diff --git "a/02_transformers_pipeline.ipynb" "b/02_transformers_pipeline.ipynb"
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+++ "b/02_transformers_pipeline.ipynb"
@@ -0,0 +1,9612 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xkIovkf6CJsj"
+   },
+   "source": [
+    "# Notebook 2: Applying a Transformers pipeline to GitHub issues\n",
+    "\n",
+    "**Dataset:** `BeNatural/github-scikit-learn-issues-dataset` (300 scikit-learn issues from GitHub, published on the Hugging Face Hub)\n",
+    "\n",
+    "**What this notebook does**\n",
+    "1. Loads the dataset from the Hugging Face Hub and checks its structure.\n",
+    "2. Shows what happens *behind the pipeline* (tokenisation, model, post-processing).\n",
+    "3. Uses a pipeline to classify each issue title as a bug report, feature request or documentation issue.\n",
+    "4. Compares the predictions with the real GitHub labels and presents the results in tables and charts.\n",
+    "5. Discusses the limitations of the results."
+   ],
+   "id": "xkIovkf6CJsj"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "48SXe-_xCJsl"
+   },
+   "source": [
+    "## 1. Setup\n",
+    "This cell installs the libraries. It can take a few minutes the first time. If you are on your own computer and they are already installed, it will finish quickly."
+   ],
+   "id": "48SXe-_xCJsl"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "z72uGbzmCJsl"
+   },
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "!pip install -q datasets transformers torch pandas matplotlib"
+   ],
+   "id": "z72uGbzmCJsl"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "1APE3PJiCJsm"
+   },
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# Folder where this notebook saves its outputs\n",
+    "os.makedirs(\"outputs\", exist_ok=True)"
+   ],
+   "id": "1APE3PJiCJsm"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "icm3TJXsCJsm"
+   },
+   "source": [
+    "## 2. Load the dataset from Hugging Face\n",
+    "The dataset is public, so no password or token is needed. The notebook contains no secret keys."
+   ],
+   "id": "icm3TJXsCJsm"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
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+    "outputId": "119ea799-3944-432a-f175-56a5350ed23f"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+      "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "README.md:   0%|          | 0.00/8.64k [00:00<?, ?B/s]"
+      ],
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+    },
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+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "github_issues.parquet: reconstructing file:   0%|          |  0.00B /  135kB            "
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "2f04d574a74442108ab88b7fcf50dd88"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "github_issues.parquet: downloading bytes:           |  0.00B            "
+      ],
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+       "model_id": "bceb9422be834da8a3e8cea3923f81cb"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "f6acc5d6f52c4be18847919c20bbacc9"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(300, 14)\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   issue_id  issue_number                 repository  \\\n",
+       "0    295764             1  scikit-learn/scikit-learn   \n",
+       "1    311965             5  scikit-learn/scikit-learn   \n",
+       "2    369933             7  scikit-learn/scikit-learn   \n",
+       "3    378489             8  scikit-learn/scikit-learn   \n",
+       "4    382343            11  scikit-learn/scikit-learn   \n",
+       "\n",
+       "                                             title  \\\n",
+       "0                 remove Y from fit in OneClassSVM   \n",
+       "1                              Bayesian regression   \n",
+       "2    Return index of support vectors in sparse SVM   \n",
+       "3                               LARS module broken   \n",
+       "4  implement warm restarts for logistic regression   \n",
+       "\n",
+       "                                                body   state  \\\n",
+       "0                                                     closed   \n",
+       "1  Add priors on the mean instead of assuming tha...  closed   \n",
+       "2                                     Assigned to me  closed   \n",
+       "3  - the parameter names are different in docstri...  closed   \n",
+       "4                                     assigned to me  closed   \n",
+       "\n",
+       "                  labels                created_at                 closed_at  \\\n",
+       "0                        2010-08-31 07:38:16+00:00 2010-08-31 07:43:11+00:00   \n",
+       "1                        2010-09-11 11:40:28+00:00 2012-01-06 13:37:31+00:00   \n",
+       "2  Enhancement, Moderate 2010-10-19 08:00:57+00:00 2013-02-02 16:08:16+00:00   \n",
+       "3                        2010-10-23 23:04:24+00:00 2010-11-25 11:09:11+00:00   \n",
+       "4            New Feature 2010-10-26 07:28:50+00:00 2014-10-12 15:30:46+00:00   \n",
+       "\n",
+       "   comments   author                                           html_url  \\\n",
+       "0         0  fabianp  https://github.com/scikit-learn/scikit-learn/i...   \n",
+       "1        11  vmichel  https://github.com/scikit-learn/scikit-learn/i...   \n",
+       "2         2  fabianp  https://github.com/scikit-learn/scikit-learn/i...   \n",
+       "3         5    satra  https://github.com/scikit-learn/scikit-learn/i...   \n",
+       "4        12  fabianp  https://github.com/scikit-learn/scikit-learn/i...   \n",
+       "\n",
+       "   text_length  time_to_close_hours  \n",
+       "0           33             0.081944  \n",
+       "1          443         11569.950833  \n",
