fine-tuned our model on a English subset, we can evaluate its ability to transfer to other languages. To do so, we'll load the validation set in a given language, apply the same filtering and preprocessing that we did for the English subset, and finally use `Trainer.evaluate()` to compute the metrics. The following function does the trick: "
      ],
      "id": "02d5d652-5d56-4bae-a444-03c8a7f1e584"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72kvAJeCT_-X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 189,
          "referenced_widgets": [
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        },
        "outputId": "e893763b-dd7a-4c1c-d566-e34f67ce0b6f"
      },
      "source": [
        "dataset_fr\n",
        "dataset_fr = dataset_fr.map(map_labels)\n",
        "tokenized_dataset = dataset_fr.map(tokenize_reviews, batched=True)\n",
        "preds = trainer.evaluate(eval_dataset=tokenized_dataset)\n",
        "preds"
      ],
      "id": "72kvAJeCT_-X",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bb606357e22045cf99913cb3ac034a7c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/22833 [00:00<?, ?ex/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "  0%|          | 0/23 [00:00<?, ?ba/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: label_name, text, lang, cc5, shop, Unnamed: 0.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 22833\n",
            "  Batch size = 16\n"
          ]
        },
        {
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        "outputId": "ee82478f-67db-4759-b3a5-bdc9a9e9dcd5"
      },
      "source": [
        "dataset_it\n",
        "dataset_it = dataset_it.map(map_labels)\n",
        "tokenized_dataset = dataset_it.map(tokenize_reviews, batched=True)\n",
        "preds = trainer.evaluate(eval_dataset=tokenized_dataset)\n",
        "preds"
      ],
      "id": "ZHm_Q6fiZzSD",
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f3f3626dea5f4ac5a8504f7549cf0370",
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              "version_major": 2
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            "text/plain": [
              "  0%|          | 0/667 [00:00<?, ?ex/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: label_name, text, lang, cc5, shop, Unnamed: 0.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 667\n",
            "  Batch size = 16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1470' max='1428' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1428/1428 03:16]\n",
              "    </div>\n",
              "    "
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'epoch': 5.0,\n",
              " 'eval_Acc': 0.8935532233883059,\n",
              " 'eval_loss': 0.4839481711387634,\n",
              " 'eval_runtime': 1.2372,\n",
              " 'eval_samples_per_second': 539.123,\n",
              " 'eval_steps_per_second': 33.948}"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77c44999-5a1d-4d34-8a72-81936158bce0"
      },
      "source": [
        "def evaluate_corpus(lang):\n",
        "    # Load the language subset\n",
        "    dataset = load_dataset(dataset_name, lang, split=\"validation\")\n",
        "    # Filter for the `sports` product category\n",
        "    product_dataset = dataset.filter(filter_for_product)\n",
        "    # Map and create label columns\n",
        "    product_dataset = product_dataset.map(map_labels)\n",
        "    # Tokenize the inputs\n",
        "    tokenized_dataset = product_dataset.map(tokenize_reviews, batched=True)\n",
        "    # Generate predictions and metrics\n",
        "    preds = trainer.evaluate(eval_dataset=tokenized_dataset)\n",
        "    return {\"MAE\": preds[\"eval_MAE\"]}"
      ],
      "id": "77c44999-5a1d-4d34-8a72-81936158bce0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93e43e7c-98b6-43b9-83cb-58218b51a45f"
      },
      "source": [
        "Let's start with English (for reference our MAE on English was around 0.5):"
      ],
      "id": "93e43e7c-98b6-43b9-83cb-58218b51a45f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "172ae1fb-e5a3-437d-bfc1-752837b010fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "282203cc-06f3-449a-b26b-17645ef68cdc"
      },
      "source": [
        "evaluate_corpus(\"fr\")"
      ],
      "id": "172ae1fb-e5a3-437d-bfc1-752837b010fa",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-98-ffe25db97383>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevaluate_corpus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"fr\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-97-bc407888c685>\u001b[0m in \u001b[0;36mevaluate_corpus\u001b[0;34m(lang)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mevaluate_corpus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlang\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[1;32m      2\u001b[0m     \u001b[0;31m# Load the language subset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlang\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"validation\"\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[0;31m# Filter for the `sports` product category\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mproduct_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilter_for_product\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'dataset_name' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "584f2bc2-3d74-40f6-b5cb-d1903f0fbbb8"
      },
      "source": [
        "Not bad! Our fine-tuned English model is able to transfer to English at roughly the same performance. How about French?"
