diff --git a/ms2query/notebooks/tutorial.ipynb b/ms2query/notebooks/tutorial.ipynb
index 411561f..7afc892 100644
--- a/ms2query/notebooks/tutorial.ipynb
+++ b/ms2query/notebooks/tutorial.ipynb
@@ -10,29 +10,18 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d564033",
+   "id": "3fdc846e",
    "metadata": {},
    "source": [
-    "# optional: download a matchms cleaned library\n",
-    "The code below downloads an already matchms cleaned library and an MS2DeepScore model. You can also use your own library, but make sure you know what you are doing and clean the library first. If you just have a few reference spectra, it is probably best to combine your spectra with the reference spectra below to make sure the MS2Query search works properly. "
+    "### Download already created library"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "b7df418e",
+   "execution_count": 57,
+   "id": "5ce3469d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The file ./zenodo_files\\data_split_inchikeys.json already exists, the file won't be downloaded\n",
-      "The file ./zenodo_files\\merged_and_cleaned_libraries_1.mgf already exists, the file won't be downloaded\n",
-      "The file ./zenodo_files\\ms2deepscore_model.pt already exists, the file won't be downloaded\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import requests\n",
     "import os\n",
@@ -50,34 +39,451 @@
     "            if chunk:\n",
     "                f.write(chunk)\n",
     "                bar.update(len(chunk))  # Update progress bar by the chunk size\n",
-    "folder_to_store_zenodo_files = \"./zenodo_files\"\n",
-    "os.makedirs(folder_to_store_zenodo_files, exist_ok=True)\n",
+    "folder_to_store_zenodo_files = \"./zenodo_files/full_library\"\n",
+    "os.makedirs(folder_to_store_zenodo_files, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "125332e2-fb26-45a4-b649-b84a7ed88181",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The file ./zenodo_files/full_library/embeddings.npz already exists, the file won't be downloaded\n",
+      "The file ./zenodo_files/full_library/library_metadata.parquet already exists, the file won't be downloaded\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The file ./zenodo_files/full_library/top_k_tanimoto_scores.parquet already exists, the file won't be downloaded\n"
+     ]
+    }
+   ],
+   "source": [
+    "download_file(\"https://zenodo.org/records/19206616/files/embeddings.npz?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"embeddings.npz\"))\n",
+    "download_file(\"https://zenodo.org/records/19206616/files/library_metadata.parquet?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"library_metadata.parquet\"))\n",
+    "download_file(\"https://zenodo.org/records/19206616/files/ms2deepscore_model?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"))\n",
+    "download_file(\"https://zenodo.org/records/19206616/files/top_k_tanimoto_scores.parquet?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"top_k_tanimoto_scores.parquet\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa436a42-d850-4ce2-86c5-741646c8b168",
+   "metadata": {},
+   "source": [
+    "# initialize reference library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "8b477da9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ms2query.ms2query_development.ReferenceLibrary import ReferenceLibrary\n",
+    "from pathlib import Path\n",
+    "reference_library = ReferenceLibrary.load_from_files(os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"),\n",
+    "                                                    os.path.join(folder_to_store_zenodo_files, \"embeddings.npz\"),\n",
+    "                                                    os.path.join(folder_to_store_zenodo_files, \"top_k_tanimoto_scores.parquet\"),\n",
+    "                                                    os.path.join(folder_to_store_zenodo_files, \"library_metadata.parquet\"))\n",
     "\n",
+    "# Or directly find the files from a directory\n",
+    "# reference_library = ReferenceLibrary.load_from_directory(Path(folder_to_store_zenodo_files))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b637cbac-c79b-4df3-8ad9-0db400a32fb4",
+   "metadata": {},
+   "source": [
+    "# Run MS2Query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "ae96dd88-c8c0-4be0-9355-b29a8f5ce37b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "10it [00:00, 83.01it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_spectra = list(tqdm(load_from_mgf(\"../../tests/test_data/10_spectra.mgf\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "ec952eaa-102f-4d86-8fbb-b91973f395a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Hashing spe\n",
+      "Computing s\n",
+      "Predicting \n",
+      "/lustre/BIF/nobackup/jonge094/ms2query_2/ms2query/ms2query_development/ReferenceLibrary.py:168: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  # construct results df\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>precursor_mz</th>\n",