+       "2           60         20096.121944  \n",
+       "3          349           780.079722  \n",
+       "4           62         34736.032222  "
+      ],
+      "text/html": [
+       "\n",
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+       "    <div>\n",
+       "<style scoped>\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>issue_id</th>\n",
+       "      <th>issue_number</th>\n",
+       "      <th>repository</th>\n",
+       "      <th>title</th>\n",
+       "      <th>body</th>\n",
+       "      <th>state</th>\n",
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+       "      <th>text_length</th>\n",
+       "      <th>time_to_close_hours</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>295764</td>\n",
+       "      <td>1</td>\n",
+       "      <td>scikit-learn/scikit-learn</td>\n",
+       "      <td>remove Y from fit in OneClassSVM</td>\n",
+       "      <td></td>\n",
+       "      <td>closed</td>\n",
+       "      <td></td>\n",
+       "      <td>2010-08-31 07:38:16+00:00</td>\n",
+       "      <td>2010-08-31 07:43:11+00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>fabianp</td>\n",
+       "      <td>https://github.com/scikit-learn/scikit-learn/i...</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0.081944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>311965</td>\n",
+       "      <td>5</td>\n",
+       "      <td>scikit-learn/scikit-learn</td>\n",
+       "      <td>Bayesian regression</td>\n",
+       "      <td>Add priors on the mean instead of assuming tha...</td>\n",
+       "      <td>closed</td>\n",
+       "      <td></td>\n",
+       "      <td>2010-09-11 11:40:28+00:00</td>\n",
+       "      <td>2012-01-06 13:37:31+00:00</td>\n",
+       "      <td>11</td>\n",
+       "      <td>vmichel</td>\n",
+       "      <td>https://github.com/scikit-learn/scikit-learn/i...</td>\n",
+       "      <td>443</td>\n",
+       "      <td>11569.950833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>369933</td>\n",
+       "      <td>7</td>\n",
+       "      <td>scikit-learn/scikit-learn</td>\n",
+       "      <td>Return index of support vectors in sparse SVM</td>\n",
+       "      <td>Assigned to me</td>\n",
+       "      <td>closed</td>\n",
+       "      <td>Enhancement, Moderate</td>\n",
+       "      <td>2010-10-19 08:00:57+00:00</td>\n",
+       "      <td>2013-02-02 16:08:16+00:00</td>\n",
+       "      <td>2</td>\n",
+       "      <td>fabianp</td>\n",
+       "      <td>https://github.com/scikit-learn/scikit-learn/i...</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20096.121944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>378489</td>\n",
+       "      <td>8</td>\n",
+       "      <td>scikit-learn/scikit-learn</td>\n",
+       "      <td>LARS module broken</td>\n",
+       "      <td>- the parameter names are different in docstri...</td>\n",
+       "      <td>closed</td>\n",
+       "      <td></td>\n",
+       "      <td>2010-10-23 23:04:24+00:00</td>\n",
+       "      <td>2010-11-25 11:09:11+00:00</td>\n",
+       "      <td>5</td>\n",
+       "      <td>satra</td>\n",
+       "      <td>https://github.com/scikit-learn/scikit-learn/i...</td>\n",
+       "      <td>349</td>\n",
+       "      <td>780.079722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>382343</td>\n",
+       "      <td>11</td>\n",
+       "      <td>scikit-learn/scikit-learn</td>\n",
+       "      <td>implement warm restarts for logistic regression</td>\n",
+       "      <td>assigned to me</td>\n",
+       "      <td>closed</td>\n",
+       "      <td>New Feature</td>\n",
+       "      <td>2010-10-26 07:28:50+00:00</td>\n",
+       "      <td>2014-10-12 15:30:46+00:00</td>\n",
+       "      <td>12</td>\n",
+       "      <td>fabianp</td>\n",
+       "      <td>https://github.com/scikit-learn/scikit-learn/i...</td>\n",
+       "      <td>62</td>\n",
+       "      <td>34736.032222</td>\n",
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+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
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+       "type": "dataframe",
+       "variable_name": "df",
+       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 300,\n  \"fields\": [\n    {\n      \"column\": \"issue_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1156320,\n        \"min\": 295764,\n        \"max\": 4103370,\n        \"num_unique_values\": 300,\n        \"samples\": [\n          2870227,\n          3681083,\n          1922616\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"issue_number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 231,\n        \"min\": 1,\n        \"max\": 768,\n        \"num_unique_values\": 300,\n        \"samples\": [\n          565,\n          703,\n          395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"repository\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"scikit-learn/scikit-learn\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 300,\n        \"samples\": [\n          \"grid search keeps all estimators in memory\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 291,\n        \"samples\": [\n          \"Just a note to remember that we can arguably still greatly improve the Coordinate Descent  (lasso / elastic net)  convergence speed if we implement the following trick:\\n\\nhttp://www-stat.stanford.edu/~tibs/ftp/strong.pdf\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"open\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"labels\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"created_at\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2010-08-31 07:38:16+00:00\",\n        \"max\": \"2012-04-13 13:40:27+00:00\",\n        \"num_unique_values\": 300,\n        \"samples\": [\n          \"2012-01-17 