diff --git a/tutorials/README.md b/tutorials/README.md
index bf0751a..2955a78 100644
--- a/tutorials/README.md
+++ b/tutorials/README.md
@@ -37,6 +37,7 @@ More complex topics that might not be suited to every IR course are still covere
 | Query Spelling Correction (work in progress)|  [🔗](tutorial-spelling-correction.ipynb) | ⏳ |
 | Splade for Query Processing (work in progress)|  [🔗](tutorial-splade-query-processing.ipynb) | ⏳ |
 | Splade for Document Processing (work in progress)|  [🔗](tutorial-splade-ranking.ipynb) | ⏳ |
+| DocT5Query (work in progress)|  [🔗](tutorial-doc-t5-query.ipynb) | ⏳ |
 | Re-ranking with cross-encoders or bi-encoders ([work in progress](https://github.com/tira-io/teaching-ir-with-shared-tasks/issues/3)) | [⏳](https://github.com/tira-io/teaching-ir-with-shared-tasks/issues/3) | [⏳](https://github.com/tira-io/teaching-ir-with-shared-tasks/issues/3) |
 | _Anyhting missing? [Propose new tutorial.](https://github.com/tira-io/teaching-ir-with-shared-tasks/issues/new)_ | | |
 
diff --git a/tutorials/tutorial-doc-t5-query.ipynb b/tutorials/tutorial-doc-t5-query.ipynb
new file mode 100644
index 0000000..66e81a2
--- /dev/null
+++ b/tutorials/tutorial-doc-t5-query.ipynb
@@ -0,0 +1,641 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IR Lab Tutorial: Document Expansion with DocT5Query\n",
+    "\n",
+    "TBD..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import All Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "PyTerrier 0.10.0 has loaded Terrier 5.8 (built by craigm on 2023-11-01 18:05) and terrier-helper 0.0.8\n",
+      "\n",
+      "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tira.third_party_integrations import ensure_pyterrier_is_loaded\n",
+    "from tira.rest_api_client import Client\n",
+    "ensure_pyterrier_is_loaded()\n",
+    "import pandas as pd\n",
+    "import pyterrier as pt\n",
+    "from tqdm import tqdm\n",
+    "import gzip\n",
+    "import json\n",
+    "\n",
+    "tira = Client()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DocT5 Query on a corpus with already high recall\n",
+    "\n",
+    "TBD."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = 'antique-test-20230107-training'\n",
+    "pt_dataset = pt.get_dataset(f'irds:ir-benchmarks/{dataset}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bm25 = tira.pt.from_submission('ir-benchmarks/tira-ir-starter/BM25 Re-Rank (tira-ir-starter-pyterrier)', dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>qid</th>\n",
+       "      <th>query</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3990512</td>\n",
+       "      <td>how can we get concentration onsomething</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>714612</td>\n",
+       "      <td>why doesn t the water fall off earth if it s r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2528767</td>\n",
+       "      <td>how do i determine the charge of the iron ion ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       qid                                              query\n",
+       "0  3990512           how can we get concentration onsomething\n",
+       "1   714612  why doesn t the water fall off earth if it s r...\n",
+       "2  2528767  how do i determine the charge of the iron ion ..."
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pt_dataset.get_topics('text').head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>name</th>\n",
+       "      <th>recall_1000</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BM25</td>\n",
+       "      <td>0.788732</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   name  recall_1000\n",
+       "0  BM25     0.788732"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pt.Experiment(\n",
+    "    retr_systems=[bm25],\n",
+    "    topics=pt_dataset.get_topics('text'),\n",
+    "    qrels=pt_dataset.get_qrels(),\n",
+    "    names=['BM25'],\n",
+    "    eval_metrics=['recall_1000']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def doc_t5_query(dataset):\n",
+    "    docs = tira.get_run_output('ir-benchmarks/seanmacavaney/DocT5Query', dataset) + '/documents.jsonl.gz'\n",
+    "    with gzip.open(docs, 'rt') as f:\n",
+    "        for l in tqdm(f):\n",
+    "            l = json.loads(l)\n",
+    "            l['text'] = l['querygen']\n",
+    "            l['docno'] = l['doc_id']\n",
+    "            del l['doc_id']\n",
+    "            del l['querygen']\n",
+    "            yield l\n",
+    "\n",
+    "def doc_t5_query_index(dataset):\n",
+    "    indexer = pt.IterDictIndexer(\"/tmp/index2\", overwrite=True, meta={'docno': 100, 'text': 20480})\n",
+    "    index_ref = indexer.index(doc_t5_query(dataset))\n",
+    "    return pt.IndexFactory.of(index_ref)\n",
+    "\n",
+    "#index = doc_t5_query_index(dataset)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index = pt.IndexFactory.of('/tmp/index2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "188633it [00:08, 21509.20it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "docs_retrieved_by_bm25 = {}\n",
+    "\n",
+    "bm25_result = bm25(pt_dataset.get_topics('title'))\n",
+    "\n",
+    "for _, i in tqdm(bm25_result.iterrows()):\n",
+    "    qid, docno = str(i['qid']), str(i['docno'])\n",
+    "\n",
+    "    if qid not in docs_retrieved_by_bm25:\n",
+    "        docs_retrieved_by_bm25[qid] = set()\n",
+    "    \n",
+    "    docs_retrieved_by_bm25[qid].add(docno)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "omit_already_retrieved_docs = lambda i: i[i.apply(lambda j: str(j['docno']) not in docs_retrieved_by_bm25[str(j['qid'])], axis=1)]\n",