+       "      <th>retention_time</th>\n",
+       "      <th>collision_energy</th>\n",
+       "      <th>compound_name</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>inchikey</th>\n",
+       "      <th>spectrum_hashes</th>\n",
+       "      <th>ms2query_reliability_prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>29144</th>\n",
+       "      <td>181.06100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Nicotinuric acid</td>\n",
+       "      <td>O=C(O)CNC(=O)c1cccnc1</td>\n",
+       "      <td>ZBSGKPYXQINNGF-UHFFFAOYSA-N</td>\n",
+       "      <td>4538586676799711060475687385406365205312522933...</td>\n",
+       "      <td>0.524193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29138</th>\n",
+       "      <td>181.06100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Nicotinuric acid</td>\n",
+       "      <td>O=C(O)CNC(=O)c1cccnc1</td>\n",
+       "      <td>ZBSGKPYXQINNGF-UHFFFAOYSA-N</td>\n",
+       "      <td>4691175720354550216218758199469384349519880571...</td>\n",
+       "      <td>0.477547</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29139</th>\n",
+       "      <td>181.06100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Nicotinuric acid</td>\n",
+       "      <td>O=C(O)CNC(=O)c1cccnc1</td>\n",
+       "      <td>ZBSGKPYXQINNGF-UHFFFAOYSA-N</td>\n",
+       "      <td>8126043578764904179452276022324867195623210187...</td>\n",
+       "      <td>0.525872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23334</th>\n",
+       "      <td>181.00700</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>thieno[2,3-d]pyrimidine-4-carboxylic acid</td>\n",
+       "      <td>O=C(O)c1ncnc2sccc12</td>\n",
+       "      <td>VOXKHPSKDOLCAC-UHFFFAOYSA-N</td>\n",
+       "      <td>4621371819674604814601843392349447447010638904...</td>\n",
+       "      <td>0.392072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29146</th>\n",
+       "      <td>181.06100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Nicotinuric acid</td>\n",
+       "      <td>O=C(O)CNC(=O)c1cccnc1</td>\n",
+       "      <td>ZBSGKPYXQINNGF-UHFFFAOYSA-N</td>\n",
+       "      <td>4605782118791699534560809552554836164365415626...</td>\n",
+       "      <td>0.511462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478528</th>\n",
+       "      <td>202.04300</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>thiabendazole</td>\n",
+       "      <td>c1ccc2[nH]c(-c3cscn3)nc2c1</td>\n",
+       "      <td>WJCNZQLZVWNLKY-UHFFFAOYSA-N</td>\n",
+       "      <td>4037303697924680981028453225819741544585843750...</td>\n",
+       "      <td>0.487617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209484</th>\n",
+       "      <td>323.13902</td>\n",
+       "      <td>105.64</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>5-[(2,5-dimethylphenoxy)methyl]-N-(4-pyridinyl...</td>\n",
+       "      <td>Cc1ccc(C)c(OCc2ccc(C(=O)Nc3ccncc3)o2)c1</td>\n",
+       "      <td>VYTRUHMGFSNSKK-UHFFFAOYSA-N</td>\n",
+       "      <td>4704480111668309422204405940015522296005033315...</td>\n",
+       "      <td>0.396629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478527</th>\n",
+       "      <td>202.04300</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>thiabendazole</td>\n",
+       "      <td>c1ccc2[nH]c(-c3cscn3)nc2c1</td>\n",
+       "      <td>WJCNZQLZVWNLKY-UHFFFAOYSA-N</td>\n",
+       "      <td>4354824398973859393735759007513142956866549634...</td>\n",
+       "      <td>0.472113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>912123</th>\n",
+       "      <td>330.18200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>N-[(1R,2S)-2-(4-ethoxyphenyl)cyclopropyl]-3,4-...</td>\n",
+       "      <td>CCOc1ccc(C2CC2NC(=O)N2CC(=O)N(C)C(C)C2)cc1</td>\n",
+       "      <td>FUDGTDMCXMAZHS-UHFFFAOYSA-N</td>\n",
+       "      <td>4689876070452517240544538176971004829216063873...</td>\n",
+       "      <td>0.487231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478527</th>\n",
+       "      <td>202.04300</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>thiabendazole</td>\n",
+       "      <td>c1ccc2[nH]c(-c3cscn3)nc2c1</td>\n",
+       "      <td>WJCNZQLZVWNLKY-UHFFFAOYSA-N</td>\n",
+       "      <td>4354824398973859393735759007513142956866549634...</td>\n",
+       "      <td>0.490491</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        precursor_mz  retention_time collision_energy  \\\n",
+       "29144      181.06100             NaN             None   \n",
+       "29138      181.06100             NaN             None   \n",
+       "29139      181.06100             NaN             None   \n",
+       "23334      181.00700             NaN             None   \n",
+       "29146      181.06100             NaN             None   \n",
+       "478528     202.04300             NaN             None   \n",
+       "209484     323.13902          105.64             20.0   \n",