15:43:10+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"closed_at\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2010-08-31 07:43:11+00:00\",\n        \"max\": \"2025-11-05 16:50:43+00:00\",\n        \"num_unique_values\": 295,\n        \"samples\": [\n          \"2012-03-25 22:18:29+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"comments\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 0,\n        \"max\": 59,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"author\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 84,\n        \"samples\": [\n          \"benjaminwilson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"html_url\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 300,\n        \"samples\": [\n          \"https://github.com/scikit-learn/scikit-learn/issues/565\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 958,\n        \"min\": 33,\n        \"max\": 6922,\n        \"num_unique_values\": 264,\n        \"samples\": [\n          325\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_to_close_hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18537.589444119472,\n        \"min\": 0.08194444444444444,\n        \"max\": 125742.1775,\n        \"num_unique_values\": 295,\n        \"samples\": [\n          1.9425\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "ds = load_dataset(\n",
+    "    \"BeNatural/github-scikit-learn-issues-dataset\",\n",
+    "    data_files=\"github_issues.parquet\",\n",
+    "    split=\"train\",\n",
+    ")\n",
+    "df = ds.to_pandas()\n",
+    "print(df.shape)       # expected: (300, 14)\n",
+    "df.head()"
+   ],
+   "id": "BPF7SNlkCJsn"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DUr-XYmoCJsn"
+   },
+   "source": [
+    "### Basic structure and missing values"
+   ],
+   "id": "DUr-XYmoCJsn"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cy1-nsqJCJsn",
+    "outputId": "d8edb4da-6668-46c0-ba3e-36e1ae44f35c"
+   },
+   "execution_count": 4,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "issue_id                             int64\n",
+      "issue_number                         int64\n",
+      "repository                          object\n",
+      "title                               object\n",
+      "body                                object\n",
+      "state                               object\n",
+      "labels                              object\n",
+      "created_at             datetime64[ns, UTC]\n",
+      "closed_at              datetime64[ns, UTC]\n",
+      "comments                             int64\n",
+      "author                              object\n",
+      "html_url                            object\n",
+      "text_length                          int64\n",
+      "time_to_close_hours                float64\n",
+      "dtype: object\n",
+      "\n",
+      "Missing values per column:\n",
+      "issue_id               0\n",
+      "issue_number           0\n",
+      "repository             0\n",
+      "title                  0\n",
+      "body                   0\n",
+      "state                  0\n",
+      "labels                 0\n",
+      "created_at             0\n",
+      "closed_at              5\n",
+      "comments               0\n",
+      "author                 0\n",
+      "html_url               0\n",
+      "text_length            0\n",
+      "time_to_close_hours    5\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.dtypes)\n",
+    "print()\n",
+    "print(\"Missing values per column:\")\n",
+    "print(df.isna().sum())"
+   ],
+   "id": "cy1-nsqJCJsn"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "CCi8Z9CGCJsn",
+    "outputId": "017621be-ac9f-4d09-95e5-5191a8939b42"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "State of issues:\n",
+      "state\n",
+      "closed    295\n",
+      "open        5\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Date range: 2010-08-31 07:38:16+00:00 to 2012-04-13 13:40:27+00:00\n",
+      "\n",
+      "Most common labels:\n",
+      "labels\n",
+      "                                    235\n",
+      "New Feature                          21\n",
+      "Enhancement                          11\n",
+      "Bug                                   7\n",
+      "Enhancement, Moderate                 5\n",
+      "Easy, Enhancement                     5\n",
+      "Documentation                         3\n",
+      "New Feature, module:linear_model      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"State of issues:\")\n",
+    "print(df[\"state\"].value_counts())\n",
+    "print()\n",
+    "print(\"Date range:\", df[\"created_at\"].min(), \"to\", df[\"created_at\"].max())\n",
+    "print()\n",
+    "print(\"Most common labels:\")\n",
+    "print(df[\"labels\"].value_counts().head(8))"
+   ],
+   "id": "CCi8Z9CGCJsn"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "E3F2ZCWQCJsn"
+   },
+   "source": [
+    "**What this tells us:** the dataset has 300 rows and 14 columns. Some issues have an empty body, and most issues have no label at all. These are the only labelled issues we can use to check the model later. The issues are all from 2010 to 2012, so they are not a sample of recent scikit-learn issues."
+   ],
+   "id": "E3F2ZCWQCJsn"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eUeqXOgPCJso"
+   },
+   "source": [
+    "## 3. Behind the pipeline\n",
+    "A Transformers pipeline hides three steps:\n",
+    "1. **Tokenisation:** the text is split into tokens and converted to numbers.\n",
+    "2. **Model:** the numbers go through a neural network, which produces raw scores called *logits*.\n",
+    "3. **Post-processing:** the logits are turned into probabilities and the highest one becomes the label.\n",
+    "\n",
+    "Here are those three steps done by hand on one issue title, using a sentiment model."
