{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GTsdkk_6TGX5"
      },
      "source": [
        "# **STEP 1: Environment Setup & Ecosystem Basics**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4IaW9ezfXVz"
      },
      "source": [
        "# Research/Learn:\n",
        "Hugging Face Hub: A central repository for hosting models and datasets, functioning similarly to GitHub for Machine Learning.\n",
        "\n",
        "Core Libraries:\n",
        "\n",
        "*  transformers: The primary library for downloading and using pre-trained models.\n",
        "\n",
        "*  tokenizers: A library to convert raw data into numerical tensors that machines can process.\n",
        "\n",
        "*  datasets: A library for accessing and managing training data for various AI models.\n",
        "\n",
        "*  accelerate: A tool to simplify and speed up training across different hardware configurations.\n",
        "\n",
        "*  peft: Implements LoRA (Low-Rank Adaptation) for efficient and fast model fine-tuning.\n",
        "\n",
        "*  trl: A library for Transformer Reinforcement Learning.\n",
        "\n",
        "*  bitsandbytes: A library for model quantization (compressing models to 8-bit or 4-bit) to run smoothly on limited GPU memory.\n",
        "\n",
        "Google Colab: A cloud-based platform for running Jupyter Notebooks with GPU support.\n",
        "\n",
        "Authentication: Using an Access Token to securely authenticate with Hugging Face without manual password entry"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWMd1cYLiQdC"
      },
      "source": [
        "Because I have a trouble prolbem with the Billing issue in Google Cloud Platform, I use collab to make notebook for step 1\n",
        "\n",
        "#Actions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351,
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          ]
        },
        "id": "Y5GaVMmXPfiz",
        "outputId": "0ff6aed8-a3df-47a5-f415-26af1450985e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1825461cf5984f88968adc2ddb4158cd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Processing Files (0 / 0)      : |          |  0.00B /  0.00B            "
            ]
          },
          "metadata": {},
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        },
        {
          "data": {
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              "model_id": "5f9fecdc86a84e8c8a083464999962b4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "New Data Upload               : |          |  0.00B /  0.00B            "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3c2dc7576a7e4582b1cbc6560f35b36d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  /content/vectorizer.joblib  : 100%|##########|  182kB /  182kB            "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "daf6420690884054ae8b7c45103b5e38",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  /content/model.joblib       : 100%|##########|  161kB /  161kB            "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dbebc5f1583741d397b2d324098b48eb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  ...ata/mnist_train_small.csv: 100%|##########| 36.5MB / 36.5MB            "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0f5be051488242a3abc37dcc0f1989e3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  ...ample_data/mnist_test.csv: 100%|##########| 18.3MB / 18.3MB            "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No files have been modified since last commit. Skipping to prevent empty commit.\n",
            "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/duyb2207513/step1/commit/cdc7bd40797e5c858ad4a17a2dd39e830a6ca920', commit_message='Upload folder using huggingface_hub', commit_description='', oid='cdc7bd40797e5c858ad4a17a2dd39e830a6ca920', pr_url=None, repo_url=RepoUrl('https://huggingface.co/duyb2207513/step1', endpoint='https://huggingface.co', repo_type='model', repo_id='duyb2207513/step1'), pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from huggingface_hub import login, upload_folder\n",
        "login()\n",
        "upload_folder(folder_path=\".\", repo_id=\"duyb2207513/step1\", repo_type=\"model\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jg1ZWKZ7idj3"
      },
      "source": [
        "Cài đặt các thư viện chính:\n",
        "Because colab have all of those library, so i don't install again."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_bm8gRnbTVxS"
      },
      "outputs": [],
      "source": [
        "\n",
        "# !pip install transformers datasets tokenizers accelerate peft trl bitsandbytes huggingface_hub\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpQ2K3KgilDM"
      },
      "source": [
        " Successful login to HF and running basic inference from a Hub model\n",
        " # Milestone 1:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264,
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        "id": "6IZQSvAfYM4H",
        "outputId": "a7169e6a-bf0f-4582-c83a-6dc437fd98cf"
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      "outputs": [
        {
          "data": {
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              "model_id": "7a0866e7251246d480d4e6e733767c3a",
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            "text/plain": [
              "config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