+       "478527     202.04300             NaN             None   \n",
+       "912123     330.18200             NaN             None   \n",
+       "478527     202.04300             NaN             None   \n",
+       "\n",
+       "                                            compound_name  \\\n",
+       "29144                                    Nicotinuric acid   \n",
+       "29138                                    Nicotinuric acid   \n",
+       "29139                                    Nicotinuric acid   \n",
+       "23334           thieno[2,3-d]pyrimidine-4-carboxylic acid   \n",
+       "29146                                    Nicotinuric acid   \n",
+       "478528                                      thiabendazole   \n",
+       "209484  5-[(2,5-dimethylphenoxy)methyl]-N-(4-pyridinyl...   \n",
+       "478527                                      thiabendazole   \n",
+       "912123  N-[(1R,2S)-2-(4-ethoxyphenyl)cyclopropyl]-3,4-...   \n",
+       "478527                                      thiabendazole   \n",
+       "\n",
+       "                                            smiles  \\\n",
+       "29144                        O=C(O)CNC(=O)c1cccnc1   \n",
+       "29138                        O=C(O)CNC(=O)c1cccnc1   \n",
+       "29139                        O=C(O)CNC(=O)c1cccnc1   \n",
+       "23334                          O=C(O)c1ncnc2sccc12   \n",
+       "29146                        O=C(O)CNC(=O)c1cccnc1   \n",
+       "478528                  c1ccc2[nH]c(-c3cscn3)nc2c1   \n",
+       "209484     Cc1ccc(C)c(OCc2ccc(C(=O)Nc3ccncc3)o2)c1   \n",
+       "478527                  c1ccc2[nH]c(-c3cscn3)nc2c1   \n",
+       "912123  CCOc1ccc(C2CC2NC(=O)N2CC(=O)N(C)C(C)C2)cc1   \n",
+       "478527                  c1ccc2[nH]c(-c3cscn3)nc2c1   \n",
+       "\n",
+       "                           inchikey  \\\n",
+       "29144   ZBSGKPYXQINNGF-UHFFFAOYSA-N   \n",
+       "29138   ZBSGKPYXQINNGF-UHFFFAOYSA-N   \n",
+       "29139   ZBSGKPYXQINNGF-UHFFFAOYSA-N   \n",
+       "23334   VOXKHPSKDOLCAC-UHFFFAOYSA-N   \n",
+       "29146   ZBSGKPYXQINNGF-UHFFFAOYSA-N   \n",
+       "478528  WJCNZQLZVWNLKY-UHFFFAOYSA-N   \n",
+       "209484  VYTRUHMGFSNSKK-UHFFFAOYSA-N   \n",
+       "478527  WJCNZQLZVWNLKY-UHFFFAOYSA-N   \n",
+       "912123  FUDGTDMCXMAZHS-UHFFFAOYSA-N   \n",
+       "478527  WJCNZQLZVWNLKY-UHFFFAOYSA-N   \n",
+       "\n",
+       "                                          spectrum_hashes  \\\n",
+       "29144   4538586676799711060475687385406365205312522933...   \n",
+       "29138   4691175720354550216218758199469384349519880571...   \n",
+       "29139   8126043578764904179452276022324867195623210187...   \n",
+       "23334   4621371819674604814601843392349447447010638904...   \n",
+       "29146   4605782118791699534560809552554836164365415626...   \n",
+       "478528  4037303697924680981028453225819741544585843750...   \n",
+       "209484  4704480111668309422204405940015522296005033315...   \n",
+       "478527  4354824398973859393735759007513142956866549634...   \n",
+       "912123  4689876070452517240544538176971004829216063873...   \n",
+       "478527  4354824398973859393735759007513142956866549634...   \n",
+       "\n",
+       "        ms2query_reliability_prediction  \n",
+       "29144                          0.524193  \n",
+       "29138                          0.477547  \n",
+       "29139                          0.525872  \n",
+       "23334                          0.392072  \n",
+       "29146                          0.511462  \n",
+       "478528                         0.487617  \n",
+       "209484                         0.396629  \n",
+       "478527                         0.472113  \n",
+       "912123                         0.487231  \n",
+       "478527                         0.490491  "
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reference_library.run_ms2query(test_spectra)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc3f2477",
+   "metadata": {},
+   "source": [
+    "# Create a new MS2Query reference library\n",
+    "Here we show how to create the reference library that can be downloaded from Zenodo. But if you want to make your own of course replace with your own files (no need to download the library)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2501dc1e-9ab0-408d-b869-8cd9874a46d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "download_file(\"https://zenodo.org/records/16882111/files/merged_and_cleaned_libraries_1.mgf?download=1\", \n",
     "              os.path.join(folder_to_store_zenodo_files, \"merged_and_cleaned_libraries_1.mgf\"))\n",
     "download_file(\"https://zenodo.org/records/17826815/files/ms2deepscore_model.pt?download=1\", \n",
     "              os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cae72056-c3d3-4f4f-b976-c47c86002007",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "library_spectra = list(tqdm(load_from_mgf(os.path.join(folder_to_store_zenodo_files, \"merged_and_cleaned_libraries_1.mgf\"))))"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "fa99d2dd",
