Reference annotations: finalize prediction pipeline

author: adamjanovsky 2023-03-17 14:40:20 +0100
committer: adamjanovsky 2023-03-17 14:40:20 +0100
commit: a6b008b167db388476e5dfb5990631e207a764d6 (patch)
tree: e71e4bbf513c8842ffcbeccc07abfed3162a73a9
parent: 731b3ea9c118a374aa1c1194305741b6e7909e83 (diff)
download: sec-certs-a6b008b167db388476e5dfb5990631e207a764d6.tar.gz
sec-certs-a6b008b167db388476e5dfb5990631e207a764d6.tar.zst
sec-certs-a6b008b167db388476e5dfb5990631e207a764d6.zip
23 files changed, 675 insertions, 378 deletions
diff --git a/notebooks/cc/reference_annotations/prediction.ipynb b/notebooks/cc/reference_annotations/prediction.ipynb
index add6e112..57fc8de1 100644
--- a/notebooks/cc/reference_annotations/prediction.ipynb
+++ b/notebooks/cc/reference_annotations/prediction.ipynb
@@ -1,86 +1,63 @@
 {
  "cells": [
   {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Prediction of reference annotations in CC Certificates\n",
-    "\n",
-    "This notebook:\n",
-    "- loads dataframe of a dataset with `(dgst, cert_id, sentences, label)`\n",
-    "- Trains a model to classify the sentences related to certificate reference to their common sentiment (meaning of reference)"
-   ]
-  },
-  {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MIG-a5459e6a-b26d-5985-874c-528458a7728b\n"
+      "Resolved repo directory as: /var/tmp/xjanovsk/certs/sec-certs\n"
      ]
     }
    ],
    "source": [
     "# When on Aura, it is important to first set CUDA_VISIBLE_DEVICES environment variable directly from notebook\n",
     "# For available GPUs, see https://www.fi.muni.cz/tech/unix/aura.html.cs\n",
+    "from __future__ import annotations\n",
     "\n",
     "import os\n",
     "\n",
-    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"MIG-a5459e6a-b26d-5985-874c-528458a7728b\"\n",
-    "print(os.getenv(\"CUDA_VISIBLE_DEVICES\"))\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"MIG-4f7fbfb7-a8a2-553d-875a-d9d56baf97a\"\n",
+    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"FALSE\"\n",
     "\n",
-    "import pandas as pd\n",
     "from sec_certs.utils.nlp import prec_recall_metric\n",
     "from pathlib import Path\n",
-    "from sec_certs.model import ReferenceSegmentExtractor, ReferenceAnnotator, ReferenceAnnotatorTrainer\n",
+    "from sec_certs.model import ReferenceAnnotatorTrainer, ReferenceSegmentExtractor\n",
     "from sec_certs.dataset import CCDataset\n",
-    "import numpy as np\n",
     "from sklearn.metrics import ConfusionMatrixDisplay\n",
     "\n",
-    "REPO_ROOT = Path(\"../../../\").resolve()"
+    "REPO_ROOT = Path(\"./../../../\").resolve()\n",
+    "print(f\"Resolved repo directory as: {REPO_ROOT}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load df or fully process it with ReferenceSegmentExtractor\n",
-    "df = pd.read_csv(REPO_ROOT / \"datasets/reference_classification_dataset.csv\")\n",
-    "\n",
-    "# dset = CCDataset.from_json(REPO_ROOT / \"datasets/cc/cc_dataset.json\")\n",
-    "# df = ReferenceSegmentExtractor().prepare_df_from_cc_certs([x for x in dset])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "config.json not found in HuggingFace Hub\n",
+      "Recovering reference segments for targets: 100%|██████████| 66/66 [00:20<00:00,  3.29it/s]\n",
+      "Recovering reference segments for reports: 100%|██████████| 82/82 [00:04<00:00, 20.45it/s]\n",
+      "config.json not found in HuggingFace Hub.\n",
       "model_head.pkl not found on HuggingFace Hub, initialising classification head with random weights. You should TRAIN this model on a downstream task to use it for predictions and inference.\n",
       "Applying column mapping to training dataset\n",
       "***** Running training *****\n",
-      "  Num examples = 17040\n",
+      "  Num examples = 560\n",
       "  Num epochs = 1\n",
-      "  Total optimization steps = 1065\n",
+      "  Total optimization steps = 35\n",
       "  Total train batch size = 16\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae824b435e5646198a37586c30e89c1f",
+       "model_id": "5cc28ba22e614f7988d5373d55989813",
        "version_major": 2,
        "version_minor": 0
       },
@@ -94,12 +71,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6a17a246cf824a758c9b814d2fb6954c",
+       "model_id": "0a74771fe7524ac4b743236e318afeed",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Iteration:   0%|          | 0/1065 [00:00<?, ?it/s]"
+       "Iteration:   0%|          | 0/35 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -118,108 +95,103 @@
      "output_type": "stream",
      "text": [
       "Internal evaluation (of model working on individual segments)\n",
-      "{'precision': 0.13023255813953488, 'recall': 0.13023255813953488}\n",
+      "{'precision': 0.5384615384615384, 'recall': 0.5384615384615384}\n",
       "Actual evaluation after ensemble soft voting\n",
-      "{'precision': 0.6923076923076923, 'recall': 0.6923076923076923}\n"
+      "{'precision': 0.5714285714285714, 'recall': 0.5714285714285714}\n",
+      "The dataset now contains 43 certificates with annotated references.\n"
      ]
     }
    ],
    "source": [
-    "# init trainer, train and evaluate\n",
-    "trainer = ReferenceAnnotatorTrainer.from_df(df, prec_recall_metric, \"transformer\", \"training\")\n",
-    "trainer.train()\n",
-    "trainer.evaluate()\n",
-    "clf = trainer.clf\n",
+    "# End-to-end example of reference annotation\n",
+    "dset: CCDataset = CCDataset.from_json(REPO_ROOT / \"my_datasets/cc/cc_dataset.json\")\n",
+    "dset.annotate_references(mode=\"training\")\n",
     "\n",
-    "# # Serialize and de-serialize the classifier\n",
-    "trainer.clf.save_pretrained(REPO_ROOT / \"datasets/cc/reference_annotator\")\n",
-    "clf = ReferenceAnnotator.from_pretrained(REPO_ROOT / \"datasets/cc/reference_annotator\")"
+    "print(f\"The dataset now contains {len([x for x in dset if x.heuristics.annotated_references])} certificates with annotated references.\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Take a look at misclassified instances\n",
-    "df_train = clf.predict_df(trainer._train_dataset)\n",
-    "df_eval = clf.predict_df(trainer._eval_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Recovering reference segments for targets: 100%|██████████| 66/66 [00:20<00:00,  3.29it/s]\n",
+      "Recovering reference segments for reports: 100%|██████████| 82/82 [00:03<00:00, 27.26it/s]\n",
+      "config.json not found in HuggingFace Hub.\n",
+      "model_head.pkl not found on HuggingFace Hub, initialising classification head with random weights. You should TRAIN this model on a downstream task to use it for predictions and inference.\n",
+      "Applying column mapping to training dataset\n",
+      "***** Running training *****\n",
+      "  Num examples = 560\n",
+      "  Num epochs = 1\n",
+      "  Total optimization steps = 35\n",
+      "  Total train batch size = 16\n"
+     ]
+    },
+    {
      "data": {
-      "text/html": [
-       "<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",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>dgst</th>\n",
-       "      <th>referenced_cert_id</th>\n",
-       "      <th>label</th>\n",
-       "      <th>segments</th>\n",
-       "      <th>y_proba</th>\n",
-       "      <th>y_pred</th>\n",
-       "      <th>correct</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>756</th>\n",
-       "      <td>ab3af998dff7a2ef</td>\n",
-       "      <td>ANSSI-CC-2017/47</td>\n",
-       "      <td>COMPONENT_USED</td>\n",
-       "      <td>[ANSSI-CC-2017/47 le 5 septembre 2017.\\n]</td>\n",
-       "      <td>[0.20372918003354076, 0.1846070821627411, 0.15...</td>\n",
-       "      <td>ON_PLATFORM</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41e97704d5954ddf80c1ce3a1d2bcc16",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "                 dgst referenced_cert_id           label  \\\n",
-       "756  ab3af998dff7a2ef   ANSSI-CC-2017/47  COMPONENT_USED   \n",
-       "\n",
-       "                                      segments  \\\n",
-       "756  [ANSSI-CC-2017/47 le 5 septembre 2017.\\n]   \n",
-       "\n",
-       "                                               y_proba       y_pred  correct  \n",
-       "756  [0.20372918003354076, 0.1846070821627411, 0.15...  ON_PLATFORM    False  "
+       "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a08aa2410f042118c79fb66d48580d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Iteration:   0%|          | 0/35 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Applying column mapping to evaluation dataset\n",
+      "***** Running evaluation *****\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Internal evaluation (of model working on individual segments)\n",
+      "{'precision': 0.5384615384615384, 'recall': 0.5384615384615384}\n",
+      "Actual evaluation after ensemble soft voting\n",
+      "{'precision': 0.5714285714285714, 'recall': 0.5714285714285714}\n"
+     ]
     }
    ],
    "source": [
-    "df_train.loc[~df_train.correct].head()"
+    "# More dissected example with reference annotations\n",
+    "df = ReferenceSegmentExtractor().prepare_df_from_cc_certs(list(dset.certs.values()))\n",
+    "trainer = ReferenceAnnotatorTrainer.from_df(df, prec_recall_metric, method=\"transformer\", mode=\"training\")\n",
+    "trainer.train()\n",
+    "trainer.evaluate()\n",
+    "\n",
+    "annotator = trainer.clf\n",
+    "df_predicted = annotator.predict_df(df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -246,7 +218,9 @@
        "      <th>dgst</th>\n",
        "      <th>referenced_cert_id</th>\n",
        "      <th>label</th>\n",
+       "      <th>split</th>\n",
        "      <th>segments</th>\n",
+       "      <th>lang</th>\n",
        "      <th>y_proba</th>\n",
        "      <th>y_pred</th>\n",
        "      <th>correct</th>\n",
@@ -254,117 +228,127 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>031667f4e242da61</td>\n",
-       "      <td>CCEVS-VR-07-0054</td>\n",
-       "      <td>ON_PLATFORM</td>\n",
-       "      <td>[[CCEVS-VR-07-0054] Common Criteria Evaluation...</td>\n",
-       "      <td>[0.08122691074400883, 0.593865460042782, 0.081...</td>\n",
+       "      <th>0</th>\n",
+       "      <td>0041baf85c9ca3ec</td>\n",
+       "      <td>BSI-DSZ-CC-0329-2006</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>[[9] Certification Report BSI-DSZ-CC-0329-2006...</td>\n",
+       "      <td>[en, en]</td>\n",
+       "      <td>[0.22958617238809345, 0.22039005448671678, 0.5...</td>\n",
        "      <td>COMPONENT_USED</td>\n",
-       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>0fe8df0e85116b61</td>\n",
-       "      <td>ANSSI-CC-2010/33</td>\n",
-       "      <td>COMPONENT_SHARED</td>\n",
-       "      <td>[CC IDeal Citiz (sur\\ncomposants SB23YR80B et ...</td>\n",
-       "      <td>[0.12980833111443874, 0.3509582926631846, 0.12...</td>\n",
+       "      <th>1</th>\n",
+       "      <td>0167c92c0d8c8b47</td>\n",
+       "      <td>BSI-DSZ-CC-0844-2014</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>train</td>\n",
+       "      <td>[As the evaluation work performed for this cer...</td>\n",
+       "      <td>[en, en, en]</td>\n",
+       "      <td>[0.2379531649933594, 0.2068740269318838, 0.555...</td>\n",
        "      <td>COMPONENT_USED</td>\n",
-       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>118</th>\n",
-       "      <td>16abf8ee0697e64f</td>\n",
-       "      <td>ANSSI-CC-2014/61</td>\n",
-       "      <td>COMPONENT_SHARED</td>\n",
-       "      <td>[sous la référence [ANSSI-CC-2014/61].\\n]</td>\n",
-       "      <td>[0.315498585227761, 0.13724939680170528, 0.136...</td>\n",
-       "      <td>ON_PLATFORM</td>\n",
-       "      <td>False</td>\n",
+       "      <th>2</th>\n",
+       "      <td>09b17cb9b3c8b1bb</td>\n",
+       "      <td>BSI-DSZ-CC-0917-2014</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>train</td>\n",
+       "      <td>[[PP0035], le 3 février\\n2014 sous la référenc...</td>\n",
+       "      <td>[fr, fr]</td>\n",
+       "      <td>[0.2828401820477907, 0.25239069649528456, 0.46...</td>\n",
+       "      <td>COMPONENT_USED</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>183</th>\n",
-       "      <td>22388445fe620ac0</td>\n",
-       "      <td>ANSSI-CC-2014/20</td>\n",
-       "      <td>EVALUATION_REUSED</td>\n",
-       "      <td>[[ANSSI-CC-\\n2014/20]\\nRapport de certificatio...</td>\n",
-       "      <td>[0.24171578677684094, 0.30556432298553843, 0.1...</td>\n",
+       "      <th>3</th>\n",
+       "      <td>0ac0120f667a8dcf</td>\n",
+       "      <td>BSI-DSZ-CC-0349-2006</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>train</td>\n",
+       "      <td>[[CR]\\nCertification Reports of underlying har...</td>\n",
+       "      <td>[en]</td>\n",
+       "      <td>[0.2314090673514774, 0.23112778759264926, 0.53...</td>\n",
        "      <td>COMPONENT_USED</td>\n",
-       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>198</th>\n",
-       "      <td>24de96ea505af909</td>\n",
-       "      <td>ANSSI-CC-2021/29</td>\n",
-       "      <td>ON_PLATFORM</td>\n",
-       "      <td>[[M01-PLF] Rapport de maintenance ANSSI-CC-202...</td>\n",
-       "      <td>[0.040365307026386466, 0.7341872530469308, 0.0...</td>\n",
-       "      <td>COMPONENT_USED</td>\n",
-       "      <td>False</td>\n",
+       "      <th>4</th>\n",
+       "      <td>0bf7a19b22163465</td>\n",
+       "      <td>ANSSI-CC-2015/36</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>train</td>\n",
+       "      <td>[[CER-\\n2015/36]\\nRapport de certification ANS...</td>\n",
+       "      <td>[fr]</td>\n",
+       "      <td>[0.3772119113773801, 0.2939748191664766, 0.328...</td>\n",
+       "      <td>EVALUATION_REUSED</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                 dgst referenced_cert_id              label  \\\n",
-       "13   031667f4e242da61   CCEVS-VR-07-0054        ON_PLATFORM   \n",
-       "91   0fe8df0e85116b61   ANSSI-CC-2010/33   COMPONENT_SHARED   \n",
-       "118  16abf8ee0697e64f   ANSSI-CC-2014/61   COMPONENT_SHARED   \n",
-       "183  22388445fe620ac0   ANSSI-CC-2014/20  EVALUATION_REUSED   \n",
-       "198  24de96ea505af909   ANSSI-CC-2021/29        ON_PLATFORM   \n",
+       "               dgst    referenced_cert_id label  split  \\\n",
+       "0  0041baf85c9ca3ec  BSI-DSZ-CC-0329-2006   NaN  valid   \n",
+       "1  0167c92c0d8c8b47  BSI-DSZ-CC-0844-2014   NaN  train   \n",
+       "2  09b17cb9b3c8b1bb  BSI-DSZ-CC-0917-2014   NaN  train   \n",
+       "3  0ac0120f667a8dcf  BSI-DSZ-CC-0349-2006   NaN  train   \n",
+       "4  0bf7a19b22163465      ANSSI-CC-2015/36   NaN  train   \n",
        "\n",
-       "                                              segments  \\\n",
-       "13   [[CCEVS-VR-07-0054] Common Criteria Evaluation...   \n",
-       "91   [CC IDeal Citiz (sur\\ncomposants SB23YR80B et ...   \n",
-       "118          [sous la référence [ANSSI-CC-2014/61].\\n]   \n",
-       "183  [[ANSSI-CC-\\n2014/20]\\nRapport de certificatio...   \n",
-       "198  [[M01-PLF] Rapport de maintenance ANSSI-CC-202...   \n",
+       "                                            segments          lang  \\\n",
+       "0  [[9] Certification Report BSI-DSZ-CC-0329-2006...      [en, en]   \n",
+       "1  [As the evaluation work performed for this cer...  [en, en, en]   \n",
+       "2  [[PP0035], le 3 février\\n2014 sous la référenc...      [fr, fr]   \n",
+       "3  [[CR]\\nCertification Reports of underlying har...          [en]   \n",
+       "4  [[CER-\\n2015/36]\\nRapport de certification ANS...          [fr]   \n",
        "\n",
-       "                                               y_proba          y_pred  \\\n",
-       "13   [0.08122691074400883, 0.593865460042782, 0.081...  COMPONENT_USED   \n",
-       "91   [0.12980833111443874, 0.3509582926631846, 0.12...  COMPONENT_USED   \n",
-       "118  [0.315498585227761, 0.13724939680170528, 0.136...     ON_PLATFORM   \n",
-       "183  [0.24171578677684094, 0.30556432298553843, 0.1...  COMPONENT_USED   \n",
-       "198  [0.040365307026386466, 0.7341872530469308, 0.0...  COMPONENT_USED   \n",
+       "                                             y_proba             y_pred  \\\n",
+       "0  [0.22958617238809345, 0.22039005448671678, 0.5...     COMPONENT_USED   \n",
+       "1  [0.2379531649933594, 0.2068740269318838, 0.555...     COMPONENT_USED   \n",
+       "2  [0.2828401820477907, 0.25239069649528456, 0.46...     COMPONENT_USED   \n",
+       "3  [0.2314090673514774, 0.23112778759264926, 0.53...     COMPONENT_USED   \n",
+       "4  [0.3772119113773801, 0.2939748191664766, 0.328...  EVALUATION_REUSED   \n",
        "\n",
-       "     correct  \n",
-       "13     False  \n",
-       "91     False  \n",
-       "118    False  \n",
-       "183    False  \n",
-       "198    False  "
+       "  correct  \n",
+       "0     NaN  \n",
+       "1     NaN  \n",
+       "2     NaN  \n",
+       "3     NaN  \n",
+       "4     NaN  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_eval.loc[~df_eval.correct].head()"
+    "df_predicted.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fd1715ae6a0>"
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f01e8061640>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -374,57 +358,8 @@
     }
    ],
    "source": [
-    "ConfusionMatrixDisplay.from_predictions(df_eval.label, df_eval.y_pred, labels=df_eval.label.unique(), display_labels=df_eval.label.unique(), xticks_rotation=90, normalize=\"pred\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "COMPONENT_USED       43\n",
-       "RECERTIFICATION      18\n",
-       "EVALUATION_REUSED    11\n",
-       "ON_PLATFORM           8\n",
-       "COMPONENT_SHARED      6\n",
-       "PREVIOUS_VERSION      5\n",
-       "Name: label, dtype: int64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_eval.label.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Experiments with ensemble for soft-voting on top of the base model.\n",
-    "# But this is dump, since the ensemble is position-specific w.r.t. sequence, while the individual sentences are not.\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "\n",
-    "df_train[\"y_proba_base\"] = df_train.segments.map(trainer._model.predict_proba).map(lambda x: x.flatten())\n",
-    "feat_len = df_train[\"y_proba_base\"].map(len).max()\n",
-    "df_train[\"rf_feature_vector\"] = df_train.y_proba_base.map(lambda x: np.pad(x, pad_width=(0, feat_len - len(x))))\n",
-    "\n",
-    "df_eval[\"y_proba_base\"] = df_eval.segments.map(trainer._model.predict_proba).map(lambda x: x.flatten())\n",
-    "df_eval[\"rf_feature_vector\"] = df_eval.y_proba_base.map(lambda x: np.pad(x, pad_width=(0, feat_len - len(x))))\n",
-    "\n",
-    "clf = RandomForestClassifier()\n",
-    "clf = clf.fit(df_train.rf_feature_vector.tolist(), df_train.label)\n",
-    "\n",
-    "df_train[\"rf_predict\"] = clf.predict(df_train.rf_feature_vector.tolist())\n",
-    "df_eval[\"rf_predict\"] = clf.predict(df_eval.rf_feature_vector.tolist())"
+    "df_labeled = df_predicted.loc[df_predicted.label.notnull()]\n",
+    "ConfusionMatrixDisplay.from_predictions(df_labeled.label, df_labeled.y_pred, labels=df_labeled.label.unique(), display_labels=df_labeled.label.unique(), xticks_rotation=90)"
    ]
   }
  ],
diff --git a/pyproject.toml b/pyproject.toml
index e83a4a6b..128c2d73 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,7 +30,6 @@
   dynamic = ["version"]
   dependencies = [
     "beautifulsoup4",
-    "billiard",
     "click",
     "html5lib",
     "jsonschema",
@@ -81,9 +80,10 @@
       "sphinx-design",
       "sphinx-copybutton",
       "ipython!=8.7.0",
+      "setfit",
+      "langdetect",
     ]
     test = ["pytest", "coverage", "pytest-cov"]
-    nlp = ["setfit", "langdetect"]
 
   [project.urls]
     Homepage = "https://seccerts.org"
diff --git a/requirements/nlp_requirements.txt b/requirements/all_requirements.txt
index 7ff4fbd2..5d49f2e2 100644
--- a/requirements/nlp_requirements.txt
+++ b/requirements/all_requirements.txt
@@ -1,9 +1,13 @@
+accessible-pygments==0.0.3
+    # via pydata-sphinx-theme
 aiohttp==3.8.4
     # via
     #   datasets
     #   fsspec
 aiosignal==1.3.1
     # via aiohttp
+alabaster==0.7.13
+    # via sphinx
 asttokens==2.2.1
     # via stack-data
 async-timeout==4.0.2
@@ -12,14 +16,24 @@ attrs==22.2.0
     # via
     #   aiohttp
     #   jsonschema
+    #   jupyter-cache
+    #   pytest
+babel==2.12.1
+    # via
+    #   pydata-sphinx-theme
+    #   sphinx
 backcall==0.2.0
     # via ipython
 beautifulsoup4==4.11.2
-    # via sec-certs (./../pyproject.toml)
-billiard==4.1.0
+    # via
+    #   pydata-sphinx-theme
+    #   sec-certs (./../pyproject.toml)
+black==23.1.0
     # via sec-certs (./../pyproject.toml)
 blis==0.7.9
     # via thinc
+build==0.10.0
+    # via pip-tools
 catalogue==2.0.8
     # via
     #   spacy
@@ -27,21 +41,32 @@ catalogue==2.0.8
     #   thinc
 certifi==2022.12.7
     # via requests
-charset-normalizer==3.0.1
+cfgv==3.3.1
+    # via pre-commit
+charset-normalizer==3.1.0
     # via
     #   aiohttp
     #   requests
 click==8.1.3
     # via
+    #   black
+    #   jupyter-cache
     #   nltk
+    #   pip-tools
     #   sec-certs (./../pyproject.toml)
     #   typer
+cmake==3.26.0
+    # via triton
 comm==0.1.2
     # via ipykernel
 confection==0.0.4
     # via thinc
 contourpy==1.0.7
     # via matplotlib
+coverage[toml]==7.2.2
+    # via
+    #   pytest-cov
+    #   sec-certs (./../pyproject.toml)
 cycler==0.11.0
     # via matplotlib
 cymem==2.0.7
@@ -49,9 +74,10 @@ cymem==2.0.7
     #   preshed
     #   spacy
     #   thinc
-datasets==2.9.0
+datasets==2.10.1
     # via
     #   evaluate
+    #   sec-certs (./../pyproject.toml)
     #   setfit
 debugpy==1.6.6
     # via ipykernel
@@ -64,70 +90,112 @@ dill==0.3.6
     #   datasets
     #   evaluate
     #   multiprocess
+distlib==0.3.6
+    # via virtualenv
 distro==1.8.0
     # via tabula-py
+docutils==0.19
+    # via
+    #   myst-parser
+    #   pydata-sphinx-theme
+    #   sphinx
 evaluate==0.4.0
     # via setfit
+exceptiongroup==1.1.1
+    # via pytest
 executing==1.2.0
     # via stack-data
-filelock==3.9.0
+fastjsonschema==2.16.3
+    # via nbformat
+filelock==3.10.0
     # via
     #   huggingface-hub
+    #   torch
     #   transformers
-fonttools==4.38.0
+    #   triton
+    #   virtualenv
+fonttools==4.39.2
     # via matplotlib
 frozenlist==1.3.3
     # via
     #   aiohttp
     #   aiosignal
-fsspec[http]==2023.1.0
+fsspec[http]==2023.3.0
     # via
     #   datasets
     #   evaluate
+gprof2dot==2022.7.29
+    # via pytest-profiling
+greenlet==2.0.2
+    # via sqlalchemy
 html5lib==1.1
     # via sec-certs (./../pyproject.toml)
-huggingface-hub==0.12.1
+huggingface-hub==0.13.2
     # via
     #   datasets
     #   evaluate
     #   sentence-transformers
     #   transformers
+identify==2.5.21
+    # via pre-commit
 idna==3.4
     # via
     #   requests
     #   yarl
+imagesize==1.4.1
+    # via sphinx
 importlib-metadata==6.0.0
-    # via jupyter-client
-importlib-resources==5.10.2
+    # via
+    #   jupyter-cache
+    #   jupyter-client
+    #   myst-nb
+    #   sphinx
+importlib-resources==5.12.0
     # via
     #   jsonschema
     #   matplotlib
-ipykernel==6.21.2
+iniconfig==2.0.0
+    # via pytest
+ipykernel==6.21.3
     # via
     #   ipywidgets
+    #   myst-nb
     #   sec-certs (./../pyproject.toml)
-ipython==8.10.0
+ipython==8.11.0
     # via
     #   ipykernel
     #   ipywidgets
+    #   myst-nb
+    #   sec-certs (./../pyproject.toml)
 ipywidgets==8.0.4
     # via sec-certs (./../pyproject.toml)
 jedi==0.18.2
     # via ipython
 jinja2==3.1.2
-    # via spacy
+    # via
+    #   myst-parser
+    #   spacy
+    #   sphinx
+    #   torch
 joblib==1.2.0
     # via
     #   nltk
     #   scikit-learn
 jsonschema==4.17.3
-    # via sec-certs (./../pyproject.toml)
+    # via
+    #   nbformat
+    #   sec-certs (./../pyproject.toml)
+jupyter-cache==0.5.0
+    # via myst-nb
 jupyter-client==8.0.3
-    # via ipykernel
-jupyter-core==5.2.0
+    # via
+    #   ipykernel
+    #   nbclient
+jupyter-core==5.3.0
     # via
     #   ipykernel
     #   jupyter-client
+    #   nbformat
 jupyterlab-widgets==3.0.5
     # via ipywidgets
 kiwisolver==1.4.4
@@ -136,13 +204,19 @@ langcodes==3.3.0
     # via spacy
 langdetect==1.0.9
     # via sec-certs (./../pyproject.toml)
+lit==15.0.7
+    # via triton
 lxml==4.9.2
     # via
     #   pikepdf
     #   sec-certs (./../pyproject.toml)
+markdown-it-py==2.2.0
+    # via
+    #   mdit-py-plugins
+    #   myst-parser
 markupsafe==2.1.2
     # via jinja2
-matplotlib==3.7.0
+matplotlib==3.7.1
     # via
     #   pysankeybeta
     #   seaborn
@@ -151,6 +225,14 @@ matplotlib-inline==0.1.6
     # via
     #   ipykernel
     #   ipython
+mdit-py-plugins==0.3.5
+    # via myst-parser
+mdurl==0.1.2
+    # via markdown-it-py
+memory-profiler==0.61.0
+    # via pytest-monitor
+mpmath==1.3.0
+    # via sympy
 multidict==6.0.4
     # via
     #   aiohttp
@@ -164,12 +246,37 @@ murmurhash==1.0.9
     #   preshed
     #   spacy
     #   thinc
+mypy==1.0.0
+    # via sec-certs (./../pyproject.toml)
+mypy-extensions==1.0.0
+    # via
+    #   black
+    #   mypy
+myst-nb==0.17.1
+    # via sec-certs (./../pyproject.toml)
+myst-parser==0.18.1
+    # via myst-nb
+nbclient==0.5.13
+    # via
+    #   jupyter-cache
+    #   myst-nb
+nbformat==5.7.3
+    # via
+    #   jupyter-cache
+    #   myst-nb
+    #   nbclient
 nest-asyncio==1.5.6
-    # via ipykernel
+    # via
+    #   ipykernel
+    #   nbclient
 networkx==3.0
-    # via sec-certs (./../pyproject.toml)
+    # via
+    #   sec-certs (./../pyproject.toml)
+    #   torch
 nltk==3.8.1
     # via sentence-transformers
+nodeenv==1.7.0
+    # via pre-commit
 numpy==1.24.2
     # via
     #   blis
@@ -193,15 +300,32 @@ numpy==1.24.2
 nvidia-cublas-cu11==11.10.3.66
     # via
     #   nvidia-cudnn-cu11
+    #   nvidia-cusolver-cu11
     #   torch
+nvidia-cuda-cupti-cu11==11.7.101
+    # via torch
 nvidia-cuda-nvrtc-cu11==11.7.99
     # via torch
 nvidia-cuda-runtime-cu11==11.7.99
     # via torch
 nvidia-cudnn-cu11==8.5.0.96
     # via torch
+nvidia-cufft-cu11==10.9.0.58
+    # via torch
+nvidia-curand-cu11==10.2.10.91
+    # via torch
+nvidia-cusolver-cu11==11.4.0.1
+    # via torch
+nvidia-cusparse-cu11==11.7.4.91
+    # via torch
+nvidia-nccl-cu11==2.14.3
+    # via torch
+nvidia-nvtx-cu11==11.7.91
+    # via torch
 packaging==23.0
     # via
+    #   black
+    #   build
     #   datasets
     #   deprecation
     #   evaluate
@@ -209,8 +333,11 @@ packaging==23.0
     #   ipykernel
     #   matplotlib
     #   pikepdf
+    #   pydata-sphinx-theme
+    #   pytest
     #   setuptools-scm
     #   spacy
+    #   sphinx
     #   thinc
     #   transformers
 pandas==1.5.3
@@ -223,6 +350,8 @@ pandas==1.5.3
     #   tabula-py
 parso==0.8.3
     # via jedi
+pathspec==0.11.1
+    # via black
 pathy==0.10.1
     # via spacy
 pdftotext==2.2.2
@@ -231,7 +360,7 @@ pexpect==4.8.0
     # via ipython
 pickleshare==0.7.5
     # via ipython
-pikepdf==7.1.0
+pikepdf==7.1.1
     # via sec-certs (./../pyproject.toml)
 pillow==9.4.0
     # via
@@ -239,20 +368,32 @@ pillow==9.4.0
     #   pikepdf
     #   sec-certs (./../pyproject.toml)
     #   torchvision
+pip-tools==6.12.3
+    # via sec-certs (./../pyproject.toml)
 pkgconfig==1.5.5
     # via sec-certs (./../pyproject.toml)
 pkgutil-resolve-name==1.3.10
     # via jsonschema
-platformdirs==3.0.0
-    # via jupyter-core
+platformdirs==3.1.1
+    # via
+    #   black
+    #   jupyter-core
+    #   virtualenv
+pluggy==1.0.0
+    # via pytest
+pre-commit==3.1.1
+    # via sec-certs (./../pyproject.toml)
 preshed==3.0.8
     # via
     #   spacy
     #   thinc
-prompt-toolkit==3.0.36
+prompt-toolkit==3.0.38
     # via ipython
 psutil==5.9.4
-    # via ipykernel
+    # via
+    #   ipykernel
+    #   memory-profiler
+    #   pytest-monitor
 ptyprocess==0.7.0
     # via pexpect
 pure-eval==0.2.2
@@ -261,21 +402,41 @@ pyarrow==11.0.0
     # via datasets
 pycryptodome==3.17
     # via pypdf
-pydantic==1.10.5
+pydantic==1.10.6
     # via
     #   confection
     #   spacy
     #   thinc
+pydata-sphinx-theme==0.13.1
+    # via sphinx-book-theme
 pygments==2.14.0
-    # via ipython
+    # via
+    #   accessible-pygments
+    #   ipython
+    #   pydata-sphinx-theme
+    #   sphinx
 pyparsing==3.0.9
     # via matplotlib
-pypdf[crypto]==3.4.1
+pypdf[crypto]==3.5.2
     # via sec-certs (./../pyproject.toml)
+pyproject-hooks==1.0.0
+    # via build
 pyrsistent==0.19.3
     # via jsonschema
 pysankeybeta==1.4.0
     # via sec-certs (./../pyproject.toml)
+pytest==7.2.2
+    # via
+    #   pytest-cov
+    #   pytest-monitor
+    #   pytest-profiling
+    #   sec-certs (./../pyproject.toml)
+pytest-cov==4.0.0
+    # via sec-certs (./../pyproject.toml)
+pytest-monitor==1.6.5
+    # via sec-certs (./../pyproject.toml)
+pytest-profiling==1.7.0
+    # via sec-certs (./../pyproject.toml)
 python-dateutil==2.8.2
     # via
     #   jupyter-client
@@ -283,14 +444,20 @@ python-dateutil==2.8.2
     #   pandas
     #   sec-certs (./../pyproject.toml)
 pytz==2022.7.1
-    # via pandas
+    # via
+    #   babel
+    #   pandas
 pyyaml==6.0
     # via
     #   datasets
     #   huggingface-hub
+    #   jupyter-cache
+    #   myst-nb
+    #   myst-parser
+    #   pre-commit
     #   sec-certs (./../pyproject.toml)
     #   transformers
-pyzmq==25.0.0
+pyzmq==25.0.1
     # via
     #   ipykernel
     #   jupyter-client
@@ -306,20 +473,24 @@ requests==2.28.2
     #   evaluate
     #   fsspec
     #   huggingface-hub
+    #   pytest-monitor
     #   responses
     #   sec-certs (./../pyproject.toml)
     #   spacy
+    #   sphinx
     #   torchvision
     #   transformers
 responses==0.18.0
     # via
     #   datasets
     #   evaluate
-scikit-learn==1.2.1
+ruff==0.0.239
+    # via sec-certs (./../pyproject.toml)
+scikit-learn==1.2.2
     # via
     #   sec-certs (./../pyproject.toml)
     #   sentence-transformers
-scipy==1.10.0
+scipy==1.10.1
     # via
     #   scikit-learn
     #   sec-certs (./../pyproject.toml)
@@ -341,47 +512,91 @@ six==1.16.0
     #   asttokens
     #   html5lib
     #   langdetect
+    #   pytest-profiling
     #   python-dateutil
 smart-open==6.3.0
     # via
     #   pathy
     #   spacy
+snowballstemmer==2.2.0
+    # via sphinx
 soupsieve==2.4
     # via beautifulsoup4
-spacy==3.5.0
+spacy==3.5.1
     # via sec-certs (./../pyproject.toml)
 spacy-legacy==3.0.12
     # via spacy
 spacy-loggers==1.0.4
     # via spacy
-srsly==2.4.5
+sphinx==5.3.0
+    # via
+    #   myst-nb
+    #   myst-parser
+    #   pydata-sphinx-theme
+    #   sec-certs (./../pyproject.toml)
+    #   sphinx-book-theme
+    #   sphinx-copybutton
+    #   sphinx-design
+sphinx-book-theme==1.0.0
+    # via sec-certs (./../pyproject.toml)
+sphinx-copybutton==0.5.1
+    # via sec-certs (./../pyproject.toml)
+sphinx-design==0.3.0
+    # via sec-certs (./../pyproject.toml)
+sphinxcontrib-applehelp==1.0.4
+    # via sphinx
+sphinxcontrib-devhelp==1.0.2
+    # via sphinx
+sphinxcontrib-htmlhelp==2.0.1
+    # via sphinx
+sphinxcontrib-jsmath==1.0.1
+    # via sphinx
+sphinxcontrib-qthelp==1.0.3
+    # via sphinx
+sphinxcontrib-serializinghtml==1.1.5
+    # via sphinx
+sqlalchemy==1.4.46
+    # via jupyter-cache
+srsly==2.4.6
     # via
     #   confection
     #   spacy
     #   thinc
 stack-data==0.6.2
     # via ipython
-tabula-py==2.6.0
+sympy==1.11.1
+    # via torch
+tabula-py==2.7.0
     # via sec-certs (./../pyproject.toml)
-thinc==8.1.7
+tabulate==0.9.0
+    # via jupyter-cache
+thinc==8.1.9
     # via spacy
 threadpoolctl==3.1.0
     # via scikit-learn
 tokenizers==0.13.2
     # via transformers
 tomli==2.0.1
-    # via setuptools-scm
-torch==1.13.1
+    # via
+    #   black
+    #   build
+    #   coverage
+    #   mypy
+    #   pyproject-hooks
+    #   pytest
+    #   setuptools-scm
+torch==2.0.0
     # via
     #   sentence-transformers
     #   torchvision
-torchvision==0.14.1
+    #   triton
+torchvision==0.15.1
     # via sentence-transformers
 tornado==6.2
     # via
     #   ipykernel
     #   jupyter-client
-tqdm==4.64.1
+tqdm==4.65.0
     # via
     #   datasets
     #   evaluate
@@ -400,24 +615,41 @@ traitlets==5.9.0
     #   jupyter-client
     #   jupyter-core
     #   matplotlib-inline
-transformers==4.26.1
+    #   nbclient
+    #   nbformat
+transformers==4.27.1
     # via sentence-transformers
+triton==2.0.0
+    # via torch
 typer==0.7.0
     # via
     #   pathy
     #   spacy
+types-python-dateutil==2.8.19.10
+    # via sec-certs (./../pyproject.toml)
+types-pyyaml==6.0.12.8
+    # via sec-certs (./../pyproject.toml)
+types-requests==2.28.11.15
+    # via sec-certs (./../pyproject.toml)
+types-urllib3==1.26.25.8
+    # via types-requests
 typing-extensions==4.5.0
     # via
+    #   black
     #   huggingface-hub
+    #   mypy
+    #   myst-nb
+    #   myst-parser
     #   pydantic
     #   pypdf
     #   setuptools-scm
     #   torch
-    #   torchvision
-urllib3==1.26.14
+urllib3==1.26.15
     # via
     #   requests
     #   responses
+virtualenv==20.21.0
+    # via pre-commit
 wasabi==1.1.1
     # via
     #   spacy
@@ -426,10 +658,16 @@ wcwidth==0.2.6
     # via prompt-toolkit
 webencodings==0.5.1
     # via html5lib
-wheel==0.38.4
+wheel==0.40.0
     # via
     #   nvidia-cublas-cu11
+    #   nvidia-cuda-cupti-cu11
     #   nvidia-cuda-runtime-cu11
+    #   nvidia-curand-cu11
+    #   nvidia-cusparse-cu11
+    #   nvidia-nvtx-cu11
+    #   pip-tools
+    #   pytest-monitor
 widgetsnbextension==4.0.5
     # via ipywidgets
 xxhash==3.2.0
@@ -438,10 +676,11 @@ xxhash==3.2.0
     #   evaluate
 yarl==1.8.2
     # via aiohttp
-zipp==3.13.0
+zipp==3.15.0
     # via
     #   importlib-metadata
     #   importlib-resources
 
 # The following packages are considered to be unsafe in a requirements file:
+# pip
 # setuptools
diff --git a/requirements/compile.sh b/requirements/compile.sh
index 1039a19f..c07386aa 100755
--- a/requirements/compile.sh
+++ b/requirements/compile.sh
@@ -5,4 +5,4 @@
 pip-compile --no-header --resolver=backtracking --output-file=requirements.txt ./../pyproject.toml
 pip-compile --no-header --resolver=backtracking --extra dev -o dev_requirements.txt ./../pyproject.toml
 pip-compile --no-header --resolver=backtracking --extra test -o test_requirements.txt ./../pyproject.toml
-pip-compile --no-header --resolver=backtracking --extra nlp -o nlp_requirements.txt ./../pyproject.toml
+pip-compile --no-header --resolver=backtracking --extra dev --extra test -o all_requirements.txt ./../pyproject.toml
diff --git a/requirements/dev_requirements.txt b/requirements/dev_requirements.txt
index 3acbdb0f..1d674e91 100644
--- a/requirements/dev_requirements.txt
+++ b/requirements/dev_requirements.txt
@@ -24,8 +24,6 @@ beautifulsoup4==4.11.1
     # via
     #   pydata-sphinx-theme
     #   sec-certs (./../pyproject.toml)
-billiard==4.0.2
-    # via sec-certs (./../pyproject.toml)
 black==23.1.0
     # via sec-certs (./../pyproject.toml)
 blis==0.7.9
@@ -49,9 +47,12 @@ click==8.1.3
     # via
     #   black
     #   jupyter-cache
+    #   nltk
     #   pip-tools
     #   sec-certs (./../pyproject.toml)
     #   typer
+cmake==3.26.0
+    # via triton
 comm==0.1.2
     # via ipykernel
 confection==0.0.3
@@ -68,7 +69,10 @@ cymem==2.0.7
     #   spacy
     #   thinc
 datasets==2.9.0
-    # via sec-certs (./../pyproject.toml)
+    # via
+    #   evaluate
+    #   sec-certs (./../pyproject.toml)
+    #   setfit
 debugpy==1.6.4
     # via ipykernel
 decorator==5.1.1
@@ -78,6 +82,7 @@ deprecation==2.1.0
 dill==0.3.6
     # via
     #   datasets
+    #   evaluate
     #   multiprocess
 distlib==0.3.6
     # via virtualenv
@@ -90,6 +95,8 @@ docutils==0.17.1
     #   sphinx
 entrypoints==0.4
     # via jupyter-client
+evaluate==0.4.0
+    # via setfit
 exceptiongroup==1.0.4
     # via pytest
 executing==1.2.0
@@ -99,6 +106,9 @@ fastjsonschema==2.16.2
 filelock==3.8.2
     # via
     #   huggingface-hub
+    #   torch
+    #   transformers
+    #   triton
     #   virtualenv
 fonttools==4.38.0
     # via matplotlib
@@ -107,7 +117,9 @@ frozenlist==1.3.3
     #   aiohttp
     #   aiosignal
 fsspec[http]==2023.1.0
-    # via datasets
+    # via
+    #   datasets
+    #   evaluate
 gprof2dot==2022.7.29
     # via pytest-profiling
 greenlet==2.0.1
@@ -115,7 +127,11 @@ greenlet==2.0.1
 html5lib==1.1
     # via sec-certs (./../pyproject.toml)
 huggingface-hub==0.12.1
-    # via datasets
+    # via
+    #   datasets
+    #   evaluate
+    #   sentence-transformers
+    #   transformers
 identify==2.5.9
     # via pre-commit
 idna==3.4
@@ -153,8 +169,11 @@ jinja2==3.1.2
     #   myst-parser
     #   spacy
     #   sphinx
+    #   torch
 joblib==1.2.0
-    # via scikit-learn
+    # via
+    #   nltk
+    #   scikit-learn
 jsonschema==4.17.3
     # via
     #   nbformat
@@ -175,6 +194,10 @@ kiwisolver==1.4.4
     # via matplotlib
 langcodes==3.3.0
     # via spacy
+langdetect==1.0.9
+    # via sec-certs (./../pyproject.toml)
+lit==15.0.7
+    # via triton
 lxml==4.9.1
     # via
     #   pikepdf
@@ -200,12 +223,16 @@ mdurl==0.1.2
     # via markdown-it-py
 memory-profiler==0.61.0
     # via pytest-monitor
+mpmath==1.3.0
+    # via sympy
 multidict==6.0.4
     # via
     #   aiohttp
     #   yarl
 multiprocess==0.70.14
-    # via datasets
+    # via
+    #   datasets
+    #   evaluate
 murmurhash==1.0.9
     # via
     #   preshed
@@ -236,7 +263,11 @@ nest-asyncio==1.5.6
     #   jupyter-client
     #   nbclient
 networkx==2.8.8
-    # via sec-certs (./../pyproject.toml)
+    # via
+    #   sec-certs (./../pyproject.toml)
+    #   torch
+nltk==3.8.1
+    # via sentence-transformers
 nodeenv==1.7.0
     # via pre-commit
 numpy==1.23.5
@@ -244,6 +275,7 @@ numpy==1.23.5
     #   blis
     #   contourpy
     #   datasets
+    #   evaluate
     #   matplotlib
     #   pandas
     #   pyarrow
@@ -252,15 +284,44 @@ numpy==1.23.5
     #   scipy
     #   seaborn
     #   sec-certs (./../pyproject.toml)
+    #   sentence-transformers
     #   spacy
     #   tabula-py
     #   thinc
+    #   torchvision
+    #   transformers
+nvidia-cublas-cu11==11.10.3.66
+    # via
+    #   nvidia-cudnn-cu11
+    #   nvidia-cusolver-cu11
+    #   torch
+nvidia-cuda-cupti-cu11==11.7.101
+    # via torch
+nvidia-cuda-nvrtc-cu11==11.7.99
+    # via torch
+nvidia-cuda-runtime-cu11==11.7.99
+    # via torch
+nvidia-cudnn-cu11==8.5.0.96
+    # via torch
+nvidia-cufft-cu11==10.9.0.58
+    # via torch
+nvidia-curand-cu11==10.2.10.91
+    # via torch
+nvidia-cusolver-cu11==11.4.0.1
+    # via torch
+nvidia-cusparse-cu11==11.7.4.91
+    # via torch
+nvidia-nccl-cu11==2.14.3
+    # via torch
+nvidia-nvtx-cu11==11.7.91
+    # via torch
 packaging==22.0
     # via
     #   black
     #   build
     #   datasets
     #   deprecation
+    #   evaluate
     #   huggingface-hub
     #   ipykernel
     #   matplotlib
@@ -270,9 +331,11 @@ packaging==22.0
     #   setuptools-scm
     #   spacy
     #   sphinx
+    #   transformers
 pandas==1.5.2
     # via
     #   datasets
+    #   evaluate
     #   pysankeybeta
     #   seaborn
     #   sec-certs (./../pyproject.toml)
@@ -298,6 +361,7 @@ pillow==9.3.0
     #   matplotlib
     #   pikepdf
     #   sec-certs (./../pyproject.toml)
+    #   torchvision
 pip-tools==6.11.0
     # via sec-certs (./../pyproject.toml)
 pkgconfig==1.5.5
@@ -383,15 +447,21 @@ pyyaml==6.0
     #   pre-commit
     #   sec-certs (./../pyproject.toml)
     #   sphinx-book-theme
+    #   transformers
 pyzmq==24.0.1
     # via
     #   ipykernel
     #   jupyter-client
 rapidfuzz==2.13.3
     # via sec-certs (./../pyproject.toml)
+regex==2022.10.31
+    # via
+    #   nltk
+    #   transformers
 requests==2.28.1
     # via
     #   datasets
+    #   evaluate
     #   fsspec
     #   huggingface-hub
     #   pytest-monitor
@@ -399,26 +469,40 @@ requests==2.28.1
     #   sec-certs (./../pyproject.toml)
     #   spacy
     #   sphinx
+    #   torchvision
+    #   transformers
 responses==0.18.0
-    # via datasets
+    # via
+    #   datasets
+    #   evaluate
 ruff==0.0.239
     # via sec-certs (./../pyproject.toml)
 scikit-learn==1.2.0
-    # via sec-certs (./../pyproject.toml)
+    # via
+    #   sec-certs (./../pyproject.toml)
+    #   sentence-transformers
 scipy==1.9.3
     # via
     #   scikit-learn
     #   sec-certs (./../pyproject.toml)
+    #   sentence-transformers
 seaborn==0.12.1
     # via
     #   pysankeybeta
     #   sec-certs (./../pyproject.toml)
+sentence-transformers==2.2.2
+    # via setfit
+sentencepiece==0.1.97
+    # via sentence-transformers
+setfit==0.6.0
+    # via sec-certs (./../pyproject.toml)
 setuptools-scm==7.0.5
     # via sec-certs (./../pyproject.toml)
 six==1.16.0
     # via
     #   asttokens
     #   html5lib
+    #   langdetect
     #   pytest-profiling
     #   python-dateutil
 smart-open==6.2.0
@@ -469,6 +553,8 @@ srsly==2.4.5
     #   thinc
 stack-data==0.6.2
     # via ipython
+sympy==1.11.1
+    # via torch
 tabula-py==2.6.0
     # via sec-certs (./../pyproject.toml)
 tabulate==0.9.0
@@ -477,6 +563,8 @@ thinc==8.1.5
     # via spacy
 threadpoolctl==3.1.0
     # via scikit-learn
+tokenizers==0.13.2
+    # via transformers
 toml==0.10.2
     # via pre-commit
 tomli==2.0.1
@@ -488,6 +576,13 @@ tomli==2.0.1
     #   pep517
     #   pytest
     #   setuptools-scm
+torch==2.0.0
+    # via
+    #   sentence-transformers
+    #   torchvision
+    #   triton
+torchvision==0.15.1
+    # via sentence-transformers
 tornado==6.2
     # via
     #   ipykernel
@@ -495,9 +590,13 @@ tornado==6.2
 tqdm==4.64.1
     # via
     #   datasets
+    #   evaluate
     #   huggingface-hub
+    #   nltk
     #   sec-certs (./../pyproject.toml)
+    #   sentence-transformers
     #   spacy
+    #   transformers
 traitlets==5.6.0
     # via
     #   comm
@@ -509,6 +608,10 @@ traitlets==5.6.0
     #   matplotlib-inline
     #   nbclient
     #   nbformat
+transformers==4.27.1
+    # via sentence-transformers
+triton==2.0.0
+    # via torch
 typer==0.7.0
     # via
     #   pathy
@@ -531,6 +634,7 @@ typing-extensions==4.4.0
     #   pydantic
     #   pypdf
     #   setuptools-scm
+    #   torch
 urllib3==1.26.13
     # via
     #   requests
@@ -547,12 +651,20 @@ webencodings==0.5.1
     # via html5lib
 wheel==0.38.4
     # via
+    #   nvidia-cublas-cu11
+    #   nvidia-cuda-cupti-cu11
+    #   nvidia-cuda-runtime-cu11
+    #   nvidia-curand-cu11
+    #   nvidia-cusparse-cu11
+    #   nvidia-nvtx-cu11
     #   pip-tools
     #   pytest-monitor
 widgetsnbextension==4.0.4
     # via ipywidgets
 xxhash==3.2.0
-    # via datasets
+    # via
+    #   datasets
+    #   evaluate
 yarl==1.8.2
     # via aiohttp
 zipp==3.11.0
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9461aaa2..f1263fc5 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,8 +6,6 @@ backcall==0.2.0
     # via ipython
 beautifulsoup4==4.11.1
     # via sec-certs (./../pyproject.toml)
-billiard==4.0.2
-    # via sec-certs (./../pyproject.toml)
 blis==0.7.9
     # via thinc
 catalogue==2.0.8
diff --git a/requirements/test_requirements.txt b/requirements/test_requirements.txt
index c2d69151..4f18443d 100644
--- a/requirements/test_requirements.txt
+++ b/requirements/test_requirements.txt
@@ -8,8 +8,6 @@ backcall==0.2.0
     # via ipython
 beautifulsoup4==4.11.1
     # via sec-certs (./../pyproject.toml)
-billiard==4.0.2
-    # via sec-certs (./../pyproject.toml)
 blis==0.7.9
     # via thinc
 catalogue==2.0.8
diff --git a/src/sec_certs/config/settings-schema.json b/src/sec_certs/config/settings-schema.json
index c05df4d4..32358b03 100644
--- a/src/sec_certs/config/settings-schema.json
+++ b/src/sec_certs/config/settings-schema.json
@@ -189,7 +189,7 @@
             ]
         },
         "cc_reference_annotator_should_train": {
-            "$ref": "#/definitions/settings/boolean_entry"
+            "$ref": "#/definitions/settings_boolean_entry"
         }
     },
     "required": [
diff --git a/src/sec_certs/config/settings.yaml b/src/sec_certs/config/settings.yaml
index 154d5dfe..dea18eed 100644
--- a/src/sec_certs/config/settings.yaml
+++ b/src/sec_certs/config/settings.yaml
@@ -14,7 +14,7 @@ year_difference_between_validations:
   value: 7
 n_threads:
   description: How many threads to use for parallel computations. Set to -1 to use all cores (*2 with multithreading).
-  value: -1
+  value: 200
 cpe_matching_threshold:
   description: Level of required string similarity between CPE and certificate name on CC CPE matching, 0-100. Lower values yield more false negatives, higher values more false positives
   value: 92
@@ -62,4 +62,4 @@ cc_reference_annotator_dir:
   value: null
 cc_reference_annotator_should_train:
   description: True if new reference annotator model shall be build, False otherwise
-  value: false
+  value: true
diff --git a/src/sec_certs/dataset/cc.py b/src/sec_certs/dataset/cc.py
index b70f992b..4c1b0f22 100644
--- a/src/sec_certs/dataset/cc.py
+++ b/src/sec_certs/dataset/cc.py
@@ -8,7 +8,7 @@ import tempfile
 from dataclasses import dataclass
 from datetime import datetime
 from pathlib import Path
-from typing import ClassVar, Iterator
+from typing import ClassVar, Iterator, Literal
 
 import numpy as np
 import pandas as pd
@@ -812,7 +812,14 @@ class CCDataset(Dataset[CCCertificate, CCAuxiliaryDatasets], ComplexSerializable
 
         return update_dset
 
-    def annotate_references(self):
+    def annotate_references(self, mode: Literal["training", "production"] = "production"):
+        """
+        Fills in `cert.heuristics.annotated_references` with reference labels.
+        This requires (a) a pre-trained `ReferenceAnnotator` or (b) to train a `ReferenceAnnotator`.
+        The behaviour is controlled by config keys `config.cc_reference_annotator_should_train` and
+        `config.cc_reference_annotator_path`. If should_train is False and path is provided, a pre-trained annotator
+        will be loaded and used.
+        """
         if not self.state.pdfs_converted:
             logger.info(
                 "Attempting run analysis of txt files while not having the pdf->txt conversion done. Returning."
@@ -831,24 +838,39 @@ class CCDataset(Dataset[CCCertificate, CCAuxiliaryDatasets], ComplexSerializable
                 annotator = ReferenceAnnotator.from_pretrained(model_dir)
             except Exception:
                 logger.error(
-                    f"annotate_references() method was called with `config.cc_reference_annotator_should_train=False`. Further, the model was not found either at `config.cc_reference_annotator_dir={config.cc_reference_annotator_dir}` nor at {self.reference_annotator_dir}. Either: (a) allow training with `config.cc_reference_annotator_should_train=False`; (b) set path to model with `config.cc_reference_annotator_path`; (c) paste the model into {self.reference_annotator_dir}. Returning."
+                    "annotate_references() method was called with `config.cc_reference_annotator_should_train=False`."
+                    f"Further, the model was not found either at `config.cc_reference_annotator_dir={config.cc_reference_annotator_dir}`"
+                    f"nor at {self.reference_annotator_dir}. Either: (a) allow training with `config.cc_reference_annotator_should_train=True`;"
+                    "(b) set path to model with `config.cc_reference_annotator_path`; (c) paste the model into {self.reference_annotator_dir}. Returning."
                 )
+                return
 
+        logger.info("Extracting segments of text relevant for reference annotations.")
         df = ReferenceSegmentExtractor().prepare_df_from_cc_certs(list(self.certs.values()))
         if config.cc_reference_annotator_should_train:
-            annotator = self._train_reference_annotator(df)
+            annotator = self._train_reference_annotator(df, mode=mode)
 
+        logger.info("Predicting reference labels")
         df = annotator.predict_df(df)
+        refs: dict[str, dict[str, str]] = dict.fromkeys(df.dgst, {})
+        for dgst, cert_id, label in zip(df.dgst, df.referenced_cert_id, df.y_pred):
+            refs[dgst][cert_id] = label
 
-        # TODO: Now iterate over DF, fill-in references
+        for dgst, value in refs.items():
+            self[dgst].heuristics.annotated_references = value
 
-    def _train_reference_annotator(self, df: pd.DataFrame, save_model: bool = True) -> ReferenceAnnotator:
-        trainer = ReferenceAnnotatorTrainer.from_df(df, prec_recall_metric, "transformer", "production")
+    def _train_reference_annotator(
+        self, df: pd.DataFrame, save_model: bool = True, mode: Literal["training", "production"] = "production"
+    ) -> ReferenceAnnotator:
+        trainer = ReferenceAnnotatorTrainer.from_df(df, prec_recall_metric, "transformer", mode)
+        logger.info(
+            "Training ReferenceAnnotator on {df.shape[0]} samples ({df.loc[df.split == 'train'].shape[0]}/{df.loc[df.split == 'valid'].shape[0]}/{df.loc[df.split == 'test'].shape[0]}) (train/valid/test)."
+        )
         trainer.train()
         logger.info(trainer.evaluate())
 
         if save_model:
-            trainer.slf.save_pretrained(self.reference_annotator_dir)
+            trainer.clf.save_pretrained(self.reference_annotator_dir)
 
         return trainer.clf
 
diff --git a/src/sec_certs/model/references/annotator.py b/src/sec_certs/model/references/annotator.py
index e64a7221..0b971a8b 100644
--- a/src/sec_certs/model/references/annotator.py
+++ b/src/sec_certs/model/references/annotator.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import json
+import logging
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Any
@@ -11,6 +12,8 @@ from setfit import SetFitModel
 
 from sec_certs.utils.nlp import softmax
 
+logger = logging.getLogger(__name__)
+
 
 @dataclass
 class ReferenceAnnotator:
@@ -32,6 +35,7 @@ class ReferenceAnnotator:
         :param str | Path model_dir: path to directory to search for model and label mapping
         :return RerefenceClassifier: classifier with SetFitModel and label mapping
         """
+        logger.info(f"Loading pre-trained reference annotator from: {model_dir}")
         model = SetFitModel.from_pretrained(str(model_dir))
         with (Path(model_dir) / "label_mapping.json").open("r") as handle:
             label_mapping = json.load(handle)
@@ -43,12 +47,13 @@ class ReferenceAnnotator:
         """
         Will dump _model and _label_mapping into a directory.
         """
+        logger.info(f"Saving ReferenceAnnotator to {model_dir}")
         model_dir = Path(model_dir)
         model_dir.mkdir(exist_ok=True, parents=True)
-
+        logger.info
         with (model_dir / "label_mapping.json").open("w") as handle:
             json.dump(self._label_mapping, handle, indent=4)
-        self._model.save_pretrained(str(model_dir))
+        self._model._save_pretrained(str(model_dir))
 
     def train(self, train_dataset: pd.DataFrame):
         raise NotImplementedError("ReferenceAnnotatorTrainer shall be used for training")
@@ -64,11 +69,12 @@ class ReferenceAnnotator:
 
     def _predict_proba_single(self, sample: list[str]) -> list[float]:
         """
-        1. Get predictions for each segment
+        1. Get predictions for each segment, convert pytorch tensor to numpy
         2. Square every prediction to reward confidence
         3. Sum probabilities for each label
+        4. softmax
         """
-        return softmax(np.power(self._model.predict_proba(sample), 2).sum(axis=0))
+        return softmax(np.power(self._model.predict_proba(sample, as_numpy=True), 2).sum(axis=0))
 
     def predict_df(self, df: pd.DataFrame) -> pd.DataFrame:
         """
@@ -78,5 +84,7 @@ class ReferenceAnnotator:
         y_proba = self.predict_proba(df.segments)
         df_new["y_proba"] = y_proba
         df_new["y_pred"] = df_new.y_proba.map(lambda x: self._label_mapping[int(np.argmax(x))])
-        df_new["correct"] = df_new.label == df_new.y_pred
+        df_new["correct"] = df_new.apply(
+            lambda row: row["y_pred"] == row["label"] if not pd.isnull(row["label"]) else np.NaN, axis=1
+        )
         return df_new
diff --git a/src/sec_certs/model/references/annotator_trainer.py b/src/sec_certs/model/references/annotator_trainer.py
index b11a7c5f..40f18dbc 100644
--- a/src/sec_certs/model/references/annotator_trainer.py
+++ b/src/sec_certs/model/references/annotator_trainer.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import logging
 from typing import Callable, Literal
 
 import pandas as pd
@@ -10,32 +11,7 @@ from setfit import SetFitModel, SetFitTrainer
 from sec_certs.model.references.annotator import ReferenceAnnotator
 from sec_certs.utils.nlp import prepare_reference_annotations_df
 
-"""
-Production workflow:
-
-df = ReferenceClassifierTrainer.prepare_df_from_cc_dataset([cert for cert in cc_dset])
-trainer = ReferenceClassifierTrainer.from_df(precision_recall, df, "transformer", "production")
-trainer.train()
-trainer.evaluate()
-
-# Print how great we are on test set
-
-trainer.clf.save_pretrained(/some/directory)
-
-... 48 hours later
-
-cc_dset.annotate_references(/path/to/model)
-
-where
-
-def annotate_references(self, model_directory):
-    clf = ReferenceClassifier.from_pretrained(model_directory)
-    df = ReferenceClassifierTrainer.prepare_df_from_cc_dataset([x for x in self])
-    df = clf.predict_df(df)
-
-    Now iterate over df, take each pair (dgst, referenced_cert_id) and fill_in dictionary in self[dgst].heuristics.references ...
-
-"""
+logger = logging.getLogger(__name__)
 
 
 class ReferenceAnnotatorTrainer:
@@ -76,6 +52,8 @@ class ReferenceAnnotatorTrainer:
     @staticmethod
     def split_df_for_production(df: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
         df.split = df.split.map({"test": "test", "train": "train", "valid": "train"})
+        if df.loc[df.split == "test"].empty:
+            logger.warning("`test` split for annotator dataset is empty -> model can be trained, but not evaluated.")
         return df.loc[df.split == "train"].drop(columns="split"), df.loc[df.split == "test"].drop(columns="split")
 
     def _init_trainer(self, method: Literal["transformer", "baseline"]):
@@ -136,6 +114,9 @@ class ReferenceAnnotatorTrainer:
         print(self._evaluate_stacked())
 
     def _evaluate_raw(self):
+        if self._eval_dataset.empty:
+            logger.error("Evaluation dataset is empty, cannot evaluate, returning.")
+            return
         return self._trainer.evaluate()
 
     def _evaluate_stacked(self):
diff --git a/src/sec_certs/model/references/segment_extractor.py b/src/sec_certs/model/references/segment_extractor.py
index 23ff4c1d..df377fec 100644
--- a/src/sec_certs/model/references/segment_extractor.py
+++ b/src/sec_certs/model/references/segment_extractor.py
@@ -22,7 +22,9 @@ class ReferenceRecord:
     Data structure to hold objects when extracting text segments from txt files relevant for reference annotations.
     """
 
-    certificate: CCCertificate
+    certificate_dgst: str
+    certificate_st_path: Path
+    certificate_report_path: Path
     referenced_cert_id: str
     source: str
     segments: set[str] | None = None
@@ -33,12 +35,7 @@ class ReferenceRecord:
         Open file, read text and extract sentences with `referenced_cert_id` match.
         Static method to allow for parallelization
         """
-        pth_to_read = (
-            record.certificate.state.st_txt_path
-            if record.source == "target"
-            else record.certificate.state.report_txt_path
-        )
-
+        pth_to_read = record.certificate_st_path if record.source == "target" else record.certificate_report_path
         with pth_to_read.open("r") as handle:
             data = handle.read()
 
@@ -50,7 +47,7 @@ class ReferenceRecord:
         return record
 
     def to_pandas_tuple(self) -> tuple[str, str, str, set[str] | None]:
-        return self.certificate.dgst, self.referenced_cert_id, self.source, self.segments
+        return self.certificate_dgst, self.referenced_cert_id, self.source, self.segments
 
 
 class ReferenceSegmentExtractor:
@@ -77,12 +74,12 @@ class ReferenceSegmentExtractor:
         ]
         df_targets = self._build_df(target_certs, "target")
         df_reports = self._build_df(report_certs, "report")
-        return self._process_df(pd.concat([df_targets, df_reports]))
+        return ReferenceSegmentExtractor._process_df(pd.concat([df_targets, df_reports]))
 
     def _build_df(self, certs: list[CCCertificate], source: Literal["target", "report"]) -> pd.DataFrame:
         attribute_mapping = {"target": "st_references", "report": "report_references"}
         records = [
-            ReferenceRecord(x, y, source)
+            ReferenceRecord(x.dgst, x.state.st_txt_path, x.state.report_txt_path, y, source)
             for x in certs
             for y in getattr(x.heuristics, attribute_mapping[source]).directly_referencing
         ]
@@ -100,7 +97,8 @@ class ReferenceSegmentExtractor:
             columns=["dgst", "referenced_cert_id", "source", "segments"],
         )
 
-    def _get_split_dict(self) -> dict[str, str]:
+    @staticmethod
+    def _get_split_dict() -> dict[str, str]:
         """
         Returns dictionary that maps dgst: split, where split in `train`, `valid`, `test`
         """
@@ -116,7 +114,8 @@ class ReferenceSegmentExtractor:
             **get_single_dct(split_directory / "test.json", "test"),
         }
 
-    def _get_annotations_dict(self) -> dict[tuple[str, str], str]:
+    @staticmethod
+    def _get_annotations_dict() -> dict[tuple[str, str], str]:
         """
         Returns dictionary mapping tuples `(dgst, referenced_cert_id) -> label`
         """
@@ -142,12 +141,13 @@ class ReferenceSegmentExtractor:
             df_annot[["dgst", "referenced_cert_id", "label"]].set_index(["dgst", "referenced_cert_id"]).label.to_dict()
         )
 
-    def _process_df(self, df: pd.DataFrame) -> pd.DataFrame:
+    @staticmethod
+    def _process_df(df: pd.DataFrame) -> pd.DataFrame:
         """
         Fully processes the dataframe.
         """
-        annotations_dict = self._get_annotations_dict()
-        split_dct = self._get_split_dict()
+        annotations_dict = ReferenceSegmentExtractor._get_annotations_dict()
+        split_dct = ReferenceSegmentExtractor._get_split_dict()
 
         return (
             df.loc[df.segments.notnull()]
diff --git a/src/sec_certs/sample/cc.py b/src/sec_certs/sample/cc.py
index 771a0eab..29498265 100644
--- a/src/sec_certs/sample/cc.py
+++ b/src/sec_certs/sample/cc.py
@@ -44,15 +44,6 @@ class ReferenceType(Enum):
     INDIRECT = "indirect"
 
 
-class ReferenceMeaning(Enum):
-    ON_PLATFORM = "on_platform"
-    COMPONENT_USED = "component_used"
-    PREVIOUS_VERSION = "previous_version"
-    EVALUATION_REUSED = "evaluation_reused"
-    COMPONENT_SHARED = "component_shared"
-    RECERTIFICATION = "recertification"
-
-
 class CCCertificate(
     Certificate["CCCertificate", "CCCertificate.Heuristics", "CCCertificate.PdfData"],
     PandasSerializableType,
@@ -445,7 +436,8 @@ class CCCertificate(
         report_references: References = field(default_factory=References)
 
         # Contains direct outward references merged from both st, and report sources, annotated with ReferenceAnnotator
-        annotated_references: dict[str, ReferenceMeaning | None] | None = field(default=None)
+        # TODO: Reference meanings as Enum if we work with it further.
+        annotated_references: dict[str, str] | None = field(default=None)
         extracted_sars: set[SAR] | None = field(default=None)
         direct_transitive_cves: set[str] | None = field(default=None)
         indirect_transitive_cves: set[str] | None = field(default=None)
diff --git a/src/sec_certs/utils/nlp.py b/src/sec_certs/utils/nlp.py
index 45ad9639..dd114662 100644
--- a/src/sec_certs/utils/nlp.py
+++ b/src/sec_certs/utils/nlp.py
@@ -18,8 +18,8 @@ def softmax(x):
     return np.exp(x - np.max(x)) / np.exp(x - np.max(x)).sum()
 
 
-def eval_strings(series):
-    return [list(literal_eval(x)) for x in series]
+def eval_strings_if_necessary(series: pd.Series) -> pd.Series:
+    return series.map(literal_eval) if isinstance(series.iloc[0], str) else series
 
 
 def filter_short_sentences(sentences, cert_id):
@@ -29,7 +29,7 @@ def filter_short_sentences(sentences, cert_id):
 def prepare_reference_annotations_df(df: pd.DataFrame):
     df = (
         df.loc[lambda df_: (df_.label != "SELF") & (df_.label.notnull())]
-        .assign(segments=lambda df_: eval_strings(df_.segments))
+        .assign(segments=lambda df_: eval_strings_if_necessary(df_.segments))
         .drop(columns="lang")
     )
     df.segments = df.apply(lambda row: filter_short_sentences(row["segments"], row["referenced_cert_id"]), axis=1)
diff --git a/src/sec_certs/utils/parallel_processing.py b/src/sec_certs/utils/parallel_processing.py
index b3016695..4c6af63b 100644
--- a/src/sec_certs/utils/parallel_processing.py
+++ b/src/sec_certs/utils/parallel_processing.py
@@ -2,11 +2,9 @@ from __future__ import annotations
 
 import time
 from multiprocessing import cpu_count
-from multiprocessing.pool import ThreadPool
+from multiprocessing.pool import Pool, ThreadPool
 from typing import Any, Callable, Iterable
 
-from billiard.pool import Pool
-
 from sec_certs.config.configuration import config
 from sec_certs.utils.tqdm import tqdm
 
diff --git a/tests/data/cc/analysis/cc_full_dataset.json b/tests/data/cc/analysis/cc_full_dataset.json
index 05e75c55..3b07f9c5 100644
--- a/tests/data/cc/analysis/cc_full_dataset.json
+++ b/tests/data/cc/analysis/cc_full_dataset.json
@@ -551,6 +551,7 @@
                     "directly_referencing": null,
                     "indirectly_referencing": null
                 },
+                "annotated_references": null,
                 "extracted_sars": {
                     "_type": "Set",
                     "elements": [
@@ -726,4 +727,4 @@
             }
         }
     ]
-}
-\ No newline at end of file
+}
diff --git a/tests/data/cc/analysis/reference_dataset.json b/tests/data/cc/analysis/reference_dataset.json
index 57ae81f1..00ab6674 100644
--- a/tests/data/cc/analysis/reference_dataset.json
+++ b/tests/data/cc/analysis/reference_dataset.json
@@ -508,6 +508,7 @@
             ]
           }
         },
+        "annotated_references": null,
         "extracted_sars": {
           "_type": "Set",
           "elements": [
@@ -1105,6 +1106,7 @@
             ]
           }
         },
+        "annotated_references": null,
         "extracted_sars": {
           "_type": "Set",
           "elements": [
@@ -1736,6 +1738,7 @@
             ]
           }
         },
+        "annotated_references": null,
         "extracted_sars": {
           "_type": "Set",
           "elements": [
@@ -1841,4 +1844,4 @@
       }
     }
   ]
-}
-\ No newline at end of file
+}
diff --git a/tests/data/cc/analysis/transitive_vulnerability_dataset.json b/tests/data/cc/analysis/transitive_vulnerability_dataset.json
index da2348bf..fb45efde 100644
--- a/tests/data/cc/analysis/transitive_vulnerability_dataset.json
+++ b/tests/data/cc/analysis/transitive_vulnerability_dataset.json
@@ -1144,6 +1144,7 @@
             ]
           }
         },
+        "annotated_references": null,
         "extracted_sars": {
           "_type": "Set",
           "elements": [
@@ -2084,6 +2085,7 @@
             ]
           }
         },
+        "annotated_references": null,
         "extracted_sars": {
           "_type": "Set",
           "elements": [
@@ -3436,6 +3438,7 @@
             ]
           }
         },
+        "annotated_references": null,
         "extracted_sars": {
           "_type": "Set",
           "elements": [
@@ -3611,4 +3614,4 @@
       }
     }
   ]
-}
-\ No newline at end of file
+}
diff --git a/tests/data/cc/analysis/vulnerable_dataset.json b/tests/data/cc/analysis/vulnerable_dataset.json
index 1afb42bb..1b23ef0d 100644
--- a/tests/data/cc/analysis/vulnerable_dataset.json
+++ b/tests/data/cc/analysis/vulnerable_dataset.json
@@ -63,6 +63,7 @@
                     "8.2"
                 ],
                 "cpe_matches": null,
+                "annotated_references": null,
                 "verified_cpe_matches": null,
                 "related_cves": null,
                 "cert_lab": null,
@@ -118,6 +119,7 @@
                     "8.2"
                 ],
                 "cpe_matches": null,
+                "annotated_references": null,
                 "verified_cpe_matches": null,
                 "related_cves": null,
                 "cert_lab": null,
@@ -125,4 +127,4 @@
             }
         }
     ]
-}
-\ No newline at end of file
+}
diff --git a/tests/data/cc/certificate/fictional_cert.json b/tests/data/cc/certificate/fictional_cert.json
index 5d5a0499..a28665ea 100644
--- a/tests/data/cc/certificate/fictional_cert.json
+++ b/tests/data/cc/certificate/fictional_cert.json
@@ -73,6 +73,7 @@
         "related_cves": null,
         "cert_lab": null,
         "cert_id": null,
+        "annotated_references": null,
         "extracted_sars": null,
         "direct_transitive_cves": null,
         "indirect_transitive_cves": null,
@@ -94,4 +95,4 @@
     "report_link": "https://path.to/report/link",
     "st_link": "https://path.to/st/link",
     "cert_link": "https://path.to/cert/link"
-}
-\ No newline at end of file
+}
diff --git a/tests/data/cc/dataset/auxiliary_datasets/maintenances/maintenance_updates.json b/tests/data/cc/dataset/auxiliary_datasets/maintenances/maintenance_updates.json
index fd21471b..122a2845 100644
--- a/tests/data/cc/dataset/auxiliary_datasets/maintenances/maintenance_updates.json
+++ b/tests/data/cc/dataset/auxiliary_datasets/maintenances/maintenance_updates.json
@@ -68,6 +68,7 @@
                     "directly_referencing": null,
                     "indirectly_referencing": null
                 },
+                "annotated_references": null,
                 "extracted_sars": null,
                 "direct_transitive_cves": null,
                 "indirect_transitive_cves": null
@@ -76,4 +77,4 @@
             "maintenance_date": "2019-08-26"
         }
     ]
-}
-\ No newline at end of file
+}
diff --git a/tests/data/cc/dataset/toy_dataset.json b/tests/data/cc/dataset/toy_dataset.json
index dec802f7..22b8d192 100644
--- a/tests/data/cc/dataset/toy_dataset.json
+++ b/tests/data/cc/dataset/toy_dataset.json
@@ -77,6 +77,7 @@
                 "related_cves": null,
                 "cert_lab": null,
                 "cert_id": null,
+                "annotated_references": null,
                 "extracted_sars": null,
                 "direct_transitive_cves": null,
                 "indirect_transitive_cves": null,
@@ -164,6 +165,7 @@
                 "related_cves": null,
                 "cert_lab": null,
                 "cert_id": null,
+                "annotated_references": null,
                 "extracted_sars": null,
                 "direct_transitive_cves": null,
                 "indirect_transitive_cves": null,
@@ -259,6 +261,7 @@
                 "related_cves": null,
                 "cert_lab": null,
                 "cert_id": null,
+                "annotated_references": null,
                 "extracted_sars": null,
                 "direct_transitive_cves": null,
                 "indirect_transitive_cves": null,
@@ -279,4 +282,4 @@
             }
         }
     ]
-}
-\ No newline at end of file
+}
author	adamjanovsky	2023-03-17 14:40:20 +0100
committer	adamjanovsky	2023-03-17 14:40:20 +0100
commit	a6b008b167db388476e5dfb5990631e207a764d6 (patch)
tree	e71e4bbf513c8842ffcbeccc07abfed3162a73a9
parent	731b3ea9c118a374aa1c1194305741b6e7909e83 (diff)
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