+   ],
+   "id": "eUeqXOgPCJso"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 226,
+     "referenced_widgets": [
+      "6d0e86994f174ea8a441881909bfb702",
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+      "e91bec106c2e4b388390cc4fd4d56446",
+      "fbc5048410854aaba5034a5854877eb5",
+      "3516515146464a298c33eb8c76031846",
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+      "d1fda3add0634c0bb0df6722a7d41c68",
+      "c4e838069589460493ec0c16a4f0d3c1",
+      "e5a11117603a42c984e3870718487417",
+      "223c2901e94f4fb2938a554388735cb5",
+      "634d1d7c145d4194b1feb5eb95a0f9a8",
+      "01b9ba0326624670b80e16bba45a507a",
+      "52aa665f6611453c9f5d1d57cb561a96",
+      "31f9255678b8415099f88cfbf8bc8ac3",
+      "8b32f9d702414133b1b075ab8262dd66",
+      "11ada76fe5234aa28a2ce9e5c6d7994b",
+      "ff2463960d2c48c79e640fe7959b1f48",
+      "635183b53afd4c6a801e0633cbc41e36",
+      "272f613b17164f829b5ff4da7d7b153a",
+      "5996717b6573494d8c9f7643bacc2b2c",
+      "f6d371d707ca4e2ca40129adc9ea01da",
+      "4fadc483690f433b85fffb6c04d86e2e",
+      "1195cd156da64e8f9d5dbb1ccbe8fadf",
+      "b92b6a6267364df282a55b95dab2c189",
+      "3b0b3a71f6a6441bba93d90ca4dccec6",
+      "56c8652b2b3a487d94f1165f7098b38e",
+      "54f26b27db594fbaa92a4e2552f63981",
+      "2530c9ce195d417b982a9e445cb7f73f",
+      "2e7689f67fab4cc29871a531e40cfc84"
+     ]
+    },
+    "id": "LPN1BO6oCJso",
+    "outputId": "5ed0e1ad-9895-48f7-e916-c4996ee2ad84"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "6d0e86994f174ea8a441881909bfb702"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "b83fb2de9e7640698139fc9493a72093"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "bd60148731bc49a5ae395c4fbbae7527"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "model.safetensors: reconstructing file:   0%|          |  0.00B /  268MB            "
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "3516515146464a298c33eb8c76031846"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "model.safetensors: downloading bytes:           |  0.00B            "
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "c4e838069589460493ec0c16a4f0d3c1"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Loading weights:   0%|          | 0/104 [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "272f613b17164f829b5ff4da7d7b153a"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Issue title: Bayesian regression\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+    "\n",
+    "model_name = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
+    "\n",
+    "text = df[\"title\"].iloc[1]\n",
+    "print(\"Issue title:\", text)"
+   ],
+   "id": "LPN1BO6oCJso"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "myIlZ7w4CJso",
+    "outputId": "e01c2a51-9714-42a5-8b9d-18174e9ff45d"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Token ids: [101, 3016, 25253, 26237, 102]\n",
+      "Tokens:    ['[CLS]', 'bay', '##esian', 'regression', '[SEP]']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 1: tokenisation\n",
+    "inputs = tokenizer(text, return_tensors=\"pt\", truncation=True)\n",
+    "print(\"Token ids:\", inputs[\"input_ids\"][0].tolist())\n",
+    "print(\"Tokens:   \", tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"][0]))"
+   ],
+   "id": "myIlZ7w4CJso"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "kLsKFcwYCJso",
+    "outputId": "46c52044-a965-4772-f41b-79656daab121"
+   },
+   "execution_count": 8,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Logits: tensor([[ 2.7019, -2.2033]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 2: model (raw scores)\n",
+    "with torch.no_grad():\n",
+    "    outputs = model(**inputs)\n",
+    "print(\"Logits:\", outputs.logits)"
+   ],
+   "id": "kLsKFcwYCJso"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p1OQoHyxCJso",
+    "outputId": "eafb93ba-800b-4ecf-a901-fd9a7ddfe751"
+   },
+   "execution_count": 9,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "NEGATIVE 0.993\n",
+      "POSITIVE 0.007\n",
+      "Predicted label: NEGATIVE\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Step 3: post-processing (probabilities and label)\n",
+    "probs = torch.softmax(outputs.logits, dim=-1)[0]\n",
+    "for i, p in enumerate(probs):\n",
+    "    print(model.config.id2label[i], round(p.item(), 3))\n",
+    "print(\"Predicted label:\", model.config.id2label[int(probs.argmax())])"
+   ],
+   "id": "p1OQoHyxCJso"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "baj3dwenCJso"
+   },
+   "source": [
+    "Now the same thing in one line with the pipeline. The answer should match the step-by-step result above."
+   ],
+   "id": "baj3dwenCJso"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 66,
+     "referenced_widgets": [
+      "179de69e391b4e2caeebc1ea8537b400",
+      "db977a3cff634af080ce9a6b1f7a087b",
+      "f0363a5dbc7b40668e404098a72a6fee",
+      "6e528b5b838946a2aa55f06bd679baf9",
+      "b445814b34e84242b3268aaf5a68fb0e",
+      "51b3ed14df864c25a3b74a46bda8ba6b",
+      "c219e45afef44b4cabf8fc2e7624f7c0",
+      "56f7c5ed5bd84fc8928ee55626942f04",
+      "095e2a3bf3f24ab2919b43ce58b13f0e",
+      "494ba67693f043c58096b95f62b47fcb",
+      "a8319df6d790469baa4509e6c2334f3a"
+     ]
+    },
+    "id": "5arboZnCCJsp",
+    "outputId": "006f7deb-792f-409e-f925-cdb232f656a6"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Loading weights:   0%|          | 0/104 [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "179de69e391b4e2caeebc1ea8537b400"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[{'label': 'NEGATIVE', 'score': 0.9926464557647705}]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "sentiment = pipeline(\"sentiment-analysis\", model=model_name)\n",
+    "sentiment(text)"
+   ],
+   "id": "5arboZnCCJsp"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0YdrbwhxCJsp"
+   },
+   "source": [
+    "## 4. Zero-shot classification of issue titles\n",
+    "Sentiment is not very useful for software issues, so the main analysis uses **zero-shot classification**. This lets a model sort text into categories we choose, without any training on our data. Each title is classified as a *bug report*, a *feature request*, or a *documentation issue*.\n",
+    "\n",
+    "The model used is a smaller, faster zero-shot model so that it runs on an ordinary computer."