+   "id": "09c62f09-ac8e-4b06-a286-28b18bf61af8",
    "metadata": {},
    "source": [
-    "# Specify file location \n",
-    "Replace with your file names"
+    "### Creates the library and saves all the files for future usage. (the files you can also just download above)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6db579a9",
+   "id": "8067f86d-9120-4611-bae0-5a70159c4328",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Create mapping from inchikey to spectrum: 100%|█████████████████████████████████████████████████████████████████████████████████| 1017531/1017531 [00:05<00:00, 183656.80it/s]\n",
+      "Hashing spectra: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1017531/1017531 [01:27<00:00, 11680.81it/s]\n",
+      "Computing spectral embeddings ...: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 1017531/1017531 [40:32<00:00, 418.26it/s]\n",
+      "Get most common inchi per inchikey: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 67648/67648 [00:03<00:00, 17833.31it/s]\n",
+      "Adding fingerprints to Inchikeys: 67648it [10:40, 105.60it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "reference_library = ReferenceLibrary.create_from_spectra(library_spectra, os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"), store_file_directory = Path(os.path.join(folder_to_store_zenodo_files, \"full_library\")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd223f01-e70d-4a0c-8e68-2f28d7746a55",
+   "metadata": {},
+   "source": [
+    "# Sanity check that benchmarking resuls also work with new library creation\n",
+    "Not part of the tutorial, just internal documentation. We test the negative in negative library searching. To check that it matches the original benmarking notebook. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b7df418e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The file ./zenodo_files/data_split_inchikeys.json already exists, the file won't be downloaded\n",
+      "The file ./zenodo_files/merged_and_cleaned_libraries_1.mgf already exists, the file won't be downloaded\n",
+      "The file ./zenodo_files/ms2deepscore_model.pt already exists, the file won't be downloaded\n"
+     ]
+    }
+   ],
    "source": [
-    "library_spectra_file = os.path.join(folder_to_store_zenodo_files, \"merged_and_cleaned_libraries_1.mgf\")\n",
-    "ms2deepscore_model_file_name = os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\")\n",
-    "query_spectrum_file = \"replace_with_your_lib_spectra.mgf\""
+    "os.makedirs(folder_to_store_zenodo_files, exist_ok=True)\n",
+    "# This file contains the inchikeys used for the data split. This ensures reproducing the exact same results, \n",
+    "# alternatively you can of course do the data split yourself, but results might differ slightly. \n",
+    "download_file(\"https://zenodo.org/records/18801895/files/data_split_inchikeys.json?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"data_split_inchikeys.json\"))\n",
+    "download_file(\"https://zenodo.org/records/16882111/files/merged_and_cleaned_libraries_1.mgf?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"merged_and_cleaned_libraries_1.mgf\"))\n",
+    "download_file(\"https://zenodo.org/records/17826815/files/ms2deepscore_model.pt?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"))"
    ]
   },
   {
@@ -90,91 +496,598 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "1017531it [09:51, 1720.67it/s]\n"
+      "730577it [04:27, 2631.43it/s]"
      ]
     }
    ],
    "source": [
     "from matchms.importing import load_from_mgf\n",
     "from tqdm import tqdm\n",
+    "import os\n",
+    "library_spectra = list(tqdm(load_from_mgf(os.path.join(folder_to_store_zenodo_files, \"merged_and_cleaned_libraries_1.mgf\"))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25be7410",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
     "\n",
-    "library_spectra = list(tqdm(load_from_mgf(library_spectra_file)))\n",
-    "query_spectra = list(tqdm(load_from_mgf(query_spectrum_file)))"
+    "with open(os.path.join(folder_to_store_zenodo_files, \"data_split_inchikeys.json\"), \"r\") as f:\n",
+    "    data_split = json.load(f)"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "64534dd6",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27a82f53",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "# Create the reference library files\n",
-    "The code below will precompute everything needed to run MS2Query. It will save this in the same folder as your ms2deepscore model. \n",
-    "The files created are \"embeddings.npz\", \"top_k_tanimoto_scores.parquet\", \"library_metadata.parquet\". "
+    "val_inchikeys = set(data_split[\"val\"])\n",
+    "train_inchikeys = set(data_split[\"train\"])\n",
+    "test_inchikeys = set(data_split[\"test\"])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "669a489a",
