mistral/classifier_factory/moderation_classifier.ipynb

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        "# Cookbook: Moderation Classifier\n",
        "\n",
        "In this cookbook, we will explore classification for moderation using our Classifier Factory to build classifiers tailored to your specific needs and use cases.\n",
        "\n",
        "To keep things straightforward, we will concentrate on a particular example that involves multilabel classification for content moderation."
      ]
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        "## Dataset\n",
        "We will use a subset of the [google/civil_comments](https://huggingface.co/datasets/google/civil_comments) dataset. This subset includes several labels that we will for multi-label classification, allowing us to obtain scores for each type of moderation.\n",
        "\n",
        "### Subset\n",
        "Lets download and prepare the subset, we will install `datasets` and load it."
      ]
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      "source": [
        "%%capture\n",
        "!pip install datasets"
      ]
    },
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      "source": [
        "# @title Loading subset\n",
        "%%capture\n",
        "from datasets import load_dataset\n",
        "import pandas as pd\n",
        "import random\n",
        "\n",
        "# Load the civil_comments dataset with streaming\n",
        "dataset = load_dataset(\"google/civil_comments\", streaming=True)\n",
        "\n",
        "# Select only a subset\n",
        "n_train = 1_000_000\n",
        "n_validation = 50_000\n",
        "n_test = 50_000\n",
        "\n",
        "\n",
        "# Function to convert scores to booleans depending on a threshold\n",
        "def convert_scores_to_booleans(example, threshold=0.5):\n",
        "    return {\n",
        "        \"text\": example[\"text\"],\n",
        "        \"toxicity\": example[\"toxicity\"] > threshold,\n",
        "        \"severe_toxicity\": example[\"severe_toxicity\"] > threshold,\n",
        "        \"obscene\": example[\"obscene\"] > threshold,\n",
        "        \"threat\": example[\"threat\"] > threshold,\n",
        "        \"insult\": example[\"insult\"] > threshold,\n",
        "        \"identity_attack\": example[\"identity_attack\"] > threshold,\n",
        "        \"sexual_explicit\": example[\"sexual_explicit\"] > threshold,\n",
        "    }\n",
        "\n",
        "\n",
        "# Shuffle, take, and convert the dataset splits\n",
        "train_samples = [\n",
        "    convert_scores_to_booleans(example)\n",
        "    for example in dataset[\"train\"].shuffle(seed=42, buffer_size=n_train).take(n_train)\n",
        "]\n",
        "validation_samples = [\n",
        "    convert_scores_to_booleans(example)\n",
        "    for example in dataset[\"validation\"]\n",
        "    .shuffle(seed=42, buffer_size=n_validation)\n",
        "    .take(n_validation)\n",
        "]\n",
        "test_samples = [\n",
        "    convert_scores_to_booleans(example)\n",
        "    for example in dataset[\"test\"].shuffle(seed=42, buffer_size=n_test).take(n_test)\n",
        "]\n",
        "\n",
        "\n",
        "# Naive filter that removes 90% of samples with zero flags and next ensures each label represents max 20% of total flagged samples\n",
        "def filter_dataset(samples, zero_flags_percentage=0.1, max_percentage=0.2):\n",
        "    zero_flags_samples = []\n",
        "    non_zero_flags_samples = []\n",
        "    label_counts = {key: 0 for key in samples[0] if key != \"text\"}\n",
        "\n",
        "    for example in samples:\n",
        "        if not any(\n",
        "            example[key] for key in example if key != \"text\"\n",
        "        ):  # Check if all flags are False\n",
        "            zero_flags_samples.append(example)\n",
        "        else:\n",
        "            non_zero_flags_samples.append(example)\n",
        "\n",
        "    # Calculate the total number of samples needed\n",
        "    total_samples = len(non_zero_flags_samples) / (1 - zero_flags_percentage)\n",
        "\n",
        "    # Calculate the number of zero-flag samples needed\n",
        "    desired_zero_flags = int(total_samples * zero_flags_percentage)\n",
        "    desired_non_zero_flags = int(total_samples * (1 - zero_flags_percentage))\n",
        "\n",
        "    # Keep only the desired number of zero-flag and non-zero-flag samples\n",
        "    zero_flags_samples = zero_flags_samples[:desired_zero_flags]\n",
        "    filtered_samples = []\n",
        "\n",
        "    for example in non_zero_flags_samples[:desired_non_zero_flags]:\n",
        "        # Check if adding this example exceeds the max percentage for any label\n",
        "        add_sample = True\n",
        "        for key in label_counts:\n",
        "            if example[key]:\n",
        "                if (label_counts[key] + 1) / desired_non_zero_flags > max_percentage:\n",
        "                    add_sample = False\n",
        "                    break\n",
        "\n",
        "        if add_sample:\n",
        "            filtered_samples.append(example)\n",
        "            for key in label_counts:\n",
        "                if example[key]:\n",
        "                    label_counts[key] += 1\n",
        "\n",
        "    # Combine the filtered zero-flag samples with the non-zero-flag samples\n",
        "    filtered_samples += zero_flags_samples\n",
        "\n",
        "    # Shuffle the filtered samples\n",
        "    random.shuffle(filtered_samples)\n",
        "\n",