+   ],
+   "id": "0YdrbwhxCJsp"
+  },
+  {
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+       "pytorch_model.bin: reconstructing file:   0%|          |  0.00B / 1.02GB            "
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+     "data": {
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+      ],
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+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Loading weights:   0%|          | 0/283 [00:00<?, ?it/s]"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
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+     "data": {
+      "text/plain": [
+       "model.safetensors: reconstructing file:   0%|          |  0.00B / 1.02GB            "
+      ],
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+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "model.safetensors: downloading bytes:           |  0.00B            "
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+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
+      ],
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+    },
+    {
+     "output_type": "display_data",
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+      ],
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+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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+      ],
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+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "3cd0a1a737574efa831d10ad68942740"
+      }
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "{'sequence': 'Bayesian regression',\n",
+       " 'labels': ['feature request', 'documentation issue', 'bug report'],\n",
+       " 'scores': [0.606218159198761, 0.3012276887893677, 0.09255419671535492]}"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "zero_shot = pipeline(\n",
+    "    \"zero-shot-classification\",\n",
+    "    model=\"valhalla/distilbart-mnli-12-3\",\n",
+    ")\n",
+    "categories = [\"bug report\", \"feature request\", \"documentation issue\"]\n",
+    "\n",
+    "zero_shot(df[\"title\"].iloc[1], candidate_labels=categories)"
+   ],
+   "id": "Ng637NuOCJsp"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZRerMdWgCJsp"
+   },
+   "source": [
+    "Now run it on all 300 titles. This can take a few minutes."
+   ],
+   "id": "ZRerMdWgCJsp"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "EdWKxQCpCJsp",
+    "outputId": "40d0fc0f-b06d-4bf9-9416-b30c313404cd"
+   },
+   "execution_count": 12,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                                             title   predicted_category  \\\n",
+       "0                 remove Y from fit in OneClassSVM      feature request   \n",
+       "1                              Bayesian regression      feature request   \n",
+       "2    Return index of support vectors in sparse SVM  documentation issue   \n",
+       "3                               LARS module broken           bug report   \n",
+       "4  implement warm restarts for logistic regression      feature request   \n",
+       "5                         improvements for the doc  documentation issue   \n",
+       "6                 add slides to doc (or reference)  documentation issue   \n",
+       "7      plot examples by default and capture stdout      feature request   \n",
+       "8           kfold cross validation assertion check      feature request   \n",
+       "9             Sort decision function in svm module      feature request   \n",
+       "\n",
+       "   predicted_score  \n",
+       "0            0.494  \n",
+       "1            0.606  \n",
+       "2            0.522  \n",
+       "3            0.541  \n",
+       "4            0.953  \n",
+       "5            0.840  \n",
+       "6            0.844  \n",
+       "7            0.501  \n",
+       "8            0.375  \n",
+       "9            0.429  "
+      ],
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+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <thead>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bayesian regression</td>\n",
+       "      <td>feature request</td>\n",
+       "      <td>0.606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Return index of support vectors in sparse SVM</td>\n",
+       "      <td>documentation issue</td>\n",
+       "      <td>0.522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LARS module broken</td>\n",
+       "      <td>bug report</td>\n",
+       "      <td>0.541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>implement warm restarts for logistic regression</td>\n",
+       "      <td>feature request</td>\n",
+       "      <td>0.953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>improvements for the doc</td>\n",
+       "      <td>documentation issue</td>\n",
+       "      <td>0.840</td>\n",
+       "    </tr>\n",
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+       "      <th>6</th>\n",
+       "      <td>add slides to doc (or reference)</td>\n",
+       "      <td>documentation issue</td>\n",
+       "      <td>0.844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>plot examples by default and capture stdout</td>\n",
+       "      <td>feature request</td>\n",
+       "      <td>0.501</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>kfold cross validation assertion check</td>\n",
+       "      <td>feature request</td>\n",
+       "      <td>0.375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Sort decision function in svm module</td>\n",
+       "      <td>feature request</td>\n",
+       "      <td>0.429</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
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+       "  <style>\n",
+       "    .colab-df-container {\n",
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+       "      gap: 12px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert {\n",
+       "      background-color: #E8F0FE;\n",
+       "      border: none;\n",
+       "      border-radius: 50%;\n",
+       "      cursor: pointer;\n",
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+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-bd9df951-be6e-4212-a253-4b61bdbc5284 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-bd9df951-be6e-4212-a253-4b61bdbc5284');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "summary": "{\n  \"name\": \"df[[\\\"title\\\", \\\"predicted_category\\\", \\\"predicted_score\\\"]]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"kfold cross validation assertion check\",\n          \"Bayesian regression\",\n          \"improvements for the doc\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"feature request\",\n          \"documentation issue\",\n          \"bug report\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19752538514777734,\n        \"min\": 0.375,\n        \"max\": 0.953,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.375,\n          0.606,\n          0.84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "source": [
+    "titles = df[\"title\"].tolist()\n",
+    "results = zero_shot(titles, candidate_labels=categories, batch_size=8)\n",
+    "\n",
+    "df[\"predicted_category\"] = [r[\"labels\"][0] for r in results]\n",
+    "df[\"predicted_score\"] = [round(r[\"scores\"][0], 3) for r in results]\n",
+    "\n",
+    "df[[\"title\", \"predicted_category\", \"predicted_score\"]].head(10)"
+   ],
+   "id": "EdWKxQCpCJsp"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "j67R1vNFCJsp"
+   },
+   "source": [
+    "## 5. Initial insights"
+   ],
+   "id": "j67R1vNFCJsp"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tFiuOQrqCJsp"
+   },
+   "source": [
+    "### 5.1 Predicted categories"
+   ],
+   "id": "tFiuOQrqCJsp"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 574
+    },
+    "id": "GRAXubaSCJsp",
+    "outputId": "4a51ab50-9319-413b-88a5-6850238d30ce"
+   },
+   "execution_count": 13,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "predicted_category\n",
+      "feature request        114\n",
+      "documentation issue    111\n",
+      "bug report              75\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "counts = df[\"predicted_category\"].value_counts()\n",
+    "print(counts)\n",
+    "\n",
+    "counts.plot(kind=\"bar\", color=\"steelblue\")\n",
+    "plt.title(\"Predicted category of issue titles (zero-shot)\")\n",
+    "plt.ylabel(\"Number of issues\")\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"outputs/predicted_categories.png\")\n",
+    "plt.show()"
+   ],
+   "id": "GRAXubaSCJsp"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HnymjYReCJsp"
+   },
+   "source": [
+    "### 5.2 How confident is the model?"