+   "id": "bf5b0447",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ms2query.ms2query_development.ReferenceLibrary import ReferenceLibrary\n",
-    "reference_library = ReferenceLibrary.create_from_spectra(library_spectra, ms2deepscore_model_file_name)"
+    "val_spectra = []\n",
+    "test_spectra = []\n",
+    "train_spectra = []\n",
+    "for spectrum in tqdm(library_spectra):\n",
+    "    inchikey = spectrum.get(\"inchikey\")[:14]\n",
+    "    if inchikey in train_inchikeys:\n",
+    "        train_spectra.append(spectrum)\n",
+    "    elif inchikey in val_inchikeys:\n",
+    "        val_spectra.append(spectrum)\n",
+    "    elif inchikey in test_inchikeys:\n",
+    "        test_spectra.append(spectrum)\n",
+    "    else:\n",
+    "        raise ValueError(f\"The inchikey {inchikey} does not appear in the val, test or train inchikeys\")"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "dfd0a633",
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "76471c2c-d7ab-4058-a42b-31b975e250ba",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "# Run MS2Query\n",
-    "The code above only has to be run once after that you can load the library faster from the saved files. "
+    "def split_on_ionmode(spectra):\n",
+    "    pos_spectra = []\n",
+    "    neg_spectra = []\n",
+    "    for spectrum in spectra:\n",
+    "        ionmode = spectrum.get(\"ionmode\")\n",
+    "        if ionmode == \"positive\":\n",
+    "            pos_spectra.append(spectrum)\n",
+    "        elif ionmode == \"negative\":\n",
+    "            neg_spectra.append(spectrum)\n",
+    "        else:\n",
+    "            raise ValueError(f\"The ionmode {ionmode} is not positive or negative\")\n",
+    "    return pos_spectra, neg_spectra"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e6cf44e2",
+   "execution_count": 20,
+   "id": "02be88d5-3962-4c8e-89ab-72fa7163d47c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# no need to run if you just created the libary above\n",
-    "reference_library = ReferenceLibrary.load_from_directory(folder_to_store_zenodo_files)"
+    "pos_val_spectra, neg_val_spectra = split_on_ionmode(val_spectra)\n",
+    "pos_test_spectra, neg_test_spectra = split_on_ionmode(test_spectra)\n",
+    "pos_train_spectra, neg_train_spectra = split_on_ionmode(train_spectra)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "566b2b0d",
+   "id": "669a489a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Create mapping from inchikey to spectrum: 100%|███████████████████████████████████████████████████████████████████████████████████| 283790/283790 [00:01<00:00, 282229.91it/s]\n",
+      "Hashing spectra: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 283790/283790 [00:19<00:00, 14677.55it/s]\n",
+      "Computing spectral embeddings ...:  11%|██████████                                                                                    | 30305/283790 [00:31<04:24, 958.90it/s]"
+     ]
+    }
+   ],
+   "source": [
+    "from ms2query.ms2query_development.ReferenceLibrary import ReferenceLibrary\n",
+    "reference_library = ReferenceLibrary.create_from_spectra(neg_train_spectra, os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "829714f0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = reference_library.run_ms2query(query_spectra)"
+    "results = reference_library.run_ms2query(neg_val_spectra)"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "98ce9660",
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c9f79c50-0c19-4a9c-afd1-115233581193",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "print(results)"
+    "results.to_csv(os.path.join(folder_to_store_zenodo_files, \"neg_neg_results.csv\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "b7e3b533",
+   "execution_count": 28,
+   "id": "49771619-f9ed-46a1-8b9f-5ae70377fd7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>precursor_mz</th>\n",
+       "      <th>retention_time</th>\n",
+       "      <th>collision_energy</th>\n",
+       "      <th>compound_name</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>inchikey</th>\n",
+       "      <th>spectrum_hashes</th>\n",
+       "      <th>ms2query_reliability_prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4574</th>\n",
+       "      <td>266.05894</td>\n",
+       "      <td>69.35</td>\n",
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+       "      <th>4574</th>\n",
+       "      <td>266.05894</td>\n",
+       "      <td>69.35</td>\n",
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+       "      <td>379724-54-6</td>\n",
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+       "      <td>FGLQAKJRFPNIJT-UHFFFAOYSA-N</td>\n",
+       "      <td>4287947218867478945721777143654811527279348390...</td>\n",
+       "      <td>0.739373</td>\n",
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+       "    <tr>\n",
+       "      <th>4578</th>\n",