+    "omit_already_retrieved_docs = pt.apply.generic(omit_already_retrieved_docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bm25_doct5query = pt.BatchRetrieve(index, wmodel=\"BM25\")\n",
+    "\n",
+    "bm25_doct5query_new = bm25_doct5query >> omit_already_retrieved_docs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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>name</th>\n",
+       "      <th>recall_1000</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DocT5Query &gt;&gt; BM25</td>\n",
+       "      <td>0.534685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DocT5Query w.o. BM25 &gt;&gt; BM25</td>\n",
+       "      <td>0.019399</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           name  recall_1000\n",
+       "0            DocT5Query >> BM25     0.534685\n",
+       "1  DocT5Query w.o. BM25 >> BM25     0.019399"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pt.Experiment(\n",
+    "    retr_systems=[bm25_doct5query, bm25_doct5query_new],\n",
+    "    topics=pt_dataset.get_topics('text'),\n",
+    "    qrels=pt_dataset.get_qrels(),\n",
+    "    names=['DocT5Query >> BM25', 'DocT5Query w.o. BM25 >> BM25'],\n",
+    "    eval_metrics=['recall_1000']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DocT5 Query on a corpus with lower recall\n",
+    "\n",
+    "TBD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = 'longeval-train-20230513-training'\n",
+    "pt_dataset = pt.get_dataset(f'irds:ir-benchmarks/{dataset}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bm25 = tira.pt.from_submission('ir-benchmarks/tira-ir-starter/BM25 Re-Rank (tira-ir-starter-pyterrier)', dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>name</th>\n",
+       "      <th>recall_1000</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BM25</td>\n",
+       "      <td>0.496595</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   name  recall_1000\n",
+       "0  BM25     0.496595"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pt.Experiment(\n",
+    "    retr_systems=[bm25],\n",
+    "    topics=pt_dataset.get_topics('text'),\n",
+    "    qrels=pt_dataset.get_qrels(),\n",
+    "    names=['BM25'],\n",
+    "    eval_metrics=['recall_1000']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Download from the Incubator: https://files.webis.de/data-in-production/data-research/tira-zenodo-dump-preparation/doc-t5-query/2024-03-19-19-46-01.zip\n",
+      "\tThis is only used for last spot checks before archival to Zenodo.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Download: 100%|██████████| 60.8M/60.8M [00:01<00:00, 38.8MiB/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Download finished. Extract...\n",
+      "Extraction finished:  /root/.tira/extracted_runs/ir-benchmarks/longeval-train-20230513-training/seanmacavaney\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "16164it [00:07, 5062.00it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11:34:18.676 [ForkJoinPool-1-worker-3] WARN org.terrier.structures.indexing.Indexer - Adding an empty document to the index (doc062207101085) - further warnings are suppressed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "1570734it [03:13, 8136.38it/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11:37:49.947 [ForkJoinPool-1-worker-3] WARN org.terrier.structures.indexing.Indexer - Indexed 110 empty documents\n"
+     ]
+    }
+   ],
+   "source": [
+    "index = doc_t5_query_index(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bm25_doct5query = pt.BatchRetrieve(index, wmodel=\"BM25\") % 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0it [00:00, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "66567it [00:04, 13604.33it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "docs_retrieved_by_bm25 = {}\n",
+    "\n",
+    "bm25_result = bm25(pt_dataset.get_topics('title'))\n",
+    "\n",
+    "for _, i in tqdm(bm25_result.iterrows()):\n",
+    "    qid, docno = str(i['qid']), str(i['docno'])\n",
+    "\n",
+    "    if qid not in docs_retrieved_by_bm25:\n",
+    "        docs_retrieved_by_bm25[qid] = set()\n",
+    "    \n",
+    "    docs_retrieved_by_bm25[qid].add(docno)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "omit_already_retrieved_docs = lambda i: i[i.apply(lambda j: str(j['docno']) not in docs_retrieved_by_bm25[str(j['qid'])], axis=1)]\n",
+    "omit_already_retrieved_docs = pt.apply.generic(omit_already_retrieved_docs)\n",
+    "\n",
+    "bm25_doct5query_new = bm25_doct5query >> omit_already_retrieved_docs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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>name</th>\n",
+       "      <th>recall_1000</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DocT5Query &gt;&gt; BM25</td>\n",
+       "      <td>0.350889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DocT5Query w.o. BM25 &gt;&gt; BM25</td>\n",
+       "      <td>0.066767</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           name  recall_1000\n",
+       "0            DocT5Query >> BM25     0.350889\n",
+       "1  DocT5Query w.o. BM25 >> BM25     0.066767"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pt.Experiment(\n",
+    "    retr_systems=[bm25_doct5query, bm25_doct5query_new],\n",
+    "    topics=pt_dataset.get_topics('text'),\n",
+    "    qrels=pt_dataset.get_qrels(),\n",
+    "    names=['DocT5Query >> BM25', 'DocT5Query w.o. BM25 >> BM25'],\n",
+    "    eval_metrics=['recall_1000']\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+}