+   ],
+   "id": "HnymjYReCJsp"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 643
+    },
+    "id": "_d0ikCPyCJsp",
+    "outputId": "e1b205d4-efca-4cd5-83e2-add99d43c1f7"
+   },
+   "execution_count": 14,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "count    300.000000\n",
+      "mean       0.639637\n",
+      "std        0.180065\n",
+      "min        0.348000\n",
+      "25%        0.491250\n",
+      "50%        0.591500\n",
+      "75%        0.793000\n",
+      "max        0.991000\n",
+      "Name: predicted_score, dtype: float64\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "print(df[\"predicted_score\"].describe())\n",
+    "\n",
+    "df[\"predicted_score\"].plot(kind=\"hist\", bins=20, color=\"steelblue\")\n",
+    "plt.title(\"Model confidence for the top category\")\n",
+    "plt.xlabel(\"Confidence score\")\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"outputs/confidence_hist.png\")\n",
+    "plt.show()"
+   ],
+   "id": "_d0ikCPyCJsp"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UK8EWhv1CJsq"
+   },
+   "source": [
+    "Because there are three categories, a score near 0.33 means the model is basically guessing. Scores near 1.0 mean it is very sure."
+   ],
+   "id": "UK8EWhv1CJsq"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9F9ckATvCJsq"
+   },
+   "source": [
+    "### 5.3 Compare with the real GitHub labels\n",
+    "Only some issues have a GitHub label. We turn the labels into the same three categories so they can be compared:\n",
+    "- label contains **Bug** -> bug report\n",
+    "- label contains **Documentation** -> documentation issue\n",
+    "- label contains **New Feature** or **Enhancement** -> feature request"
+   ],
+   "id": "9F9ckATvCJsq"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NSpHQAXvCJsq",
+    "outputId": "4b7362d1-149b-4211-fb87-ba50d4075ff5"
+   },
+   "execution_count": 15,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Issues with a usable label: 65 out of 300\n",
+      "true_category\n",
+      "feature request        54\n",
+      "bug report              7\n",
+      "documentation issue     4\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "def map_label(label):\n",
+    "    if pd.isna(label):\n",
+    "        return None\n",
+    "    if \"Bug\" in label:\n",
+    "        return \"bug report\"\n",
+    "    if \"Documentation\" in label:\n",
+    "        return \"documentation issue\"\n",
+    "    if \"New Feature\" in label or \"Enhancement\" in label:\n",
+    "        return \"feature request\"\n",
+    "    return None\n",
+    "\n",
+    "df[\"true_category\"] = df[\"labels\"].apply(map_label)\n",
+    "labelled = df[df[\"true_category\"].notna()].copy()\n",
+    "\n",
+    "print(\"Issues with a usable label:\", len(labelled), \"out of\", len(df))\n",
+    "print(labelled[\"true_category\"].value_counts())"
+   ],
+   "id": "NSpHQAXvCJsq"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 192
+    },
+    "id": "9f4do4M5CJsq",
+    "outputId": "54d17089-ed55-4752-a7ae-03b7f18ea640"
+   },
+   "execution_count": 16,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Agreement with GitHub labels: 0.738\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Model prediction     bug report  documentation issue  feature request\n",
+       "GitHub label                                                         \n",
+       "bug report                    4                    2                1\n",
+       "documentation issue           0                    4                0\n",
+       "feature request               2                   12               40"
+      ],
+      "text/html": [
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+       "    <div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Model prediction</th>\n",
+       "      <th>bug report</th>\n",
+       "      <th>documentation issue</th>\n",
+       "      <th>feature request</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GitHub label</th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "    <tr>\n",
+       "      <th>bug report</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>documentation issue</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>feature request</th>\n",
+       "      <td>2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>40</td>\n",
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+       "</div>\n",
+       "    <div class=\"colab-df-buttons\">\n",
+       "\n",
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+       "    .colab-df-container {\n",
+       "      display:flex;\n",
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+       "      display: none;\n",
+       "      fill: #1967D2;\n",
+       "      height: 32px;\n",
+       "      padding: 0 0 0 0;\n",
+       "      width: 32px;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-convert:hover {\n",
+       "      background-color: #E2EBFA;\n",
+       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+       "      fill: #174EA6;\n",
+       "    }\n",
+       "\n",
+       "    .colab-df-buttons div {\n",
+       "      margin-bottom: 4px;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+       "      fill: #FFFFFF;\n",
+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-54c43e94-4ae4-46c1-9c8a-c3f8a649572d button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-54c43e94-4ae4-46c1-9c8a-c3f8a649572d');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
+       "        const docLink = document.createElement('div');\n",
+       "        docLink.innerHTML = docLinkHtml;\n",
+       "        element.appendChild(docLink);\n",
+       "      }\n",
+       "    </script>\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "    </div>\n",