        "    return filtered_samples\n",
        "\n",
        "\n",
        "# Filter the samples\n",
        "train_samples = filter_dataset(train_samples)\n",
        "validation_samples = filter_dataset(validation_samples)\n",
        "test_samples = filter_dataset(test_samples)\n",
        "\n",
        "# Combine all samples to calculate label percentages\n",
        "all_samples = train_samples + validation_samples + test_samples\n",
        "all_df = pd.DataFrame(all_samples)\n",
        "\n",
        "# Calculate the percentage of samples for each label\n",
        "label_percentages = all_df.drop(columns=[\"text\"]).mean()\n",
        "\n",
        "# Identify labels with less than 1% samples\n",
        "labels_to_remove = label_percentages[label_percentages < 0.01].index.tolist()\n",
        "\n",
        "# Remove the identified labels from all splits\n",
        "train_df = pd.DataFrame(train_samples).drop(columns=labels_to_remove)\n",
        "validation_df = pd.DataFrame(validation_samples).drop(columns=labels_to_remove)\n",
        "test_df = pd.DataFrame(test_samples).drop(columns=labels_to_remove)"
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          "text": [
            "Train set length: 20607\n",
            "Validation set length: 1010\n",
            "Test set length: 1013\n"
          ]
        },
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>toxicity</th>\n",
              "      <th>obscene</th>\n",
              "      <th>threat</th>\n",
              "      <th>insult</th>\n",
              "      <th>identity_attack</th>\n",
              "      <th>sexual_explicit</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Flim flam artist Paul Ryan doing his stuff....</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The current winner-take-all method of awarding...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I guess you'll have to decrease your consumpti...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Raps are toast.  Zero character.  Zero class. ...</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Something is wrong, the wife was shouting he h...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008</th>\n",
              "      <td>\"Justin Trudeau to stand up to Donald Trump\"?\\...</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1009</th>\n",
              "      <td>There's little doubt that Donald Trump is a di...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1010</th>\n",
              "      <td>racism. Gotta undo what the black man built!</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1011</th>\n",
              "      <td>He didn't lash out at Gold Star families. That...</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1012</th>\n",
              "      <td>Sing:  \"Ip you pool around my wife, I will pok...</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "                                                   text  toxicity  obscene  \\\n",
              "0        Flim flam artist Paul Ryan doing his stuff....     False    False   \n",
              "1     The current winner-take-all method of awarding...     False    False   \n",
              "2     I guess you'll have to decrease your consumpti...     False    False   \n",
              "3     Raps are toast.  Zero character.  Zero class. ...      True    False   \n",
              "4     Something is wrong, the wife was shouting he h...     False    False   \n",
              "...                                                 ...       ...      ...   \n",
              "1008  \"Justin Trudeau to stand up to Donald Trump\"?\\...      True    False   \n",
              "1009  There's little doubt that Donald Trump is a di...     False    False   \n",
              "1010       racism. Gotta undo what the black man built!      True    False   \n",
              "1011  He didn't lash out at Gold Star families. That...      True    False   \n",
              "1012  Sing:  \"Ip you pool around my wife, I will pok...      True    False   \n",
              "\n",
              "      threat  insult  identity_attack  sexual_explicit  \n",
              "0      False   False            False            False  \n",
              "1      False   False            False            False  \n",
              "2      False   False            False            False  \n",
              "3      False    True            False            False  \n",
              "4      False   False            False            False  \n",
              "...      ...     ...              ...              ...  \n",
              "1008   False    True            False            False  \n",
              "1009   False   False            False            False  \n",
              "1010   False   False             True            False  \n",
              "1011   False    True            False            False  \n",
              "1012    True   False            False            False  \n",
              "\n",
              "[1013 rows x 7 columns]"
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      "source": [
        "# Print the length of samples for each set\n",
        "print(\"Train set length:\", len(train_df))\n",
        "print(\"Validation set length:\", len(validation_df))\n",
        "print(\"Test set length:\", len(test_df))\n",
        "\n",
        "# Display the test DataFrame to verify\n",
        "test_df"
      ]
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            "text/plain": [
              "<Figure size 3000x1800 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title Displaying distribution\n",
        "import matplotlib.pyplot as plt\n",
        "from collections import defaultdict\n",
        "\n",
        "\n",
        "# Function to count the number of samples flagged for each category\n",
        "def count_flags(samples, labels_to_keep):\n",
        "    no_flags = sum(\n",
        "        all(\n",
        "            value == False\n",
        "            for key, value in sample.items()\n",
        "            if key != \"text\" and key in labels_to_keep\n",
        "        )\n",
        "        for sample in samples\n",
        "    )\n",
        "    counts = {key: sum(sample[key] for sample in samples) for key in labels_to_keep}\n",
        "    return [no_flags] + list(counts.values())\n",
        "\n",
        "\n",
        "# Function to count flagged and non-flagged samples for each label\n",
        "def count_flagged_vs_non_flagged(samples, labels_to_keep):\n",
        "    counts = defaultdict(lambda: {\"flagged\": 0, \"none-flagged\": 0})\n",
        "    for sample in samples:\n",
        "        for key, value in sample.items():\n",
        "            if key != \"text\" and key in labels_to_keep:\n",
        "                if value:\n",
        "                    counts[key][\"flagged\"] += 1\n",
        "                else:\n",
        "                    counts[key][\"none-flagged\"] += 1\n",
        "    return counts\n",
        "\n",
        "\n",
        "# Function to count total flagged and non-flagged samples\n",
        "def count_total_flagged_vs_non_flagged(samples, labels_to_keep):\n",
        "    total_flagged = 0\n",
        "    total_non_flagged = 0\n",
        "    for sample in samples:\n",
        "        flagged = any(\n",
        "            value\n",
        "            for key, value in sample.items()\n",
        "            if key != \"text\" and key in labels_to_keep\n",
        "        )\n",
        "        if flagged:\n",
        "            total_flagged += 1\n",
        "        else:\n",
        "            total_non_flagged += 1\n",
        "    return [total_non_flagged, total_flagged]\n",
        "\n",
        "\n",
        "# Create a list of labels for the pie charts from the samples\n",
        "labels_to_keep = [\n",
        "    key for key in train_samples[0] if key != \"text\" and key not in labels_to_remove\n",
        "]\n",
        "labels = [\"No Flags\"] + labels_to_keep\n",
        "\n",
        "# Count flags for each dataset\n",
        "train_counts = count_flags(train_samples, labels_to_keep)\n",
        "validation_counts = count_flags(validation_samples, labels_to_keep)\n",
        "test_counts = count_flags(test_samples, labels_to_keep)\n",
        "\n",
        "# Count total flagged vs non-flagged for each dataset\n",
        "total_train_counts = count_total_flagged_vs_non_flagged(train_samples, labels_to_keep)\n",
        "total_validation_counts = count_total_flagged_vs_non_flagged(\n",
        "    validation_samples, labels_to_keep\n",
        ")\n",
        "total_test_counts = count_total_flagged_vs_non_flagged(test_samples, labels_to_keep)\n",
        "\n",
        "# Sort the labels and counts for pie charts\n",
        "sorted_train_labels, sorted_train_counts = zip(\n",
        "    *sorted(zip(labels, train_counts), key=lambda x: x[1], reverse=True)\n",
        ")\n",
        "sorted_validation_labels, sorted_validation_counts = zip(\n",
        "    *sorted(zip(labels, validation_counts), key=lambda x: x[1], reverse=True)\n",
        ")\n",
        "sorted_test_labels, sorted_test_counts = zip(\n",
        "    *sorted(zip(labels, test_counts), key=lambda x: x[1], reverse=True)\n",
        ")\n",
        "\n",
        "# Create a single figure with subplots for pie charts and stacked bar plots\n",
        "fig, axes = plt.subplots(3, 3, figsize=(30, 18))\n",
        "\n",
        "# Plot the pie charts for category distribution\n",
        "axes[0, 0].pie(\n",
        "    sorted_train_counts, labels=sorted_train_labels, autopct=\"%1.1f%%\", startangle=140\n",
        ")\n",
        "axes[0, 0].set_title(\"Train Samples (Category Distribution)\")\n",
        "\n",
        "axes[1, 0].pie(\n",
        "    sorted_validation_counts,\n",
        "    labels=sorted_validation_labels,\n",
        "    autopct=\"%1.1f%%\",\n",
        "    startangle=140,\n",
        ")\n",
        "axes[1, 0].set_title(\"Validation Samples (Category Distribution)\")\n",
        "\n",
        "axes[2, 0].pie(\n",
        "    sorted_test_counts, labels=sorted_test_labels, autopct=\"%1.1f%%\", startangle=140\n",
        ")\n",
        "axes[2, 0].set_title(\"Test Samples (Category Distribution)\")\n",
        "\n",
        "# Plot the pie charts for flagged vs non-flagged distribution\n",
        "axes[0, 1].pie(\n",
        "    total_train_counts,\n",
        "    labels=[\"None-Flagged\", \"Flagged\"],\n",
        "    autopct=\"%1.1f%%\",\n",
        "    startangle=140,\n",
        "    colors=[\"blue\", \"red\"],\n",
        ")\n",
        "axes[0, 1].set_title(\"Train Samples (Flagged vs None-Flagged)\")\n",
        "\n",
        "axes[1, 1].pie(\n",
        "    total_validation_counts,\n",
        "    labels=[\"None-Flagged\", \"Flagged\"],\n",
        "    autopct=\"%1.1f%%\",\n",
        "    startangle=140,\n",
        "    colors=[\"blue\", \"red\"],\n",
        ")\n",
        "axes[1, 1].set_title(\"Validation Samples (Flagged vs None-Flagged)\")\n",
        "\n",
        "axes[2, 1].pie(\n",
        "    total_test_counts,\n",
        "    labels=[\"None-Flagged\", \"Flagged\"],\n",
        "    autopct=\"%1.1f%%\",\n",
        "    startangle=140,\n",
        "    colors=[\"blue\", \"red\"],\n",
        ")\n",
        "axes[2, 1].set_title(\"Test Samples (Flagged vs None-Flagged)\")\n",
        "\n",
        "\n",
        "# Create stacked bar plots for flagged vs. non-flagged samples\n",
        "def plot_stacked_bar(ax, samples, title, labels_to_keep):\n",
        "    counts = count_flagged_vs_non_flagged(samples, labels_to_keep)\n",
        "    labels = counts.keys()\n",
        "    flagged_counts = [counts[label][\"flagged\"] for label in labels]\n",
        "    non_flagged_counts = [counts[label][\"none-flagged\"] for label in labels]\n",
        "\n",
        "    # Sort the labels and counts for stacked bar plots by flagged counts\n",
        "    sorted_labels, sorted_non_flagged_counts, sorted_flagged_counts = zip(\n",
        "        *sorted(\n",
        "            zip(labels, non_flagged_counts, flagged_counts),\n",
        "            key=lambda x: x[2],\n",
        "            reverse=True,\n",
        "        )\n",
        "    )\n",
        "\n",
        "    ax.bar(sorted_labels, sorted_non_flagged_counts, label=\"None-Flagged\", color=\"blue\")\n",
        "    ax.bar(\n",
        "        sorted_labels,\n",
        "        sorted_flagged_counts,\n",
        "        bottom=sorted_non_flagged_counts,\n",
        "        label=\"Flagged\",\n",
        "        color=\"red\",\n",
        "    )\n",
        "\n",
        "    ax.set_ylabel(\"Count\")\n",
        "    ax.set_title(title)\n",
        "    ax.legend()\n",
        "    ax.tick_params(axis=\"x\", rotation=45)\n",
        "\n",
        "\n",
        "# Plot stacked bar plots for each dataset\n",
        "plot_stacked_bar(\n",
        "    axes[0, 2], train_samples, \"Train Samples (Stacked Bar)\", labels_to_keep\n",
        ")\n",
        "plot_stacked_bar(\n",
        "    axes[1, 2], validation_samples, \"Validation Samples (Stacked Bar)\", labels_to_keep\n",
        ")\n",
        "plot_stacked_bar(axes[2, 2], test_samples, \"Test Samples (Stacked Bar)\", labels_to_keep)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkHIWSQgrtnT"
      },
      "source": [
        "This will be the subset we will be training with. In an optimal scenario, you may need to do further curation and data preparation to balance the dataset and achieve better results. In this scenario, we have a fairly balanced dataset for `toxicity` and `insult`, but it is much less balanced for the rest of the labels."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HKm6rNHUF_n"
      },
      "source": [
        "## Format Data\n",
        "\n",
        "Now that we have loaded our dataset, we will convert it to the proper desired format to upload for training.\n",
        "\n",
        "The data will be converted to a **JSONL** format as follows:\n",
        "```json\n",
        "{\"text\": \"I believe the Trump administration made a big mistake by its strict definition of family members allowed to visit the U.S. from the six Muslim-majority countries.   It knew or should have known opponents would pounce on a narrow definition and, therefore, should have expanded it a bit to deflect challenges and have a better chance of overcoming inevitable appeals in court.  Moreover, the relatives to be allowed in were not coming as permanent residents; they were coming as visitors for no longer than 6 months with a B1/B2 tourist visas.  Why fall on your sword over that?  Those coming to visit still had to be vetted by consular officials issuing tourist visas.  It would have been easy for the State Department to quietly tighten or enhance the vetting process in the six countries to make it a bit more difficult for people to obtain visas, thus minimizing the number of people coming and make it a non-issue.\", \"labels\": {\"moderation\": [\"safe\"]}}\n",
        "{\"text\": \"Great comment Jake..\", \"labels\": {\"moderation\": [\"safe\"]}}\n",
        "{\"text\": \"Uh huh. Then why don't you behave that way in the ADN comments section? \\nYour alleged \\\"c'est la vie\\\" attitude is nowhere to be found on this website. Where's your vaunted \\\"personal integrity\\\" here?  You sound like Trump at his ridiculous and incredibly embarrassing - for him and his cabinet - \\\"Tell Me How Much You Worship Me\\\" fake cabinet meeting.\", \"labels\": {\"moderation\": [\"toxicity\", \"insult\"]}}\n",
        "{\"text\": \"And given at least a two year probation, complete with random UAs and mandated counseling for liars.\", \"labels\": {\"moderation\": [\"toxicity\", \"insult\"]}}\n",
        "{\"text\": \"You did make an accusation else there wouldn't be an investigation.\", \"labels\": {\"moderation\": [\"safe\"]}}\n",
        "...\n",
        "```\n",
        "With an example of a label being:\n",
        "```json\n",
        "\"labels\": {\n",
        "  \"moderation\": [\n",
        "    \"toxicity\",\n",
        "    \"insult\"\n",
        "  ]\n",
        "}\n",
        "```\n",
        "For multi-label classification, we arbitrarily defined a new label \"safe\" to represent samples that were not flagged."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltHoh_QXJh0n",
        "outputId": "f544c13a-d8ad-4d88-9009-bf8d8181e0c5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 20607/20607 [00:00<00:00, 57140.72it/s]\n",
            "100%|██████████| 1010/1010 [00:00<00:00, 54499.51it/s]\n",
            "100%|██████████| 1013/1013 [00:00<00:00, 51337.93it/s]\n"
          ]
        }
      ],
      "source": [
        "from tqdm import tqdm\n",
        "import json\n",
        "\n",
        "def dataset_to_jsonl(dataset, output_file):\n",
        "    # Extract the possible categories from the dataset columns, excluding the 'text' column\n",
        "    possible_categories = [col for col in dataset.columns if col != \"text\"]\n",
        "\n",
        "    # Open the output file in write mode\n",
        "    with open(output_file, \"w\") as f:\n",
        "        # Iterate over each row in the dataset\n",
        "        for _, row in tqdm(dataset.iterrows(), total=dataset.shape[0]):\n",
        "            # Extract the text and labels from the row\n",
        "            text = row[\"text\"]\n",
        "            labels = [\n",
        "                category\n",
        "                for category in possible_categories\n",
        "                if row[category]\n",
        "            ]\n",
        "            if len(labels) == 0:\n",
        "                labels = [\"safe\"]\n",
        "\n",
        "            # Create the JSON object\n",
        "            json_object = {\"text\": text, \"labels\": {\"moderation\": labels}}\n",
        "\n",
        "            # Write the JSON object to the file as a JSON line\n",
        "            f.write(json.dumps(json_object) + \"\\n\")\n",
        "\n",
        "# Save files\n",
        "dataset_to_jsonl(train_df, \"training_file.jsonl\")\n",
        "dataset_to_jsonl(validation_df, \"validation_file.jsonl\")\n",
        "dataset_to_jsonl(test_df, \"test_file.jsonl\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "82fzZ0kJV0VS"
      },
      "source": [
        "The data was converted and saved properly. We can now train our model.\n",
        "\n",
        "## Training\n",
        "There are two methods to train the model: either upload and train via [la platforme](https://console.mistral.ai/build/finetuned-models) or via the [API](https://classifier-factory.platform-docs-9m1.pages.dev/capabilities/finetuning/classifier_factory/).\n",
        "\n",
        "First, we need to install `mistralai`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Td3wp01pWJkC"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install mistralai"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPpoJiSq5LUj"
      },
      "source": [
        "And setup our client, you can create an API key [here](https://console.mistral.ai/api-keys/)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HLDxqIh_WRAu"
      },
      "outputs": [],
      "source": [
        "from mistralai import Mistral\n",
        "import os\n",
        "\n",
        "# Set the API key for Mistral\n",
        "api_key = \"API_KEY\"\n",
        "# Set your Weights and Biases key\n",
        "# wandb_key = \"WANDB_KEY\"\n",
        "\n",
        "# Initialize the Mistral client\n",
        "client = Mistral(api_key=api_key)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7opKZ1q5PgR"
      },
      "source": [
        "We will upload 2 files, the training set and the validation set ( optional ) that will be used for validation loss."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C2XLncvEWWSc"
      },
      "outputs": [],
      "source": [
        "# Upload the training data\n",
        "training_data = client.files.upload(\n",
        "    file={\n",
        "        \"file_name\": \"training_file.jsonl\",\n",
        "        \"content\": open(\"training_file.jsonl\", \"rb\"),\n",
        "    }\n",
        ")\n",
        "\n",
        "# Upload the validation data\n",
        "validation_data = client.files.upload(\n",
        "    file={\n",
        "        \"file_name\": \"validation_file.jsonl\",\n",
        "        \"content\": open(\"validation_file.jsonl\", \"rb\"),\n",
        "    }\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8w2XmKOWewV"
      },
      "source": [
        "With the data uploaded, we can create a job."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kXtqDouNd_40",
        "outputId": "9ec1e034-07cf-4fe0-bdb7-77ba400abb55"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "    \"id\": \"cbe076ac-40a6-4fa4-9162-281531405d20\",\n",
            "    \"auto_start\": false,\n",
            "    \"model\": \"ministral-3b-latest\",\n",
            "    \"status\": \"QUEUED\",\n",
            "    \"created_at\": 1744806685,\n",
            "    \"modified_at\": 1744806685,\n",
            "    \"training_files\": [\n",
            "        \"d0a8d6ac-2d9c-4d43-af04-e40b244fbdb3\"\n",
            "    ],\n",
            "    \"hyperparameters\": {\n",
            "        \"training_steps\": 200,\n",
            "        \"learning_rate\": 5e-05,\n",
            "        \"weight_decay\": 0.1,\n",
            "        \"warmup_fraction\": 0.05,\n",
            "        \"epochs\": null,\n",
            "        \"seq_len\": 16384\n",
            "    },\n",
            "    \"validation_files\": [\n",
            "        \"ea6c9ca7-470b-4953-92ab-4f216a967852\"\n",
            "    ],\n",
            "    \"fine_tuned_model\": null,\n",
            "    \"suffix\": null,\n",
            "    \"integrations\": [\n",
            "        {\n",
            "            \"project\": \"moderation-classifier\",\n",
            "            \"name\": null,\n",
            "            \"run_name\": null,\n",
            "            \"url\": null\n",
            "        }\n",
            "    ],\n",
            "    \"trained_tokens\": null,\n",
            "    \"metadata\": {\n",
            "        \"expected_duration_seconds\": null,\n",
            "        \"cost\": 0.0,\n",
            "        \"cost_currency\": null,\n",
            "        \"train_tokens_per_step\": null,\n",
            "        \"train_tokens\": null,\n",
            "        \"data_tokens\": null,\n",
            "        \"estimated_start_time\": null\n",
            "    }\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "# Create a fine-tuning job\n",
        "created_job = client.fine_tuning.jobs.create(\n",
        "    model=\"ministral-3b-latest\",\n",
        "    job_type=\"classifier\",\n",
        "    training_files=[{\"file_id\": training_data.id, \"weight\": 1}],\n",
        "    validation_files=[validation_data.id],\n",
        "    hyperparameters={\"training_steps\": 200, \"learning_rate\": 0.00005},\n",
        "    auto_start=False,\n",
        "#   integrations=[\n",
        "#       {\n",
        "#           \"project\": \"moderation-classifier\",\n",
        "#           \"api_key\": wandb_key,\n",
        "#       }\n",
        "#   ]\n",
        ")\n",
        "print(json.dumps(created_job.model_dump(), indent=4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d_xl2Ngc5XKP"
      },
      "source": [
        "Once the job is created, we can review details such as the number of epochs and other relevant information. This allows us to make informed decisions before initiating the job.\n",
        "\n",
        "We'll retrieve the job and wait for it to complete the validation process before starting. This validation step ensures the job is ready to begin."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lsqS0Ym85YBl",
        "outputId": "17938986-ea3a-4bc7-f271-529e0d68ebf6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "    \"id\": \"cbe076ac-40a6-4fa4-9162-281531405d20\",\n",
            "    \"auto_start\": false,\n",
            "    \"model\": \"ministral-3b-latest\",\n",
            "    \"status\": \"VALIDATED\",\n",
            "    \"created_at\": 1744806685,\n",
            "    \"modified_at\": 1744806687,\n",
            "    \"training_files\": [\n",
            "        \"d0a8d6ac-2d9c-4d43-af04-e40b244fbdb3\"\n",
            "    ],\n",
            "    \"hyperparameters\": {\n",
            "        \"training_steps\": 200,\n",
            "        \"learning_rate\": 5e-05,\n",
            "        \"weight_decay\": 0.1,\n",
            "        \"warmup_fraction\": 0.05,\n",
            "        \"epochs\": 7.29886929671425,\n",
            "        \"seq_len\": 16384\n",
            "    },\n",
            "    \"classifier_targets\": [\n",
            "        {\n",
            "            \"name\": \"moderation\",\n",
            "            \"labels\": [\n",
            "                \"toxicity\",\n",
            "                \"safe\",\n",
            "                \"identity_attack\",\n",
            "                \"sexual_explicit\",\n",
            "                \"obscene\",\n",
            "                \"insult\",\n",
            "                \"threat\"\n",
            "            ]\n",
            "        }\n",
            "    ],\n",
            "    \"validation_files\": [\n",
            "        \"ea6c9ca7-470b-4953-92ab-4f216a967852\"\n",
            "    ],\n",
            "    \"fine_tuned_model\": null,\n",
            "    \"suffix\": null,\n",
            "    \"integrations\": [\n",
            "        {\n",
            "            \"project\": \"moderation-classifier\",\n",
            "            \"name\": null,\n",
            "            \"run_name\": null,\n",
            "            \"url\": null\n",
            "        }\n",
            "    ],\n",
            "    \"trained_tokens\": null,\n",
            "    \"metadata\": {\n",
            "        \"expected_duration_seconds\": 3400,\n",
            "        \"cost\": 6.56,\n",
            "        \"cost_currency\": \"EUR\",\n",
            "        \"train_tokens_per_step\": 65536,\n",
            "        \"train_tokens\": 13107200,\n",
            "        \"data_tokens\": 1795785,\n",
            "        \"estimated_start_time\": null\n",
            "    },\n",
            "    \"events\": [\n",
            "        {\n",
            "            \"name\": \"status-updated\",\n",
            "            \"created_at\": 1744806685,\n",
            "            \"data\": {\n",
            "                \"status\": \"QUEUED\"\n",
            "            }\n",
            "        },\n",
            "        {\n",
            "            \"name\": \"status-updated\",\n",
            "            \"created_at\": 1744806685,\n",
            "            \"data\": {\n",
            "                \"status\": \"VALIDATING\"\n",
            "            }\n",
            "        },\n",
            "        {\n",
            "            \"name\": \"status-updated\",\n",
            "            \"created_at\": 1744806687,\n",
            "            \"data\": {\n",
            "                \"status\": \"VALIDATED\"\n",
            "            }\n",
            "        }\n",
            "    ],\n",
            "    \"checkpoints\": []\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "# Retrieve the job details\n",
        "retrieved_job = client.fine_tuning.jobs.get(job_id=created_job.id)\n",
        "print(json.dumps(retrieved_job.model_dump(), indent=4))\n",
        "\n",
        "import time\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# Wait for the job to be validated\n",
        "while retrieved_job.status not in [\"VALIDATED\"]:\n",
        "    retrieved_job = client.fine_tuning.jobs.get(job_id=created_job.id)\n",
        "\n",
        "    clear_output(wait=True)  # Clear the previous output (User Friendly)\n",
        "    print(json.dumps(retrieved_job.model_dump(), indent=4))\n",
        "    time.sleep(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNcRBIyisgMV"
      },
      "source": [
        "We can now run the job."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dsXORPwcrtnW",
        "outputId": "db139656-9374-4fc7-aa65-6ad46234d940"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "    \"id\": \"cbe076ac-40a6-4fa4-9162-281531405d20\",\n",
            "    \"auto_start\": false,\n",
            "    \"model\": \"ministral-3b-latest\",\n",
            "    \"status\": \"QUEUED\",\n",
            "    \"created_at\": 1744806685,\n",
            "    \"modified_at\": 1744806690,\n",
            "    \"training_files\": [\n",
            "        \"d0a8d6ac-2d9c-4d43-af04-e40b244fbdb3\"\n",
            "    ],\n",
            "    \"hyperparameters\": {\n",
            "        \"training_steps\": 200,\n",
            "        \"learning_rate\": 5e-05,\n",
            "        \"weight_decay\": 0.1,\n",
            "        \"warmup_fraction\": 0.05,\n",
            "        \"epochs\": 7.29886929671425,\n",
            "        \"seq_len\": 16384\n",
            "    },\n",
            "    \"classifier_targets\": [\n",
            "        {\n",
            "            \"name\": \"moderation\",\n",
            "            \"labels\": [\n",
            "                \"toxicity\",\n",
            "                \"safe\",\n",
            "                \"identity_attack\",\n",
            "                \"sexual_explicit\",\n",
            "                \"obscene\",\n",
            "                \"insult\",\n",
            "                \"threat\"\n",
            "            ]\n",
            "        }\n",
            "    ],\n",
            "    \"validation_files\": [\n",
            "        \"ea6c9ca7-470b-4953-92ab-4f216a967852\"\n",
            "    ],\n",
            "    \"fine_tuned_model\": null,\n",
            "    \"suffix\": null,\n",
            "    \"integrations\": [\n",
            "        {\n",
            "            \"project\": \"moderation-classifier\",\n",
            "            \"name\": null,\n",
            "            \"run_name\": null,\n",
            "            \"url\": null\n",
            "        }\n",
            "    ],\n",
            "    \"trained_tokens\": null,\n",
            "    \"metadata\": {\n",
            "        \"expected_duration_seconds\": 3400,\n",
            "        \"cost\": 6.56,\n",
            "        \"cost_currency\": \"EUR\",\n",
            "        \"train_tokens_per_step\": 65536,\n",
            "        \"train_tokens\": 13107200,\n",
            "        \"data_tokens\": 1795785,\n",
            "        \"estimated_start_time\": 1744806861\n",
            "    },\n",
            "    \"events\": [\n",
            "        {\n",
            "            \"name\": \"status-updated\",\n",
            "            \"created_at\": 1744806685,\n",
            "            \"data\": {\n",
            "                \"status\": \"QUEUED\"\n",
            "            }\n",
            "        },\n",
            "        {\n",
            "            \"name\": \"status-updated\",\n",
            "            \"created_at\": 1744806685,\n",
            "            \"data\": {\n",
            "                \"status\": \"VALIDATING\"\n",
            "            }\n",
            "        },\n",
            "        {\n",
            "            \"name\": \"status-updated\",\n",
            "            \"created_at\": 1744806687,\n",
            "            \"data\": {\n",
            "                \"status\": \"VALIDATED\"\n",
            "            }\n",
            "        }\n",
            "    ],\n",
            "    \"checkpoints\": []\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "# Start the fine-tuning job\n",
        "client.fine_tuning.jobs.start(job_id=created_job.id)\n",
        "\n",
        "# Retrieve the job details again\n",
        "retrieved_job = client.fine_tuning.jobs.get(job_id=created_job.id)\n",
        "print(json.dumps(retrieved_job.model_dump(), indent=4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5SyxkdW5cfJ"
      },
      "source": [
        "The job is now starting. Let's keep track of the status and plot the loss.\n",
        "\n",
        "For that, we highly recommend making use of our Weights and Biases integration, but we will also keep track of it directly in this notebook.\n",
        "\n",
        "### WANDB\n",
        "\n",
        "**Training:**\n",
        "\n",
        "![moderation-train-loss.png](https://raw.githubusercontent.com/mistralai/cookbook/refs/heads/main/mistral/classifier_factory/moderation-train-loss.png)\n",
        "\n",
        "**Eval/Validation:**\n",
        "\n",
        "![moderation-validation-loss.png](https://raw.githubusercontent.com/mistralai/cookbook/refs/heads/main/mistral/classifier_factory/moderation-validation-loss.png)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r3A2bFJo5d9y",
        "outputId": "dccf0185-61ce-41fb-cb99-875c3e87e846"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SUCCESS\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# Initialize DataFrames to store the metrics\n",
        "train_metrics_df = pd.DataFrame(columns=[\"Step Number\", \"Train Loss\"])\n",
        "valid_metrics_df = pd.DataFrame(columns=[\"Step Number\", \"Valid Loss\"])\n",
        "\n",
        "# Total training steps\n",
        "total_training_steps = retrieved_job.hyperparameters.training_steps\n",
        "\n",
        "# Wait for the job to complete\n",
        "while retrieved_job.status in [\"QUEUED\", \"RUNNING\"]:\n",
        "    retrieved_job = client.fine_tuning.jobs.get(job_id=created_job.id)\n",
        "\n",
        "    if retrieved_job.status == \"QUEUED\":\n",
        "        time.sleep(5)\n",
        "        continue\n",
        "\n",
        "    # Clear the previous output (User Friendly)\n",
        "    clear_output(wait=True)\n",
        "    print(retrieved_job.status)\n",
        "\n",
        "    # Extract metrics from all checkpoints\n",
        "    for checkpoint in retrieved_job.checkpoints[::-1]:\n",
        "        metrics = checkpoint.metrics\n",
        "        step_number = checkpoint.step_number\n",
        "\n",
        "        # Check if the step number is already in the DataFrame\n",
        "        if (\n",
        "            step_number\n",
        "            not in train_metrics_df[\"Step Number\"]\n",
        "        ):\n",
        "            # Prepare the new row for train loss\n",
        "            train_row = {\n",
        "                \"Step Number\": step_number,\n",
        "                \"Train Loss\": metrics.train_loss,\n",
        "            }\n",
        "\n",
        "            # Append the new train metrics to the DataFrame\n",
        "            train_metrics_df = pd.concat(\n",
        "                [train_metrics_df, pd.DataFrame([train_row])], ignore_index=True\n",
        "            )\n",
        "\n",
        "            # Prepare the new row for valid loss if available\n",
        "            if metrics.valid_loss != 0:\n",
        "                valid_row = {\n",
        "                    \"Step Number\": step_number,\n",
        "                    \"Valid Loss\": metrics.valid_loss,\n",
        "                }\n",
        "                # Append the new valid metrics to the DataFrame\n",
        "                valid_metrics_df = pd.concat(\n",
        "                    [valid_metrics_df, pd.DataFrame([valid_row])], ignore_index=True\n",
        "                )\n",
        "\n",
        "    if len(retrieved_job.checkpoints) > 0:\n",
        "        # Sort the DataFrames by step number\n",
        "        train_metrics_df = train_metrics_df.sort_values(by=\"Step Number\")\n",
        "        valid_metrics_df = valid_metrics_df.sort_values(by=\"Step Number\")\n",
        "\n",
        "        # Plot the evolution of train loss and valid loss\n",
        "        plt.figure(figsize=(10, 6))\n",
        "\n",
        "        # Plot train loss\n",
        "        plt.plot(\n",
        "            train_metrics_df[\"Step Number\"],\n",
        "            train_metrics_df[\"Train Loss\"],\n",
        "            label=\"Train Loss\",\n",
        "            linestyle=\"-\",\n",
        "        )\n",
        "\n",
        "        # Plot valid loss only if available\n",
        "        if not valid_metrics_df.empty:\n",
        "            plt.plot(\n",
        "                valid_metrics_df[\"Step Number\"],\n",
        "                valid_metrics_df[\"Valid Loss\"],\n",
        "                label=\"Valid Loss\",\n",
        "                linestyle=\"--\",\n",
        "            )\n",
        "\n",
        "        plt.xlabel(\"Step Number\")\n",
        "        plt.ylabel(\"Loss\")\n",
        "        plt.title(\"Train Loss and Valid Loss\")\n",
        "        plt.legend()\n",
        "        plt.grid(True)\n",
        "        plt.show()\n",
        "\n",
        "    time.sleep(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NBsK2MJX5gLA"
      },
      "source": [
        "### Inference\n",
        "Our model is trained and ready for use! Let's test it on a sample from our test set!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GpPELUtT5fe-",
        "outputId": "584ca330-e690-4e70-9232-62944ac91ce2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Text: Flim flam artist Paul Ryan doing his stuff....\n",
            "Classifier Response: {\n",
            "    \"id\": \"9c3a76bc84204dcdadaf11e291365a3b\",\n",
            "    \"model\": \"ft:classifier:ministral-3b-latest:8e2706f0:20250416:cbe076ac\",\n",
            "    \"results\": [\n",
            "        {\n",
            "            \"moderation\": {\n",
            "                \"scores\": {\n",
            "                    \"toxicity\": 0.11157931387424469,\n",
            "                    \"safe\": 0.7990996837615967,\n",
            "                    \"identity_attack\": 0.0010769261280074716,\n",
            "                    \"sexual_explicit\": 0.0030203138012439013,\n",
            "                    \"obscene\": 0.008739566430449486,\n",
            "                    \"insult\": 0.07260604947805405,\n",
            "                    \"threat\": 0.0038781599141657352\n",
            "                }\n",
            "            }\n",
            "        }\n",
            "    ]\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "# Load the test samples\n",
        "with open(\"test_file.jsonl\", \"r\") as f:\n",
        "    test_samples = [json.loads(l) for l in f.readlines()]\n",
        "\n",
        "# Classify the first test sample\n",
        "classifier_response = client.classifiers.classify(\n",
        "    model=retrieved_job.fine_tuned_model,\n",
        "    inputs=[test_samples[0][\"text\"]],\n",
        ")\n",
        "print(\"Text:\", test_samples[0][\"text\"])\n",
        "print(\"Classifier Response:\", json.dumps(classifier_response.model_dump(), indent=4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TdPKXyg5rdn"
      },
      "source": [
        "For a more in-depth guide on multi-target, with an evaluation comparison between LLMs and our classifier API, visit this [cookbook](https://colab.research.google.com/github/mistralai/cookbook/blob/main/mistral/classifier_factory/product_classification.ipynb)."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}