      ],
      "id": "584f2bc2-3d74-40f6-b5cb-d1903f0fbbb8"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4be0db14-2abc-4597-ade1-a27196a95e15",
        "outputId": "b94c1277-30f9-4f4e-82ba-b825fcd57fa1"
      },
      "source": [
        "evaluate_corpus(\"ja\")"
      ],
      "id": "4be0db14-2abc-4597-ade1-a27196a95e15",
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Reusing dataset amazon_reviews_multi (/data/.cache/hf/datasets/amazon_reviews_multi/ja/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609)\n",
            "Loading cached processed dataset at /data/.cache/hf/datasets/amazon_reviews_multi/ja/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609/cache-a0b282f870bb7c27.arrow\n",
            "Loading cached processed dataset at /data/.cache/hf/datasets/amazon_reviews_multi/ja/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609/cache-f5ab579eace2ea64.arrow\n",
            "Loading cached processed dataset at /data/.cache/hf/datasets/amazon_reviews_multi/ja/1.0.0/724e94f4b0c6c405ce7e476a6c5ef4f87db30799ad49f765094cf9770e0f7609/cache-e0443037d9ae83f1.arrow\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `XLMRobertaForSequenceClassification.forward` and have been ignored: product_id, review_title, label_name, stars, review_body, review_id, reviewer_id, language, product_category.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 121\n",
            "  Batch size = 16\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'MAE': 0.6776859504132231}"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "233ff17c-aaf1-474a-8baa-4ac95d34f0fb"
      },
      "source": [
        "Nice, this is very similar too! This shows the great power of multilingual models - provided your target language was included in the pretraining, there's a good chance you'll only need to tune and deploy a single model in production instead of running one per language. \n",
        "\n",
        "This wraps up our training and evaluation step - one last thing to try is seeing how we can interact with our model in a `pipeline`."
      ],
      "id": "233ff17c-aaf1-474a-8baa-4ac95d34f0fb"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6af782a3-6b3c-4597-8490-d9ca5ce37933"
      },
      "source": [
        "## Using your fine-tuned model"
      ],
      "id": "6af782a3-6b3c-4597-8490-d9ca5ce37933"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "50100e5f-2344-4baf-9a9d-acf514693651",
        "colab": {
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          ]
        },
        "outputId": "59082c64-59b8-461b-eeea-52e84c297c5b"
      },
      "source": [
        "from transformers import pipeline \n",
        "\n",
        "finetuned_checkpoint = \"peter2000/xlm-roberta-base-finetuned-ecoicop\"\n",
        "classifier = pipeline(\"text-classification\", model=finetuned_checkpoint)"
      ],
      "id": "50100e5f-2344-4baf-9a9d-acf514693651",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpwesd153l\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa798b5ce3b54b988294177e3e4321a5",
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              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/6.29k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "storing https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/config.json in cache at /root/.cache/huggingface/transformers/dd2de7518f860c63bcab28246f666951c4d7bb22ce5e3f0afc097a984fc96aab.bf00b5627699e9c6f7d79a458f776bc57d5aff48595e33bb366189e4ceb9a9f3\n",
            "creating metadata file for /root/.cache/huggingface/transformers/dd2de7518f860c63bcab28246f666951c4d7bb22ce5e3f0afc097a984fc96aab.bf00b5627699e9c6f7d79a458f776bc57d5aff48595e33bb366189e4ceb9a9f3\n",
            "loading configuration file https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/dd2de7518f860c63bcab28246f666951c4d7bb22ce5e3f0afc097a984fc96aab.bf00b5627699e9c6f7d79a458f776bc57d5aff48595e33bb366189e4ceb9a9f3\n",
            "Model config XLMRobertaConfig {\n",
            "  \"_name_or_path\": \"xlm-roberta-base\",\n",
            "  \"architectures\": [\n",
            "    \"XLMRobertaForSequenceClassification\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"1185_Edible ices and ice cream\",\n",
            "    \"1\": \"1116_Pasta products and couscous\",\n",
            "    \"2\": \"1136_Other preserved or processed fish and seafood-based preparations\",\n",
            "    \"3\": \"1112_Flours and other cereals\",\n",
            "    \"4\": \"1212_Tea\",\n",
            "    \"5\": \"1223_Fruit and vegetable juices\",\n",
            "    \"6\": \"9999_Non-Food\",\n",
            "    \"7\": \"1151_Butter\",\n",
            "    \"8\": \"1127_Dried, salted or smoked meat\",\n",
            "    \"9\": \"1182_Jams, marmalades and honey\",\n",
            "    \"10\": \"1192_Salt, spices and culinary herbs\",\n",
            "    \"11\": \"1115_Pizza and quiche\",\n",
            "    \"12\": \"1191_Sauces, condiments\",\n",
            "    \"13\": \"1193_Baby food\",\n",
            "    \"14\": \"1114_Other bakery products\",\n",
            "    \"15\": \"1124_Poultry\",\n",
            "    \"16\": \"1145_Cheese and curd\",\n",
            "    \"17\": \"1173_Dried vegetables, other preserved or processed vegetables\",\n",
            "    \"18\": \"1163_Dried fruit and nuts\",\n",
            "    \"19\": \"1222_Soft drinks\",\n",
            "    \"20\": \"1211_Coffee\",\n",
            "    \"21\": \"1146_Other milk products\",\n",
            "    \"22\": \"1113_Bread\",\n",
            "    \"23\": \"1111_Rice\",\n",