+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "summary": "{\n  \"name\": \")\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"GitHub label\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"bug report\",\n          \"documentation issue\",\n          \"feature request\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bug report\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"documentation issue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 2,\n        \"max\": 12,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          4,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature request\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 0,\n        \"max\": 40,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "source": [
+    "accuracy = (labelled[\"true_category\"] == labelled[\"predicted_category\"]).mean()\n",
+    "print(\"Agreement with GitHub labels:\", round(accuracy, 3))\n",
+    "\n",
+    "pd.crosstab(\n",
+    "    labelled[\"true_category\"],\n",
+    "    labelled[\"predicted_category\"],\n",
+    "    rownames=[\"GitHub label\"],\n",
+    "    colnames=[\"Model prediction\"],\n",
+    ")"
+   ],
+   "id": "9f4do4M5CJsq"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KteCBzcfCJsq"
+   },
+   "source": [
+    "Read the table across: each row is the real label, and the columns show what the model predicted. Numbers on the diagonal are correct predictions."
+   ],
+   "id": "KteCBzcfCJsq"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XlKyjsbKCJsq"
+   },
+   "source": [
+    "### 5.4 Sentiment as a second view"
+   ],
+   "id": "XlKyjsbKCJsq"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 556
+    },
+    "id": "uRi1ZN1XCJsq",
+    "outputId": "d189d59e-2a37-4ab9-b2b0-f785a4aba325"
+   },
+   "execution_count": 17,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "sentiment\n",
+      "NEGATIVE    261\n",
+      "POSITIVE     39\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "sent = sentiment(titles, truncation=True, batch_size=16)\n",
+    "df[\"sentiment\"] = [s[\"label\"] for s in sent]\n",
+    "print(df[\"sentiment\"].value_counts())\n",
+    "\n",
+    "df[\"sentiment\"].value_counts().plot(kind=\"bar\", color=\"steelblue\")\n",
+    "plt.title(\"Sentiment of issue titles (general-purpose model)\")\n",
+    "plt.ylabel(\"Number of issues\")\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"outputs/sentiment.png\")\n",
+    "plt.show()"
+   ],
+   "id": "uRi1ZN1XCJsq"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "J2pnd0DrCJsq"
+   },
+   "source": [
+    "### 5.5 Examples to inspect by eye"
+   ],
+   "id": "J2pnd0DrCJsq"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 430
+    },
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+   },
+   "execution_count": 18,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Most confident predictions:\n"
+     ]
+    },
+    {
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+       "                                                 title   predicted_category  \\\n",
+       "77               Documentation about ROC has a mistake  documentation issue   \n",
+       "250          document classification example is broken  documentation issue   \n",
+       "217  Broken \"Citing scikit-learn\" link from nested ...  documentation issue   \n",
+       "50              libsvm's parameter p is not documented  documentation issue   \n",
+       "25                  GridSearchCV doc lacks information  documentation issue   \n",
+       "\n",
+       "     predicted_score  \n",
+       "77             0.991  \n",
+       "250            0.990  \n",
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+       "50             0.985  \n",
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+       "\n",
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+       "          + ' to learn more about interactive tables.';\n",
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+     "name": "stdout",
+     "text": [
+      "Least confident predictions:\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "                                                 title   predicted_category  \\\n",
+       "90               comment tests in LLE (arpack backend)           bug report   \n",
+       "141  linear_model.LinearRegression attr. coef_ wron...  documentation issue   \n",
+       "107                       strange behaviors of svm.SVC      feature request   \n",
+       "75   kernels in pairwise module inconsistent with l...  documentation issue   \n",
+       "185  Shape problems building a pipeline containing ...  documentation issue   \n",
+       "\n",
+       "     predicted_score  \n",
+       "90             0.348  \n",
+       "141            0.360  \n",
+       "107            0.361  \n",
+       "75             0.361  \n",
+       "185            0.361  "
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+       "      <td>linear_model.LinearRegression attr. coef_ wron...</td>\n",
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+       "      <th>107</th>\n",
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+       "      <th>185</th>\n",
+       "      <td>Shape problems building a pipeline containing ...</td>\n",
+       "      <td>documentation issue</td>\n",
+       "      <td>0.361</td>\n",
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+       "\n",
+       "    [theme=dark] .colab-df-convert {\n",
+       "      background-color: #3B4455;\n",
+       "      fill: #D2E3FC;\n",
+       "    }\n",
+       "\n",
+       "    [theme=dark] .colab-df-convert:hover {\n",
+       "      background-color: #434B5C;\n",
+       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+       "    }\n",
+       "  </style>\n",
+       "\n",
+       "    <script>\n",
+       "      const buttonEl =\n",
+       "        document.querySelector('#df-6de6d00d-ccbc-4892-80b8-485857cff960 button.colab-df-convert');\n",
+       "      buttonEl.style.display =\n",
+       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+       "\n",
+       "      async function convertToInteractive(key) {\n",
+       "        const element = document.querySelector('#df-6de6d00d-ccbc-4892-80b8-485857cff960');\n",