+       "      <td>266.05900</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>379724-54-6</td>\n",
+       "      <td>O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1</td>\n",
+       "      <td>FGLQAKJRFPNIJT-UHFFFAOYSA-N</td>\n",
+       "      <td>4453904535900060540047468210876322356832506165...</td>\n",
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+       "      <td>379724-54-6</td>\n",
+       "      <td>O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1</td>\n",
+       "      <td>FGLQAKJRFPNIJT-UHFFFAOYSA-N</td>\n",
+       "      <td>4438257318628859128622642248978796499540902384...</td>\n",
+       "      <td>0.770879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283597</th>\n",
+       "      <td>740.52030</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>PE 36:3</td>\n",
+       "      <td>CCCCCC=CCC=CCCCCCCCC(=O)OC(COC(=O)CCCCCCCC=CCC...</td>\n",
+       "      <td>GKAFCSRKMWFPSJ-UHFFFAOYSA-N</td>\n",
+       "      <td>4605131291822750236269081691830926191534825838...</td>\n",
+       "      <td>0.943663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283597</th>\n",
+       "      <td>740.52030</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>PE 36:3</td>\n",
+       "      <td>CCCCCC=CCC=CCCCCCCCC(=O)OC(COC(=O)CCCCCCCC=CCC...</td>\n",
+       "      <td>GKAFCSRKMWFPSJ-UHFFFAOYSA-N</td>\n",
+       "      <td>4605131291822750236269081691830926191534825838...</td>\n",
+       "      <td>0.943663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201773</th>\n",
+       "      <td>744.55300</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>PE(18:0_18:1) - (2-aminoethoxy)[2-[octadec-9-e...</td>\n",
+       "      <td>CCCCCCCCC=CCCCCCCCC(=O)OC(COC(=O)CCCCCCCCCCCCC...</td>\n",
+       "      <td>JQKOHRZNEOQNJE-UHFFFAOYSA-N</td>\n",
+       "      <td>8543555785430847640307667357763008254598100629...</td>\n",
+       "      <td>0.921203</td>\n",
+       "    </tr>\n",
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+       "      <th>235132</th>\n",
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+       "      <td>30.0</td>\n",
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+       "      <td>0.907530</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15967 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        precursor_mz  retention_time collision_energy  \\\n",
+       "4574       266.05894           69.35             30.0   \n",
+       "4576       266.05894           69.35             60.0   \n",
+       "4574       266.05894           69.35             30.0   \n",
+       "4578       266.05900             NaN             None   \n",
+       "4581       266.05900             NaN             None   \n",
+       "...              ...             ...              ...   \n",
+       "283597     740.52030             NaN             45.0   \n",
+       "283597     740.52030             NaN             45.0   \n",
+       "201773     744.55300             NaN             20.0   \n",
+       "235132     766.54100             NaN             30.0   \n",
+       "235132     766.54100             NaN             30.0   \n",
+       "\n",
+       "                                            compound_name  \\\n",
+       "4574                                          379724-54-6   \n",
+       "4576                                          379724-54-6   \n",
+       "4574                                          379724-54-6   \n",
+       "4578                                          379724-54-6   \n",
+       "4581                                          379724-54-6   \n",
+       "...                                                   ...   \n",
+       "283597                                            PE 36:3   \n",
+       "283597                                            PE 36:3   \n",
+       "201773  PE(18:0_18:1) - (2-aminoethoxy)[2-[octadec-9-e...   \n",
+       "235132  1-Octadecanoyl-2-(5Z,8Z,11Z,14Z-eicosatetraeno...   \n",
+       "235132  1-Octadecanoyl-2-(5Z,8Z,11Z,14Z-eicosatetraeno...   \n",
+       "\n",
+       "                                                   smiles  \\\n",
+       "4574                    O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1   \n",
+       "4576                    O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1   \n",
+       "4574                    O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1   \n",
+       "4578                    O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1   \n",
+       "4581                    O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1   \n",
+       "...                                                   ...   \n",
+       "283597  CCCCCC=CCC=CCCCCCCCC(=O)OC(COC(=O)CCCCCCCC=CCC...   \n",
+       "283597  CCCCCC=CCC=CCCCCCCCC(=O)OC(COC(=O)CCCCCCCC=CCC...   \n",
+       "201773  CCCCCCCCC=CCCCCCCCC(=O)OC(COC(=O)CCCCCCCCCCCCC...   \n",
+       "235132  CCCCCC=CCC=CCC=CCC=CCCCC(=O)OC(COC(=O)CCCCCCCC...   \n",