            "    \"24\": \"1161_Fresh or chilled fruit\",\n",
            "    \"25\": \"1142_Fresh low fat milk\",\n",
            "    \"26\": \"2111_Spirits and liqueurs\",\n",
            "    \"27\": \"1132_Frozen fish\",\n",
            "    \"28\": \"1121_Beef and veal\",\n",
            "    \"29\": \"1172_Frozen vegetables other than potatoes and other tubers\",\n",
            "    \"30\": \"1117_Breakfast cereals\",\n",
            "    \"31\": \"1183_Chocolate\",\n",
            "    \"32\": \"1144_Yoghurt\",\n",
            "    \"33\": \"2131_Lager beer\",\n",
            "    \"34\": \"1164_Preserved fruit and fruit-based products\",\n",
            "    \"35\": \"1153_Olive oil\",\n",
            "    \"36\": \"1141_Fresh whole milk\",\n",
            "    \"37\": \"1123_Lamb and goat\",\n",
            "    \"38\": \"1118_Other cereal products\",\n",
            "    \"39\": \"1199_Other food products n.e.c.\",\n",
            "    \"40\": \"1134_Frozen seafood\",\n",
            "    \"41\": \"1171_Fresh or chilled vegetables other than potatoes and other tubers\",\n",
            "    \"42\": \"1126_Edible offal\",\n",
            "    \"43\": \"2121_Wine from grapes\",\n",
            "    \"44\": \"1162_Frozen fruit\",\n",
            "    \"45\": \"1125_Other meats\",\n",
            "    \"46\": \"1184_Confectionery products\",\n",
            "    \"47\": \"1176_Other tubers and products of tuber vegetables\",\n",
            "    \"48\": \"1221_Mineral or spring waters\",\n",
            "    \"49\": \"1174_Potatoes\",\n",
            "    \"50\": \"1154_Other edible oils\",\n",
            "    \"51\": \"1175_Crisps\",\n",
            "    \"52\": \"1128_Other meat preparations\",\n",
            "    \"53\": \"2201_Cigarettes\",\n",
            "    \"54\": \"1181_Sugar\",\n",
            "    \"55\": \"1147_Eggs\",\n",
            "    \"56\": \"1194_Ready-made meals\",\n",
            "    \"57\": \"1133_Fresh or chilled seafood\",\n",
            "    \"58\": \"1122_Pork\",\n",
            "    \"59\": \"2202_Cigars\",\n",
            "    \"60\": \"1186_Artificial sugar substitutes\",\n",
            "    \"61\": \"2122_Wine from other fruits\",\n",
            "    \"62\": \"1155_Other edible animal fats\",\n",
            "    \"63\": \"1135_Dried, smoked or salted fish and seafood\",\n",
            "    \"64\": \"1152_Margarine and other vegetable fats\",\n",
            "    \"65\": \"1143_Preserved milk\",\n",
            "    \"66\": \"2124_Wine-based drinks\",\n",
            "    \"67\": \"2134_Beer-based drinks\",\n",
            "    \"68\": \"1131_Fresh or chilled fish\",\n",
            "    \"69\": \"2112_Alcoholic soft drinks\",\n",
            "    \"70\": \"2133_Low and non-alcoholic beer\",\n",
            "    \"71\": \"2132_Other alcoholic beer\",\n",
            "    \"72\": \"2123_Fortified wines\",\n",
            "    \"73\": \"1213_Cocoa and powdered chocolate\",\n",
            "    \"74\": \"2203_Other tobacco products\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"1111_Rice\": 23,\n",
            "    \"1112_Flours and other cereals\": 3,\n",
            "    \"1113_Bread\": 22,\n",
            "    \"1114_Other bakery products\": 14,\n",
            "    \"1115_Pizza and quiche\": 11,\n",
            "    \"1116_Pasta products and couscous\": 1,\n",
            "    \"1117_Breakfast cereals\": 30,\n",
            "    \"1118_Other cereal products\": 38,\n",
            "    \"1121_Beef and veal\": 28,\n",
            "    \"1122_Pork\": 58,\n",
            "    \"1123_Lamb and goat\": 37,\n",
            "    \"1124_Poultry\": 15,\n",
            "    \"1125_Other meats\": 45,\n",
            "    \"1126_Edible offal\": 42,\n",
            "    \"1127_Dried, salted or smoked meat\": 8,\n",
            "    \"1128_Other meat preparations\": 52,\n",
            "    \"1131_Fresh or chilled fish\": 68,\n",
            "    \"1132_Frozen fish\": 27,\n",
            "    \"1133_Fresh or chilled seafood\": 57,\n",
            "    \"1134_Frozen seafood\": 40,\n",
            "    \"1135_Dried, smoked or salted fish and seafood\": 63,\n",
            "    \"1136_Other preserved or processed fish and seafood-based preparations\": 2,\n",
            "    \"1141_Fresh whole milk\": 36,\n",
            "    \"1142_Fresh low fat milk\": 25,\n",
            "    \"1143_Preserved milk\": 65,\n",
            "    \"1144_Yoghurt\": 32,\n",
            "    \"1145_Cheese and curd\": 16,\n",
            "    \"1146_Other milk products\": 21,\n",
            "    \"1147_Eggs\": 55,\n",
            "    \"1151_Butter\": 7,\n",
            "    \"1152_Margarine and other vegetable fats\": 64,\n",
            "    \"1153_Olive oil\": 35,\n",
            "    \"1154_Other edible oils\": 50,\n",
            "    \"1155_Other edible animal fats\": 62,\n",
            "    \"1161_Fresh or chilled fruit\": 24,\n",
            "    \"1162_Frozen fruit\": 44,\n",
            "    \"1163_Dried fruit and nuts\": 18,\n",
            "    \"1164_Preserved fruit and fruit-based products\": 34,\n",
            "    \"1171_Fresh or chilled vegetables other than potatoes and other tubers\": 41,\n",
            "    \"1172_Frozen vegetables other than potatoes and other tubers\": 29,\n",
            "    \"1173_Dried vegetables, other preserved or processed vegetables\": 17,\n",
            "    \"1174_Potatoes\": 49,\n",
            "    \"1175_Crisps\": 51,\n",
            "    \"1176_Other tubers and products of tuber vegetables\": 47,\n",
            "    \"1181_Sugar\": 54,\n",