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            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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        },
        {
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              "vocab.txt: 0.00B [00:00, ?B/s]"
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        {
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            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
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          },
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        },
        {
          "data": {
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              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading weights:   0%|          | 0/104 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1. Raw scores (Logits): [[-4.351162910461426, 4.709738731384277]]\n",
            "2. Probabilities (%): [[0.0001161047475761734, 0.9998838901519775]]\n",
            "\n",
            "Input sentence: 'This movie is absolutely amazing! I loved it.'\n",
            "AI conclusion: POSITIVE\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "# 1. Define the model name (A model already fine-tuned for sentiment analysis)\n",
        "model_name = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
        "\n",
        "# 2. Provide a sample sentence (e.g., a compliment)\n",
        "text = \"This movie is absolutely amazing! I loved it.\"\n",
        "\n",
        "# Tokenize the sentence and return PyTorch tensors\n",
        "inputs = tokenizer(text, return_tensors=\"pt\")\n",
        "\n",
        "# 3. Feed the input into the model\n",
        "with torch.no_grad():\n",
        "    outputs = model(**inputs)\n",
        "\n",
        "# 4. DECODE THE OUTPUT\n",
        "# Extract the raw mathematical scores calculated by the model\n",
        "logits = outputs.logits\n",
        "print(\"1. Raw scores (Logits):\", logits.tolist())\n",
        "\n",
        "# Convert raw scores into probabilities (percentages) using the Softmax function\n",
        "probabilities = torch.nn.functional.softmax(logits, dim=-1)\n",
        "print(\"2. Probabilities (%):\", probabilities.tolist())\n",
        "# Expected output format: [[0.0001, 0.9999]] (meaning 0.01% Negative, 99.99% Positive)\n",
        "\n",
        "# Select the index (ID) with the highest probability score using argmax\n",
        "predicted_class_id = torch.argmax(probabilities).item()\n",
        "\n",
        "# Map the predicted ID back to a human-readable string label using the model's configuration\n",
        "predicted_label = model.config.id2label[predicted_class_id]\n",
        "\n",
        "# Print the final result\n",
        "print(f\"\\nInput sentence: '{text}'\")\n",
        "print(f\"AI conclusion: {predicted_label}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALHRr2Tni0uA"
      },
      "source": [
        " # Milestone 2:"
      ]
    },
    {
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          },
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          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "`torch_dtype` is deprecated! Use `dtype` instead!\n",
            "`torch_dtype` is deprecated! Use `dtype` instead!\n"
          ]
        },
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Passing `generation_config` together with generation-related arguments=({'max_new_tokens'}) is deprecated and will be removed in future versions. Please pass either a `generation_config` object OR all generation parameters explicitly, but not both.\n",
            "Both `max_new_tokens` (=256) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
          ]
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        {
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          "text": [
            "## Hugging Face: Nền tảng cho trí tuệ nhân tạo\n",
            "\n",
            "Hugging Face là một nền tảng trực tuyến cho việc phát triển và chia sẻ các mô hình trí tuệ nhân tạo (AI). Nó cung cấp một kho repository phong phú với:\n",
            "\n",
            "* **Mô hình AI:** Từ các mô hình ngôn ngữ, phân loại hình ảnh, đến các thuật toán xử lý âm thanh, bạn có thể tìm kiếm và sử dụng các mô hình AI đã được tạo ra.\n",
            "* **Tạo dựng mô hình:**  Sử dụng công cụ mạnh mẽ trong Hugging Face để xây dựng và fine-tuning các mô hình AI riêng của bạn.\n",
            "* **Chia sẻ và kết nối:** Chia sẻ mã nguồn và mô hình với cộng đồng, trao đổi kinh nghiệm và thu thập ý tưởng.\n",
            "\n",
            "\n",
            "Hugging Face giúp cho việc sử dụng AI trở nên dễ dàng hơn, mở ra cánh cửa cho các nhà phát triển và người dùng, từ dân số bình dân đến các doanh nghiệp lớn. \n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Pipeline:\n",
        "\n",
        "#Run inference basic:\n",
        "from transformers import pipeline\n",
        "import torch\n",
        "\n",
        "# 1 model example\n",
        "model_id = \"google/gemma-2-2b-it\"\n",
        "\n",
        "# 2. Make an pipeline for conversation\n",
        "# pineline will automatic download model\n",
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model_id,\n",
        "    model_kwargs={\"torch_dtype\": torch.bfloat16},\n",
        "    device=\"cuda\", # Chạy bằng GPU cho nhanh\n",
        ")\n",
        "\n",
        "# 3. Give a question like this:\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": \"Chào bạn, hãy giới thiệu ngắn gọn về Hugging Face bằng tiếng Việt.\"},\n",
