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            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "215fbd48e91d4df9926525e6bc8d44cc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PerSpbcFqvHA"
      },
      "outputs": [],
      "source": [
        "!pip install -q transformers datasets torch > /dev/null"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\"sentiment-analysis\")"
      ],
      "metadata": {
        "id": "QAI5NlUWrew8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img2text = pipeline(task='image-to-text',model='google/pix2struct-textcaps-base')"
      ],
      "metadata": {
        "id": "1tFMOLTVj2Y_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img2text(\"/content/evaluate_huggingface.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rhvu0iQJkMLG",
        "outputId": "d506dea4-8cd6-4522-a80e-36fdfd38d3dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1313: UserWarning: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': 'A book titled Mastering Huggingface Model Evaluation.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier(\"There is a lot to learn from the new models.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pHaqRToxnCf",
        "outputId": "e33e4c28-8b02-4c21-f547-15b303ef3467"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9977133274078369}]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"nlptown/bert-base-multilingual-uncased-sentiment\""
      ],
      "metadata": {
        "id": "fwGPhIBuyRJW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "id": "_a7fjJsoxnHL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoding = tokenizer(\"We are very happy to show you the 🤗 Transformers library.\")"
      ],
      "metadata": {
        "id": "Im6ePeIwxnLT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoding"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WfJOabLSxnOb",
        "outputId": "1bb4fe8e-d682-42be-8bc8-84c12f72a639"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 11312, 10320, 12495, 19308, 10114, 11391, 10855, 10103, 100, 58263, 13299, 119, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pt_batch = tokenizer(\n",
        "    [\"We are very happy to show you the 🤗 Transformers library.\", \n",
        "     \"We hope you don't hate it.\"],\n",
        "    padding=True,\n",
        "    truncation=True,\n",
        "    max_length=512,\n",
        "    return_tensors=\"pt\",\n",
        ")"
      ],
      "metadata": {
        "id": "9h2mJZ34zDX6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(pt_batch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AcoO8odbzDNd",
        "outputId": "e17e790b-8d4c-4ad6-e09a-25bc3206c0b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "transformers.tokenization_utils_base.BatchEncoding"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pt_batch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6Q5h3--zDI4",
        "outputId": "26c9c9c5-ceb7-4321-9f5e-9bf6e9f20b95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[  101, 11312, 10320, 12495, 19308, 10114, 11391, 10855, 10103,   100,\n",
              "         58263, 13299,   119,   102],\n",
              "        [  101, 11312, 18763, 10855, 11530,   112,   162, 39487, 10197,   119,\n",
              "           102,     0,     0,     0]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]])}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model(**pt_batch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kpb8gOLpzX1r",
        "outputId": "65ae4656-1370-42e6-aa2a-24ee9d2bb54e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SequenceClassifierOutput(loss=None, logits=tensor([[-2.6222, -2.7745, -0.8967,  2.0137,  3.3064],\n",
              "        [ 0.0064, -0.1258, -0.0503, -0.1655,  0.1329]],\n",
              "       grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "\n",
        "pt_pred = nn.functional.softmax(model(**pt_batch).logits, \n",
        "                                dim=-1)"
      ],
      "metadata": {
        "id": "wXcgOMCazX7S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pt_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ox2bijyYzX_7",
        "outputId": "dbd6c505-2531-428d-a085-3e7c26c6d27d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],\n",
              "        [0.2084, 0.1826, 0.1969, 0.1755, 0.2365]], grad_fn=<SoftmaxBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pt_save_directory = \"./pt_save_pretrained\"\n",
        "tokenizer.save_pretrained(pt_save_directory)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JC0syYSYzYFZ",
        "outputId": "29143949-9c0b-4d4f-9953-a2d318d28166"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('./pt_save_pretrained/tokenizer_config.json',\n",
              " './pt_save_pretrained/special_tokens_map.json',\n",
              " './pt_save_pretrained/vocab.txt',\n",
              " './pt_save_pretrained/added_tokens.json',\n",
              " './pt_save_pretrained/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoConfig\n",
        "\n",
        "my_config = AutoConfig.from_pretrained(\"distilbert-base-uncased\", \n",
        "                                       n_heads=12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "64b07fdbfee6429496cd484f48a1c6db",
            "cdbc61804b234ffab0116328465e48f6",
            "506d6a80bdc045d8b291d7430671ae7a",
            "eb670ee24e7c4c4bbcf108ba14b15c60",
            "f8f7bf8dc69c4513843eee7c4f6c8036",
            "502caf2db2344c618b99ef0ec0f379c0",
            "c7f15d407caa49338e3d8767046ac9df",
            "77f350a1dc4f4ee693281001fce96d33",
            "a913597c87ee4b7fb6a15e76c18d41b8",
            "9aa2cd574a3f47c1b5c1ea28624a580a",
            "01083fae066544ab8abad88d97247f40"
          ]
        },
        "id": "V3sCrhpAzYIm",
        "outputId": "e09239e8-e2e8-4b43-d23b-f230f1244ec5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64b07fdbfee6429496cd484f48a1c6db"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_config"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MlzH08yzYND",
        "outputId": "d7174f1a-698d-4d76-db57-00ca5e2e163c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DistilBertConfig {\n",
              "  \"_name_or_path\": \"distilbert-base-uncased\",\n",
              "  \"activation\": \"gelu\",\n",
              "  \"architectures\": [\n",
              "    \"DistilBertForMaskedLM\"\n",
              "  ],\n",
              "  \"attention_dropout\": 0.1,\n",
              "  \"dim\": 768,\n",
              "  \"dropout\": 0.1,\n",
              "  \"hidden_dim\": 3072,\n",
              "  \"initializer_range\": 0.02,\n",
              "  \"max_position_embeddings\": 512,\n",
              "  \"model_type\": \"distilbert\",\n",
              "  \"n_heads\": 12,\n",
              "  \"n_layers\": 6,\n",
              "  \"pad_token_id\": 0,\n",
              "  \"qa_dropout\": 0.1,\n",
              "  \"seq_classif_dropout\": 0.2,\n",
              "  \"sinusoidal_pos_embds\": false,\n",
              "  \"tie_weights_\": true,\n",
              "  \"transformers_version\": \"4.28.1\",\n",
              "  \"vocab_size\": 30522\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel\n",
        "\n",
        "my_model = AutoModel.from_config(my_config)"
      ],
      "metadata": {
        "id": "5rRIDDey0X_3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"path/to/save/folder/\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    num_train_epochs=2,\n",
        ")"
      ],
      "metadata": {
        "id": "ubQnU4LB0X9L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"rotten_tomatoes\")"
      ],
      "metadata": {
        "id": "rkwSk6Xr0X5_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_dataset(dataset):\n",
        "    return tokenizer(dataset[\"text\"])"
      ],
      "metadata": {
        "id": "19pRds7F0X3Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.map(tokenize_dataset, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "0a08fe2c01fc47c7814b0be46afa75a8",
            "a63f555180794dc4afdfd2b2231ebce9",
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            "961162116aec4a91a01d4ea4105fada6",
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            "7bc1ef54475347859d00d43899868817",
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            "bb176ed656634a28bca12f67f06ee2ca",
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            "cd3c7596d52b42f6a5f0fdfebe6e40ff",
            "4d112685a0be406bbebafa4b9cbf6feb",
            "998d585385df4d9dbdd528186a46122e",
            "1034e1dc83db4d0ea07425167ad93932",
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            "67b5e4b8e1a544b2b30e4ff9c97d2b88",
            "b596d50f268040418047047e148a8c7f",
            "13d170d9f1cb483e85995168019f20c2",
            "15ae293ba2e7473aa98c11647b762c2b",
            "533fb987fbe84783853df8fbd521e944",
            "3a3dfd4d0dc548df81ba1f99ad35cfa4",
            "15b965f60a5d4825b8691725d22da7b2",
            "8399dcfa9a044459b91960e844874ca0",
            "b74d147a74814490aa81b1ce0803c902",
            "fcb230b35de54dee94ac90eb68ea289c",
            "63425dbb5ca244eb8e24ebb703611b3a",
            "494f175ccb564ae68cd8d895fed897e5",
            "5445e74a6faf4e08bc48861449b793ea",
            "215fbd48e91d4df9926525e6bc8d44cc"
          ]
        },
        "id": "t0ckmkPj0X0X",
        "outputId": "cc683b0c-0e2d-4cf0-8286-8b7ea0781e75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/8530 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a08fe2c01fc47c7814b0be46afa75a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1066 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cd3c7596d52b42f6a5f0fdfebe6e40ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1066 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15ae293ba2e7473aa98c11647b762c2b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['train'][0]"
      ],
      "metadata": {
        "id": "QzRJ1xRf0Xxr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "1K1Kfyo00XsO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=dataset[\"train\"],\n",
        "    eval_dataset=dataset[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        ")"
      ],
      "metadata": {
        "id": "ymAeW2xn1SPJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "797UO4cG1SM8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Dt7Mcvty1SGy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zb6qn8pV1SDO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}