            "    \"1182_Jams, marmalades and honey\": 9,\n",
            "    \"1183_Chocolate\": 31,\n",
            "    \"1184_Confectionery products\": 46,\n",
            "    \"1185_Edible ices and ice cream\": 0,\n",
            "    \"1186_Artificial sugar substitutes\": 60,\n",
            "    \"1191_Sauces, condiments\": 12,\n",
            "    \"1192_Salt, spices and culinary herbs\": 10,\n",
            "    \"1193_Baby food\": 13,\n",
            "    \"1194_Ready-made meals\": 56,\n",
            "    \"1199_Other food products n.e.c.\": 39,\n",
            "    \"1211_Coffee\": 20,\n",
            "    \"1212_Tea\": 4,\n",
            "    \"1213_Cocoa and powdered chocolate\": 73,\n",
            "    \"1221_Mineral or spring waters\": 48,\n",
            "    \"1222_Soft drinks\": 19,\n",
            "    \"1223_Fruit and vegetable juices\": 5,\n",
            "    \"2111_Spirits and liqueurs\": 26,\n",
            "    \"2112_Alcoholic soft drinks\": 69,\n",
            "    \"2121_Wine from grapes\": 43,\n",
            "    \"2122_Wine from other fruits\": 61,\n",
            "    \"2123_Fortified wines\": 72,\n",
            "    \"2124_Wine-based drinks\": 66,\n",
            "    \"2131_Lager beer\": 33,\n",
            "    \"2132_Other alcoholic beer\": 71,\n",
            "    \"2133_Low and non-alcoholic beer\": 70,\n",
            "    \"2134_Beer-based drinks\": 67,\n",
            "    \"2201_Cigarettes\": 53,\n",
            "    \"2202_Cigars\": 59,\n",
            "    \"2203_Other tobacco products\": 74,\n",
            "    \"9999_Non-Food\": 6\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"xlm-roberta\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"problem_type\": \"single_label_classification\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.11.3\",\n",
            "  \"type_vocab_size\": 1,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 250002\n",
            "}\n",
            "\n",
            "loading configuration file https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/dd2de7518f860c63bcab28246f666951c4d7bb22ce5e3f0afc097a984fc96aab.bf00b5627699e9c6f7d79a458f776bc57d5aff48595e33bb366189e4ceb9a9f3\n",
            "Model config XLMRobertaConfig {\n",
            "  \"_name_or_path\": \"xlm-roberta-base\",\n",
            "  \"architectures\": [\n",
            "    \"XLMRobertaForSequenceClassification\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"1185_Edible ices and ice cream\",\n",
            "    \"1\": \"1116_Pasta products and couscous\",\n",
            "    \"2\": \"1136_Other preserved or processed fish and seafood-based preparations\",\n",
            "    \"3\": \"1112_Flours and other cereals\",\n",
            "    \"4\": \"1212_Tea\",\n",
            "    \"5\": \"1223_Fruit and vegetable juices\",\n",
            "    \"6\": \"9999_Non-Food\",\n",
            "    \"7\": \"1151_Butter\",\n",
            "    \"8\": \"1127_Dried, salted or smoked meat\",\n",
            "    \"9\": \"1182_Jams, marmalades and honey\",\n",
            "    \"10\": \"1192_Salt, spices and culinary herbs\",\n",
            "    \"11\": \"1115_Pizza and quiche\",\n",
            "    \"12\": \"1191_Sauces, condiments\",\n",
            "    \"13\": \"1193_Baby food\",\n",
            "    \"14\": \"1114_Other bakery products\",\n",
            "    \"15\": \"1124_Poultry\",\n",
            "    \"16\": \"1145_Cheese and curd\",\n",
            "    \"17\": \"1173_Dried vegetables, other preserved or processed vegetables\",\n",
            "    \"18\": \"1163_Dried fruit and nuts\",\n",
            "    \"19\": \"1222_Soft drinks\",\n",
            "    \"20\": \"1211_Coffee\",\n",
            "    \"21\": \"1146_Other milk products\",\n",
            "    \"22\": \"1113_Bread\",\n",
            "    \"23\": \"1111_Rice\",\n",
            "    \"24\": \"1161_Fresh or chilled fruit\",\n",
            "    \"25\": \"1142_Fresh low fat milk\",\n",
            "    \"26\": \"2111_Spirits and liqueurs\",\n",
            "    \"27\": \"1132_Frozen fish\",\n",
            "    \"28\": \"1121_Beef and veal\",\n",
            "    \"29\": \"1172_Frozen vegetables other than potatoes and other tubers\",\n",
            "    \"30\": \"1117_Breakfast cereals\",\n",
            "    \"31\": \"1183_Chocolate\",\n",
            "    \"32\": \"1144_Yoghurt\",\n",
            "    \"33\": \"2131_Lager beer\",\n",
            "    \"34\": \"1164_Preserved fruit and fruit-based products\",\n",
            "    \"35\": \"1153_Olive oil\",\n",
            "    \"36\": \"1141_Fresh whole milk\",\n",
            "    \"37\": \"1123_Lamb and goat\",\n",
            "    \"38\": \"1118_Other cereal products\",\n",
            "    \"39\": \"1199_Other food products n.e.c.\",\n",
            "    \"40\": \"1134_Frozen seafood\",\n",
            "    \"41\": \"1171_Fresh or chilled vegetables other than potatoes and other tubers\",\n",
            "    \"42\": \"1126_Edible offal\",\n",
            "    \"43\": \"2121_Wine from grapes\",\n",
            "    \"44\": \"1162_Frozen fruit\",\n",
            "    \"45\": \"1125_Other meats\",\n",
            "    \"46\": \"1184_Confectionery products\",\n",
            "    \"47\": \"1176_Other tubers and products of tuber vegetables\",\n",
            "    \"48\": \"1221_Mineral or spring waters\",\n",
            "    \"49\": \"1174_Potatoes\",\n",
            "    \"50\": \"1154_Other edible oils\",\n",
            "    \"51\": \"1175_Crisps\",\n",
            "    \"52\": \"1128_Other meat preparations\",\n",
            "    \"53\": \"2201_Cigarettes\",\n",
            "    \"54\": \"1181_Sugar\",\n",
            "    \"55\": \"1147_Eggs\",\n",
            "    \"56\": \"1194_Ready-made meals\",\n",
            "    \"57\": \"1133_Fresh or chilled seafood\",\n",
            "    \"58\": \"1122_Pork\",\n",
            "    \"59\": \"2202_Cigars\",\n",
            "    \"60\": \"1186_Artificial sugar substitutes\",\n",
            "    \"61\": \"2122_Wine from other fruits\",\n",
            "    \"62\": \"1155_Other edible animal fats\",\n",
            "    \"63\": \"1135_Dried, smoked or salted fish and seafood\",\n",
            "    \"64\": \"1152_Margarine and other vegetable fats\",\n",
            "    \"65\": \"1143_Preserved milk\",\n",
            "    \"66\": \"2124_Wine-based drinks\",\n",
            "    \"67\": \"2134_Beer-based drinks\",\n",
            "    \"68\": \"1131_Fresh or chilled fish\",\n",
            "    \"69\": \"2112_Alcoholic soft drinks\",\n",
            "    \"70\": \"2133_Low and non-alcoholic beer\",\n",
            "    \"71\": \"2132_Other alcoholic beer\",\n",
            "    \"72\": \"2123_Fortified wines\",\n",
            "    \"73\": \"1213_Cocoa and powdered chocolate\",\n",
            "    \"74\": \"2203_Other tobacco products\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"1111_Rice\": 23,\n",
            "    \"1112_Flours and other cereals\": 3,\n",
            "    \"1113_Bread\": 22,\n",
            "    \"1114_Other bakery products\": 14,\n",
            "    \"1115_Pizza and quiche\": 11,\n",
            "    \"1116_Pasta products and couscous\": 1,\n",
            "    \"1117_Breakfast cereals\": 30,\n",
            "    \"1118_Other cereal products\": 38,\n",
            "    \"1121_Beef and veal\": 28,\n",
            "    \"1122_Pork\": 58,\n",
            "    \"1123_Lamb and goat\": 37,\n",
            "    \"1124_Poultry\": 15,\n",
            "    \"1125_Other meats\": 45,\n",
            "    \"1126_Edible offal\": 42,\n",
            "    \"1127_Dried, salted or smoked meat\": 8,\n",
            "    \"1128_Other meat preparations\": 52,\n",
            "    \"1131_Fresh or chilled fish\": 68,\n",
            "    \"1132_Frozen fish\": 27,\n",
            "    \"1133_Fresh or chilled seafood\": 57,\n",
            "    \"1134_Frozen seafood\": 40,\n",
            "    \"1135_Dried, smoked or salted fish and seafood\": 63,\n",
            "    \"1136_Other preserved or processed fish and seafood-based preparations\": 2,\n",
            "    \"1141_Fresh whole milk\": 36,\n",
            "    \"1142_Fresh low fat milk\": 25,\n",
            "    \"1143_Preserved milk\": 65,\n",
            "    \"1144_Yoghurt\": 32,\n",
            "    \"1145_Cheese and curd\": 16,\n",
            "    \"1146_Other milk products\": 21,\n",
            "    \"1147_Eggs\": 55,\n",
            "    \"1151_Butter\": 7,\n",
            "    \"1152_Margarine and other vegetable fats\": 64,\n",
            "    \"1153_Olive oil\": 35,\n",
            "    \"1154_Other edible oils\": 50,\n",
            "    \"1155_Other edible animal fats\": 62,\n",
            "    \"1161_Fresh or chilled fruit\": 24,\n",
            "    \"1162_Frozen fruit\": 44,\n",
            "    \"1163_Dried fruit and nuts\": 18,\n",
            "    \"1164_Preserved fruit and fruit-based products\": 34,\n",
            "    \"1171_Fresh or chilled vegetables other than potatoes and other tubers\": 41,\n",
            "    \"1172_Frozen vegetables other than potatoes and other tubers\": 29,\n",
            "    \"1173_Dried vegetables, other preserved or processed vegetables\": 17,\n",
            "    \"1174_Potatoes\": 49,\n",
            "    \"1175_Crisps\": 51,\n",
            "    \"1176_Other tubers and products of tuber vegetables\": 47,\n",
            "    \"1181_Sugar\": 54,\n",
            "    \"1182_Jams, marmalades and honey\": 9,\n",
            "    \"1183_Chocolate\": 31,\n",
            "    \"1184_Confectionery products\": 46,\n",
            "    \"1185_Edible ices and ice cream\": 0,\n",
            "    \"1186_Artificial sugar substitutes\": 60,\n",
            "    \"1191_Sauces, condiments\": 12,\n",
            "    \"1192_Salt, spices and culinary herbs\": 10,\n",
            "    \"1193_Baby food\": 13,\n",
            "    \"1194_Ready-made meals\": 56,\n",
            "    \"1199_Other food products n.e.c.\": 39,\n",
            "    \"1211_Coffee\": 20,\n",
            "    \"1212_Tea\": 4,\n",
            "    \"1213_Cocoa and powdered chocolate\": 73,\n",
            "    \"1221_Mineral or spring waters\": 48,\n",
            "    \"1222_Soft drinks\": 19,\n",
            "    \"1223_Fruit and vegetable juices\": 5,\n",
            "    \"2111_Spirits and liqueurs\": 26,\n",
            "    \"2112_Alcoholic soft drinks\": 69,\n",
            "    \"2121_Wine from grapes\": 43,\n",
            "    \"2122_Wine from other fruits\": 61,\n",
            "    \"2123_Fortified wines\": 72,\n",
            "    \"2124_Wine-based drinks\": 66,\n",
            "    \"2131_Lager beer\": 33,\n",
            "    \"2132_Other alcoholic beer\": 71,\n",
            "    \"2133_Low and non-alcoholic beer\": 70,\n",
            "    \"2134_Beer-based drinks\": 67,\n",
            "    \"2201_Cigarettes\": 53,\n",
            "    \"2202_Cigars\": 59,\n",
            "    \"2203_Other tobacco products\": 74,\n",
            "    \"9999_Non-Food\": 6\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"xlm-roberta\",\n",
            "  \"num_attention_heads\": 12,\n",
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            "  \"output_past\": true,\n",
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            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"problem_type\": \"single_label_classification\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.11.3\",\n",
            "  \"type_vocab_size\": 1,\n",
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            "  \"vocab_size\": 250002\n",
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          "name": "stderr",
          "text": [
            "storing https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/da92dc0d9f359e59ee12e0114cdbbab205af4bd52c7f82e378f38aa2a68a808b.21ed62e526319708fa6c3003c456e65020ec28d7acf40d29e0341a6d64ae1be2\n",
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            "\n",
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            "text/plain": [
              "Downloading:   0%|          | 0.00/8.66M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "storing https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/tokenizer.json in cache at /root/.cache/huggingface/transformers/c100d7dab65ee0bfb7b54ae8c79a57cc4b6d0ba23da791d4fedad282ffc6fd3a.a3b1d1ab4fbe41e81da4e31a6b11eab90ced91130f592dd59b933bccf4751b6b\n",
            "creating metadata file for /root/.cache/huggingface/transformers/c100d7dab65ee0bfb7b54ae8c79a57cc4b6d0ba23da791d4fedad282ffc6fd3a.a3b1d1ab4fbe41e81da4e31a6b11eab90ced91130f592dd59b933bccf4751b6b\n",
            "https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/special_tokens_map.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpg0rw7lsz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5e4986245f224063a9cb9e65b7143108",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "storing https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/special_tokens_map.json in cache at /root/.cache/huggingface/transformers/9024d08c89b951ba679b5a6517b3a74a41275798d06a1642679ab6639caa860d.a11ebb04664c067c8fe5ef8f8068b0f721263414a26058692f7b2e4ba2a1b342\n",
            "creating metadata file for /root/.cache/huggingface/transformers/9024d08c89b951ba679b5a6517b3a74a41275798d06a1642679ab6639caa860d.a11ebb04664c067c8fe5ef8f8068b0f721263414a26058692f7b2e4ba2a1b342\n",
            "loading file https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/sentencepiece.bpe.model from cache at /root/.cache/huggingface/transformers/fe68bb46984f51fec1bd2839ac26e58beee3d33f7fdb28ca06308f69a2ceb251.71e50b08dbe7e5375398e165096cacc3d2086119d6a449364490da6908de655e\n",
            "loading file https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/tokenizer.json from cache at /root/.cache/huggingface/transformers/c100d7dab65ee0bfb7b54ae8c79a57cc4b6d0ba23da791d4fedad282ffc6fd3a.a3b1d1ab4fbe41e81da4e31a6b11eab90ced91130f592dd59b933bccf4751b6b\n",
            "loading file https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/added_tokens.json from cache at None\n",
            "loading file https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/special_tokens_map.json from cache at /root/.cache/huggingface/transformers/9024d08c89b951ba679b5a6517b3a74a41275798d06a1642679ab6639caa860d.a11ebb04664c067c8fe5ef8f8068b0f721263414a26058692f7b2e4ba2a1b342\n",
            "loading file https://huggingface.co/peter2000/xlm-roberta-base-finetuned-ecoicop/resolve/main/tokenizer_config.json from cache at /root/.cache/huggingface/transformers/5f1c25a7d196cb8126ec24d83bbf38019ed1bfda93ae4080278865bf0ebbca4c.b36482fbec4a714d3cfec99e0b05f4fdeec9e759090a78aed5597583a8b4783d\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2582a5b7-0fc1-4b48-9e10-8a8b4ac76af6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "024142d3-4259-415b-adc8-38572aeb62e3"
      },
      "source": [
        "classifier(\"Schwartau Frühstücksmarmelade mit \")"
      ],
      "id": "2582a5b7-0fc1-4b48-9e10-8a8b4ac76af6",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': '1182_Jams, marmalades and honey', 'score': 0.9867764115333557}]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9428f746-d2c2-43b3-ac9d-d5b3a71baf0d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f7b5ff4-c61f-4a40-e1fa-d6c7f525e4bd"
      },
      "source": [
        "classifier(\"Filetes de merluza sin piel PESCANOVA, bolsa 400 g 1 KILO A 10,98 €\")"
      ],
      "id": "9428f746-d2c2-43b3-ac9d-d5b3a71baf0d",
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': '1136_Other preserved or processed fish and seafood-based preparations',\n",
              "  'score': 0.8314335346221924}]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdd590e9-1a71-4e05-b2dc-59190c35b57e"
      },
      "source": [
        ""
      ],
      "id": "fdd590e9-1a71-4e05-b2dc-59190c35b57e",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQSI73aVw-15"
      },
      "source": [
        "# SageMaker"
      ],
      "id": "oQSI73aVw-15"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "id": "RMWMVs86xBBP",
        "outputId": "3c48d51b-26e6-49ea-955b-dd88514dfa8b"
      },
      "source": [
        "!pip install sagemaker"
      ],
      "id": "RMWMVs86xBBP",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "requests 2.23.0 requires urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1, but you have urllib3 1.26.7 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed boto3-1.19.4 botocore-1.22.4 jmespath-0.10.0 pathos-0.2.8 pox-0.3.0 ppft-1.6.6.4 protobuf3-to-dict-0.1.5 s3transfer-0.5.0 sagemaker-2.66.1 smdebug-rulesconfig-1.0.1 urllib3-1.26.7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "QMCFppWPxCxG",
        "outputId": "47bdf2ea-38ec-49bc-8cfa-a380a0485054"
      },
      "source": [
        "import sagemaker\n",
        "import boto3\n",
        "from sagemaker.huggingface import HuggingFace\n",
        "\n",
        "# gets role for executing training job\n",
        "iam_client = boto3.client('iam')\n",
        "role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']\n",
        "hyperparameters = {\n",
        "\t'model_name_or_path':'peter2000/xlm-roberta-base-finetuned-ecoicop',\n",
        "\t'output_dir':'/opt/ml/model'\n",
        "\t# add your remaining hyperparameters\n",
        "\t# more info here https://github.com/huggingface/transformers/tree/v4.6.1/examples/pytorch/text-classification\n",
        "}\n",
        "\n",
        "# git configuration to download our fine-tuning script\n",
        "git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.6.1'}\n",
        "\n",
        "# creates Hugging Face estimator\n",
        "huggingface_estimator = HuggingFace(\n",
        "\tentry_point='run_glue.py',\n",
        "\tsource_dir='./examples/pytorch/text-classification',\n",
        "\tinstance_type='ml.p3.2xlarge',\n",
        "\tinstance_count=1,\n",
        "\trole=role,\n",
        "\tgit_config=git_config,\n",
        "\ttransformers_version='4.6.1',\n",
        "\tpytorch_version='1.7.1',\n",
        "\tpy_version='py36',\n",
        "\thyperparameters = hyperparameters\n",
        ")\n",
        "\n",
        "# starting the train job\n",
        "huggingface_estimator.fit()"
      ],
      "id": "QMCFppWPxCxG",
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NoCredentialsError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNoCredentialsError\u001b[0m                        Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-59-34d6978360f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# gets role for executing training job\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0miam_client\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mboto3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'iam'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mrole\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miam_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_role\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRoleName\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Role'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Arn'\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      8\u001b[0m hyperparameters = {\n\u001b[1;32m      9\u001b[0m         \u001b[0;34m'model_name_or_path'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'peter2000/xlm-roberta-base-finetuned-ecoicop'\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/python3.7/dist-packages/botocore/client.py\u001b[0m in \u001b[0;36m_api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    386\u001b[0m                     \"%s() only accepts keyword arguments.\" % py_operation_name)\n\u001b[1;32m    387\u001b[0m             \u001b[0;31m# The \"self\" in this scope is referring to the BaseClient.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_api_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperation_name\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    389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m         \u001b[0m_api_call\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpy_operation_name\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/python3.7/dist-packages/botocore/client.py\u001b[0m in \u001b[0;36m_make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m    693\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m             http, parsed_response = self._make_request(\n\u001b[0;32m--> 695\u001b[0;31m                 operation_model, request_dict, request_context)\n\u001b[0m\u001b[1;32m    696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    697\u001b[0m         self.meta.events.emit(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/botocore/client.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, operation_model, request_dict, request_context)\u001b[0m\n\u001b[1;32m    712\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_context\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[1;32m    713\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--> 714\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_endpoint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperation_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_dict\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    715\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\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[1;32m    716\u001b[0m             self.meta.events.emit(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/botocore/endpoint.py\u001b[0m in \u001b[0;36mmake_request\u001b[0;34m(self, operation_model, request_dict)\u001b[0m\n\u001b[1;32m    100\u001b[0m         logger.debug(\"Making request for %s with params: %s\",\n\u001b[1;32m    101\u001b[0m                      operation_model, request_dict)\n\u001b[0;32m--> 102\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation_model\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    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation_model\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/botocore/endpoint.