+       "235132  CCCCCC=CCC=CCC=CCC=CCCCC(=O)OC(COC(=O)CCCCCCCC...   \n",
+       "\n",
+       "                           inchikey  \\\n",
+       "4574    FGLQAKJRFPNIJT-UHFFFAOYSA-N   \n",
+       "4576    FGLQAKJRFPNIJT-UHFFFAOYSA-N   \n",
+       "4574    FGLQAKJRFPNIJT-UHFFFAOYSA-N   \n",
+       "4578    FGLQAKJRFPNIJT-UHFFFAOYSA-N   \n",
+       "4581    FGLQAKJRFPNIJT-UHFFFAOYSA-N   \n",
+       "...                             ...   \n",
+       "283597  GKAFCSRKMWFPSJ-UHFFFAOYSA-N   \n",
+       "283597  GKAFCSRKMWFPSJ-UHFFFAOYSA-N   \n",
+       "201773  JQKOHRZNEOQNJE-UHFFFAOYSA-N   \n",
+       "235132  ANRKEHNWXKCXDB-UHFFFAOYSA-N   \n",
+       "235132  ANRKEHNWXKCXDB-UHFFFAOYSA-N   \n",
+       "\n",
+       "                                          spectrum_hashes  \\\n",
+       "4574    4287947218867478945721777143654811527279348390...   \n",
+       "4576    8445518326529625949101509334596600018097006285...   \n",
+       "4574    4287947218867478945721777143654811527279348390...   \n",
+       "4578    4453904535900060540047468210876322356832506165...   \n",
+       "4581    4438257318628859128622642248978796499540902384...   \n",
+       "...                                                   ...   \n",
+       "283597  4605131291822750236269081691830926191534825838...   \n",
+       "283597  4605131291822750236269081691830926191534825838...   \n",
+       "201773  8543555785430847640307667357763008254598100629...   \n",
+       "235132  4772335068029728878564587931928277302571040079...   \n",
+       "235132  4772335068029728878564587931928277302571040079...   \n",
+       "\n",
+       "        ms2query_reliability_prediction  \n",
+       "4574                           0.739373  \n",
+       "4576                           0.605217  \n",
+       "4574                           0.739373  \n",
+       "4578                           0.759073  \n",
+       "4581                           0.770879  \n",
+       "...                                 ...  \n",
+       "283597                         0.943663  \n",
+       "283597                         0.943663  \n",
+       "201773                         0.921203  \n",
+       "235132                         0.907530  \n",
+       "235132                         0.907530  \n",
+       "\n",
+       "[15967 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "6d9d10f8-a042-48c2-a9d1-b925815b9ac0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicted_inchikeys = [inchikey[:14] for inchikey in results[\"inchikey\"].tolist()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "de56ab2f-1e81-462a-9197-7d6099c22ad3",
    "metadata": {},
    "outputs": [],
    "source": [
-    "results.to_csv(\"ms2query_results.csv\")"
+    "predicted_scores = results[\"ms2query_reliability_prediction\"].tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b4bd52eb-742e-4a0b-bdaf-77b7a182a4e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import os\n",
+    "intermediates_folder = \"./cross_ionmode_intermediate\"\n",
+    "\n",
+    "with open(os.path.join(intermediates_folder, \"all_fingerprints.pickle\"), \"rb\") as handle:\n",
+    "    all_fingerprints = pickle.load(handle)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "1905a382-d0d5-4c45-a2c6-11358e509e70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix\n",
+    "import numpy as np\n",
+    "def get_prediction_tanimoto(predicted_inchikeys, val_spectra, val_and_train_fingerprints):\n",
+    "    assert len(predicted_inchikeys) == len(val_spectra)\n",
+    "\n",
+    "    tanimoto_for_prediction = []\n",
+    "    for index, predicted_inchikey in enumerate(tqdm(predicted_inchikeys)):\n",
+    "        if predicted_inchikey is not None:\n",
+    "            correct_inchikey = val_spectra[index].get(\"inchikey\")[:14]\n",
+    "            predicted_fingerprint = val_and_train_fingerprints.get_fingerprints([predicted_inchikey])[0]\n",
+    "            actual_fingerprint = val_and_train_fingerprints.get_fingerprints([correct_inchikey])[0]\n",
+    "            tanimoto_prediction = jaccard_similarity_matrix(np.array([predicted_fingerprint]), np.array([actual_fingerprint]))[0][0]\n",
+    "            tanimoto_for_prediction.append(tanimoto_prediction)\n",
+    "        else:\n",
+    "            tanimoto_for_prediction.append(0.0)\n",
+    "    return tanimoto_for_prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "1ec0c831-ba10-4c0f-907e-6b7420f35ebd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████| 15967/15967 [00:00<00:00, 18131.11it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "neg_neg_tanimoto_for_prediction = get_prediction_tanimoto(predicted_inchikeys, neg_val_spectra, all_fingerprints)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "7eeccfdc-1542-43fc-ba7c-79ece4346a4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def create_values_violin_plot(predicted_scores, val_spectra, tanimoto_for_predictions, n_buckets):\n",