+       "        const dataTable =\n",
+       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+       "                                                    [key], {});\n",
+       "        if (!dataTable) return;\n",
+       "\n",
+       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+       "          + ' to learn more about interactive tables.';\n",
+       "        element.innerHTML = '';\n",
+       "        dataTable['output_type'] = 'display_data';\n",
+       "        await google.colab.output.renderOutput(dataTable, element);\n",
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+       "\n",
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+       "  </div>\n"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "dataframe",
+       "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"linear_model.LinearRegression attr. coef_ wrong shape\",\n          \"Shape problems building a pipeline containing PatchExtractor and MiniBatchDictionaryLearning\",\n          \"strange behaviors of svm.SVC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"bug report\",\n          \"documentation issue\",\n          \"feature request\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005718391382198324,\n        \"min\": 0.348,\n        \"max\": 0.361,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.348,\n          0.36,\n          0.361\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+      }
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "print(\"Most confident predictions:\")\n",
+    "display(df.sort_values(\"predicted_score\", ascending=False)[[\"title\",\"predicted_category\",\"predicted_score\"]].head(5))\n",
+    "\n",
+    "print(\"Least confident predictions:\")\n",
+    "display(df.sort_values(\"predicted_score\")[[\"title\",\"predicted_category\",\"predicted_score\"]].head(5))"
+   ],
+   "id": "-L5hz_prCJsr"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JYVkcZPeCJsr"
+   },
+   "source": [
+    "## 6. Limitations\n",
+    "- **Only titles were classified.** Titles are short, so the model has little to go on. Bodies would give more context but are much longer, and 9 are empty.\n",
+    "- **Few labels to check against.** Only a minority of issues have a GitHub label, so the agreement figure is based on a small group and could change a lot with a few more issues.\n",
+    "- **Labels are not perfect truth.** GitHub labels were added by project members, and \"Enhancement\" and \"New Feature\" are merged here into one category.\n",
+    "- **Category choice matters.** Zero-shot results depend on how the category names are worded. Different names could give different results.\n",
+    "- **Sentiment model mismatch.** The sentiment model was trained on movie reviews. Technical issue titles often sound negative because they describe problems, not because the writer is unhappy.\n",
+    "- **Old data.** All issues are from 2010 to 2012, so results say little about recent scikit-learn issues.\n",
+    "- **No human review.** Predictions were only spot-checked, not manually verified."
+   ],
+   "id": "JYVkcZPeCJsr"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jL3gK7i_CJsr"
+   },
+   "source": [
+    "## 7. Saving outputs and storage\n",
+    "The predictions are saved to a local `outputs` folder together with the charts. The source data stays on the Hugging Face Hub, which keeps a version history. No passwords or API tokens are used or stored in this notebook."
+   ],
+   "id": "jL3gK7i_CJsr"
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dZ44f9rtCJsr",
+    "outputId": "97606c46-b132-4180-8a67-18ba2e5ec36d"
+   },
+   "execution_count": 19,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "['sentiment.png', 'confidence_hist.png', 'predicted_categories.png', 'issue_predictions.csv']\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.to_csv(\"outputs/issue_predictions.csv\", index=False)\n",
+    "print(os.listdir(\"outputs\"))"
+   ],
+   "id": "dZ44f9rtCJsr"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary of findings\n",
+    "\n",
+    "- **Pipeline demonstration:** The title \"Bayesian regression\" was split into 5 tokens, passed through the model to give raw scores (logits), and converted into probabilities. The manual result (NEGATIVE, 0.993) matched the one-line pipeline.\n",
+    "- **Zero-shot classification:** The model classified all 300 titles. Predictions were spread across feature request (114), documentation issue (111) and bug report (75).\n",
+    "- **Agreement with labels:** Of the 65 issues with a usable GitHub label, the model agreed with 48 (73.8%). However, 54 of those 65 are feature requests, so always predicting \"feature request\" would give about 83%. The model therefore performed worse than this simple baseline and its accuracy should not be read as strong performance.\n",
+    "- **Error pattern:** The model over-predicted documentation issues (12 labelled feature requests were predicted as documentation) and found only 4 of 7 labelled bugs. All 4 labelled documentation issues were correct.\n",
+    "- **Confidence:** Titles with explicit words like \"documentation\" or \"doc\" had confidence above 0.98. The least confident predictions were close to 0.35, which is near random for three categories, and several were visibly wrong.\n",
+    "- **Sentiment:** The general-purpose sentiment model labelled 261 of 300 titles NEGATIVE. Bug reports and feature requests describe problems, so this reflects the model's training on reviews and not the mood of the writers. Sentiment is not a useful signal for this dataset.\n",
+    "- **Conclusion:** Zero-shot classification is useful for obvious cases such as documentation issues but unreliable for ambiguous titles. Results are limited by short titles, few labels to check against, and the 2010 to 2012 date range."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "colab": {
+   "provenance": [],
+   "gpuType": "T4"
+  },
+  "accelerator": "GPU",
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