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, request_dict, operation_model)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation_model\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[1;32m    131\u001b[0m         \u001b[0mattempts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m         \u001b[0mrequest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation_model\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    133\u001b[0m         \u001b[0mcontext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequest_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'context'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         success_response, exception = self._get_response(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/botocore/endpoint.py\u001b[0m in \u001b[0;36mcreate_request\u001b[0;34m(self, params, operation_model)\u001b[0m\n\u001b[1;32m    114\u001b[0m                 op_name=operation_model.name)\n\u001b[1;32m    115\u001b[0m             self._event_emitter.emit(event_name, request=request,\n\u001b[0;32m--> 116\u001b[0;31m                                      operation_name=operation_model.name)\n\u001b[0m\u001b[1;32m    117\u001b[0m         \u001b[0mprepared_request\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mprepared_request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/botocore/hooks.py\u001b[0m in \u001b[0;36memit\u001b[0;34m(self, event_name, **kwargs)\u001b[0m\n\u001b[1;32m    355\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0memit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_name\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m         \u001b[0maliased_event_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alias_event_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_emitter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0memit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maliased_event_name\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    358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0memit_until_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_name\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/botocore/hooks.py\u001b[0m in \u001b[0;36memit\u001b[0;34m(self, event_name, **kwargs)\u001b[0m\n\u001b[1;32m    226\u001b[0m                  \u001b[0mhandlers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m         \"\"\"\n\u001b[0;32m--> 228\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_emit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent_name\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    229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0memit_until_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_name\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/botocore/hooks.py\u001b[0m in \u001b[0;36m_emit\u001b[0;34m(self, event_name, kwargs, stop_on_response)\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers_to_call\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Event %s: calling handler %s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhandler\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    212\u001b[0m             \u001b[0mresponses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstop_on_response\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[0;32m/usr/local/lib/python3.7/dist-packages/botocore/signers.py\u001b[0m in \u001b[0;36mhandler\u001b[0;34m(self, operation_name, request, **kwargs)\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;31m# this method is invoked to sign the request.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m         \u001b[0;31m# Don't call this method directly.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperation_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\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     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     def sign(self, operation_name, request, region_name=None,\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/botocore/signers.py\u001b[0m in \u001b[0;36msign\u001b[0;34m(self, operation_name, request, region_name, signing_type, expires_in, signing_name)\u001b[0m\n\u001b[1;32m    163\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m             \u001b[0mauth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_auth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\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    166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_choose_signer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigning_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\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[0;32m/usr/local/lib/python3.7/dist-packages/botocore/auth.py\u001b[0m in \u001b[0;36madd_auth\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    376\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_auth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\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[1;32m    377\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcredentials\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[0;32m--> 378\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mNoCredentialsError\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    379\u001b[0m         \u001b[0mdatetime_now\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutcnow\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[1;32m    380\u001b[0m         \u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'timestamp'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime_now\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSIGV4_TIMESTAMP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNoCredentialsError\u001b[0m: Unable to locate credentials"
          ]
        }
      ]
    }
  ]
}