        "]\n",
        "\n",
        "outputs = pipe(messages, max_new_tokens=256)\n",
        "\n",
        "# 4. the result\n",
        "print(outputs[0][\"generated_text\"][-1][\"content\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DV-4AFqRTMuy"
      },
      "source": [
        "# **STEP 2 Standard Training Pipelines for ML Models**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rRkKpotKksfd"
      },
      "source": [
        "# Research/Learn:\n",
        "A standard Machine Learning (ML) training workflow using Hugging Face (HF) involves loading datasets with datasets, pre-processing using tokenizers, fine-tuning models via transformers.Trainer, and evaluating performance. This iterative process optimizes models using pre-trained weights for specific tasks, utilizing GPU acceleration for efficiency. Steps in the HF Workflow:\n",
        "* Data Preparation: Load datasets using the HF Datasets library and preprocess data (e.g., tokenization) using HF Tokenizers to convert text into numerical input suitable for models.\n",
        "* Model Selection: Select a pre-trained model architecture from HF Transformers appropriate for the task (e.g., classification, generation).\n",
        "* Training Setup: Utilize the Trainer API to define training arguments such as learning rate, batch size, and evaluation strategies.\n",
        "* Training: Fine-tune the model on your dataset.Evaluation: Assess the model's performance on a validation set, commonly tracking metrics like accuracy, F1-score, or perplexity.\n",
        "* Sharing: Push the final model to the Hugging Face Hub to share or deploy\n",
        "\n",
        "Differences from DL and LLM is:Deep Learning is child of Machine Learning, using a lot of neural network layer to simulate how brain process data. LLM (Large Language Model) is application of Deep learning that using a special neural network architecture is called Transformer to process text.\n",
        "\n",
        "Data preprocessing: is the progress that make cleaned data.\n",
        "\n",
        "Evaluation metrics are used to measure how well a machine learning model performs on a given task. Different tasks require different metrics. For example, with clasification tasks use accuracy, F1-Score,; with regression tasks use MSE,MAE,.; with seq2seq tasks use rouge or perplexity,...\n",
        "\n",
        "Trainer API in Hugging Face simplifies the training and evaluation process of transformer models by providing a high-level interface."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Te9kCiKktGuQ"
      },
      "outputs": [],
      "source": [
        "# Example about Trainer API\n",
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=8,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MsQqUIm4TC8E"
      },
      "outputs": [],
      "source": [
        "#Action1: Load dataset\n",
        "from datasets import load_dataset\n",
        "# load iris classification dataset\n",
        "dataset = load_dataset(\"hitorilabs/iris\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RRtc1kblV_I3",
        "outputId": "4ac12275-cd80-43ff-f67a-9a8d4a0ecaae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['petal_length', 'petal_width', 'sepal_length', 'sepal_width', 'species'],\n",
              "    num_rows: 150\n",
              "})"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset['train']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m7L-R4iBWzdA"
      },
      "outputs": [],
      "source": [
        "train_ds = dataset['train']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wyiDNHcrVAGc",
        "outputId": "99f3bdb9-5b73-4d27-b76f-b012681e0bf2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   petal_length  petal_width  sepal_length  sepal_width  species\n",
            "0      0.067797     0.041667      0.222222     0.625000        0\n",
            "1      0.067797     0.041667      0.166667     0.416667        0\n",
            "2      0.050847     0.041667      0.111111     0.500000        0\n",
            "3      0.084746     0.041667      0.083333     0.458333        0\n",
            "4      0.067797     0.041667      0.194444     0.666667        0\n"
          ]
        }
      ],
      "source": [
        "#Action 2:normalize to [0,1]\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import pandas as pd\n",
        "\n",
        "def normalize_dataset(dataset):\n",
        "    # Convert HF Dataset -> pandas\n",
        "    df = dataset.to_pandas()\n",
        "\n",
        "    feature_cols = ['petal_length', 'petal_width', 'sepal_length', 'sepal_width']\n",
        "\n",
        "    # Khởi tạo scaler\n",
        "    scaler = MinMaxScaler()\n",
        "\n",
        "    # Fit + transform\n",
        "    df[feature_cols] = scaler.fit_transform(df[feature_cols])\n",
        "\n",
        "    return df, scaler\n",
        "# Convert sang pandas\n",
        "df = train_ds.to_pandas()\n",
        "\n",
        "# Feature columns\n",
        "feature_cols = ['petal_length', 'petal_width', 'sepal_length', 'sepal_width']\n",
        "\n",
        "# Normalize\n",
        "scaler = MinMaxScaler()\n",
        "df[feature_cols] = scaler.fit_transform(df[feature_cols])\n",
        "\n",
        "print(df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "z04-nWMBX4ES",
        "outputId": "97283e7b-0d71-4e47-b5cc-22f59235a737"
      },
      "outputs": [
        {
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              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GaussianNB</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "GaussianNB()"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "#Action3 : select model, I load native Bayes model\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "#split dataset:\n",
        "X_train, X_test, y_train, y_test = train_test_split(df[feature_cols], df['species'], test_size=0.2, random_state=42)\n",
        "#Action 4: training model\n",
        "model=GaussianNB()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ZIZTCM_Y2yB",
        "outputId": "b633d60d-1a9e-4e8e-f504-cdbe882f03ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 1.0\n"
          ]
        }
      ],
      "source": [
        "#4 evaluating model\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NG-dlHfodPST"
      },
      "source": [
        "# Milestone:\n",
        "\n",
        "Fully trained ML model (e.g., text classifier) pushed to HF Hub with\n",
        "model card."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ozad-glcbXVX"
      },
      "outputs": [],
      "source": [
        "\n",
        "from datasets import load_dataset\n",
        "dataset= load_dataset(\"ag_news\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ad2BGlC5bxhx"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "texts = dataset[\"train\"][\"text\"]\n",
        "labels = dataset[\"train\"][\"label\"]\n",
        "\n",
        "vectorizer = TfidfVectorizer(max_features=5000)\n",
        "X = vectorizer.fit_transform(texts)\n",
        "y = labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "VdmJBtRab6t5",
        "outputId": "f3c5fd10-fc11-4b09-e4c4-ff34d53e043a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=200)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression(max_iter=200)"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model = LogisticRegression(max_iter=200)\n",
        "model.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jor70W1Kb9EO",
        "outputId": "01e6835c-db16-40e0-9c0a-e1e2a0af7432"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9063157894736842\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "X_test = vectorizer.transform(dataset[\"test\"][\"text\"])\n",
        "y_test = dataset[\"test\"][\"label\"]\n",
        "\n",
        "preds = model.predict(X_test)\n",
        "\n",
        "print(\"Accuracy:\", accuracy_score(y_test, preds))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 385,
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        },
        "id": "NAfcQxMucEM2",
        "outputId": "24941be9-4c4c-4766-effc-d352a07b0b2c"
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      "outputs": [
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              "  model.joblib                : 100%|##########|  161kB /  161kB            "
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            "No files have been modified since last commit. Skipping to prevent empty commit.\n",
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              "model_id": "5861b9691df74f2eada053ef5b472c83",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "New Data Upload               : |          |  0.00B /  0.00B            "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "02f2e35b4dfa45269ee70a3216568165",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  vectorizer.joblib           : 100%|##########|  182kB /  182kB            "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No files have been modified since last commit. Skipping to prevent empty commit.\n",
            "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/duyb2207513/step1/commit/cdc7bd40797e5c858ad4a17a2dd39e830a6ca920', commit_message='Upload vectorizer.joblib with huggingface_hub', commit_description='', oid='cdc7bd40797e5c858ad4a17a2dd39e830a6ca920', pr_url=None, repo_url=RepoUrl('https://huggingface.co/duyb2207513/step1', endpoint='https://huggingface.co', repo_type='model', repo_id='duyb2207513/step1'), pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from huggingface_hub import HfApi\n",
        "import joblib\n",
        "\n",
        "# Save model\n",
        "joblib.dump(model, \"model.joblib\")\n",
        "joblib.dump(vectorizer, \"vectorizer.joblib\")\n",
        "\n",
        "api = HfApi()\n",
        "\n",
        "\n",
        "repo_type=\"model\"\n",
        "api.upload_file(\n",
        "    path_or_fileobj=\"model.joblib\",\n",
        "    path_in_repo=\"model.joblib\",\n",
        "    repo_id=\"duyb2207513/step1\"\n",
        ")\n",
        "\n",
        "api.upload_file(\n",
        "    path_or_fileobj=\"vectorizer.joblib\",\n",
        "    path_in_repo=\"vectorizer.joblib\",\n",
        "    repo_id=\"duyb2207513/step1\"\n",
        ")"
      ]
    },
    {
      "attachments": {
        "image.png": {
          "image/png": 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      },
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        " Model card.\n",
        "![image.png](attachment:image.png)"
      ]
    }
  ],
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    "colab": {
      "gpuType": "T4",
      "provenance": []
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      "display_name": "Python 3",
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    },
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      "name": "python"
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