+    "    \"\"\"The predictions are ranked based on the predicted scores, and are split in different groups from high to low, \n",
+    "    for each group the average tanimoto per inchikey is computed.\"\"\"\n",
+    "    tanimoto_per_bucket = []\n",
+    "    ranked_indexes_per_bucket = bucket_by_value(predicted_scores, n_buckets)\n",
+    "    for indexes_in_bucket in ranked_indexes_per_bucket: \n",
+    "        average_tanimoto_per_inchikey = get_average_tanimoto_per_inchikey(val_spectra, tanimoto_for_predictions, indexes_in_bucket)\n",
+    "        tanimoto_per_bucket.append(average_tanimoto_per_inchikey)\n",
+    "    return tanimoto_per_bucket\n",
+    "\n",
+    "def bucket_by_value(values, n_buckets=10):\n",
+    "    \"\"\"\n",
+    "    Splits values into n equally sized buckets based on rank. \n",
+    "    So the highest scores are in the first bucket en the lowest in the last. Each bucket has the same number of values. \n",
+    "    \"\"\"\n",
+    "    values = np.array(values)\n",
+    "    # Get indexes that would sort the array descending (top values first)\n",
+    "    sorted_indexes = np.argsort(values)[::-1]\n",
+    "    \n",
+    "    # Split sorted indexes into n equal(ish) buckets\n",
+    "    buckets = np.array_split(sorted_indexes, n_buckets)\n",
+    "    \n",
+    "    return buckets\n",
+    "    \n",
+    "def get_average_tanimoto_per_inchikey(query_spectrum_set, tanimoto_for_predictions, selected_indexes):\n",
+    "    \"\"\"Calculates the average tanimoto per inchikey for the selected indexes\"\"\"\n",
+    "    assert len(tanimoto_for_predictions) == len(query_spectrum_set)\n",
+    "    average_scores_per_inchikey = []\n",
+    "    # Calculate score per unique inchikey\n",
+    "    for inchikey in query_spectrum_set.spectrum_indices_per_inchikey.keys():\n",
+    "        matching_spectrum_indexes = query_spectrum_set.spectrum_indices_per_inchikey[inchikey]\n",
+    "        prediction_scores = []\n",
+    "        for index in matching_spectrum_indexes:\n",
+    "            if index in selected_indexes:\n",
+    "                prediction_scores.append(tanimoto_for_predictions[index])\n",
+    "        if len(prediction_scores) > 0:\n",
+    "            average_prediction = sum(prediction_scores) / len(prediction_scores)\n",
+    "            average_scores_per_inchikey.append(average_prediction)\n",
+    "    return average_scores_per_inchikey\n",
+    "\n",
+    "def plot_average_per_threshold(predicted_scores, tanimoto_for_predictions, val_spectra, n_of_buckets):\n",
+    "    buckets = bucket_by_value(predicted_scores,n_of_buckets)\n",
+    "    mean_per_fraction = []\n",
+    "    selected_indexes = set()\n",
+    "    for indexes_in_bucket in tqdm(buckets):\n",
+    "        selected_indexes.update(set(indexes_in_bucket.tolist()))\n",
+    "        average_scores_per_inchikey = get_average_tanimoto_per_inchikey(val_spectra, tanimoto_for_predictions, selected_indexes)\n",
+    "        mean = sum(average_scores_per_inchikey)/len(average_scores_per_inchikey)\n",
+    "        mean_per_fraction.append(mean)\n",
+    "    return mean_per_fraction\n",
+    "    \n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "def plot_comparison_plot(results):\n",
+    "    n = len(list(results.values())[0])\n",
+    "    labels = [f\"0 - {(100/n)*(i+1)} %\" for i in range(n)]\n",
+    "    for label, mean_per_fraction in results.items():\n",
+    "        plt.plot(labels, mean_per_fraction, label = label)\n",
+    "    \n",
+    "    plt.xlabel(\"Fraction predicted\")\n",
+    "    plt.ylabel(\"Average Tanimoto\")\n",
+    "    plt.legend()\n",
+    "    plt.ylim((0, 1))\n",
+    "    \n",
+    "    step = n // 10\n",
+    "    positions = range(len(labels))\n",
+    "    \n",
+    "    plt.xticks(\n",
+    "        ticks=positions[step-1::step],    # start at 9, then 19, 29, ...\n",
+    "        labels=labels[step-1::step],\n",
+    "        rotation=45,\n",
+    "        ha='right'\n",
+    "    )\n",
+    "    plt.grid(axis='both', linestyle='--', alpha=0.5)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "3bf6e470-aa36-4c07-85d0-474d0e52002b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Create mapping from inchikey to spectrum: 100%|████████████████████████████████████| 15967/15967 [00:00<00:00, 367569.63it/s]\n",
+      "100%|█████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 603.54it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "plot_comparison_plot({\n",
+    "    # \"MS2DeepScore\":  plot_average_per_threshold(ms2deepscores.squeeze(), neg_neg_tanimoto_for_prediction_ms2deepscore, neg_val_spectra, 100),\n",
+    "    \"MS2Query 2.0\": plot_average_per_threshold(predicted_scores, neg_neg_tanimoto_for_prediction, AnnotatedSpectrumSet.create_spectrum_set(neg_val_spectra), 100),\n",
+    "})"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ms2query_2",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },