Update notebooks

9 files changed, 1255 insertions, 340 deletions

diff --git a/notebooks/01-look-at-emnist.ipynb b/notebooks/01-look-at-emnist.ipynb
index 8c5d54e..5e5750e 100644
--- a/notebooks/01-look-at-emnist.ipynb
+++ b/notebooks/01-look-at-emnist.ipynb
@@ -3,14 +3,45 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: /home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torchvision/image.so: undefined symbol: _ZNK3c1010TensorImpl36is_contiguous_nondefault_policy_implENS_12MemoryFormatE\n",
-      "  warn(f\"Failed to load image Python extension: {e}\")\n"
+     "ename": "ImportError",
+     "evalue": "cannot import name 'Schema' from 'pydantic' (/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/pydantic/__init__.py)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning/app/__init__.py:7\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_utilities\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimports\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m module_available\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _root_logger  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _logger  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning_app/__init__.py:31\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m__version__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m version \u001b[38;5;28;01mas\u001b[39;00m __version__  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[0;32m---> 31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapp\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LightningApp  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mflow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LightningFlow  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning_app/core/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapp\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LightningApp\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mflow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LightningFlow\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning_app/core/app.py:15\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _console\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrequest_types\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m APIRequest, CommandRequest, DeltaRequest\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstants\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     17\u001b[0m     DEBUG_ENABLED,\n\u001b[1;32m     18\u001b[0m     FLOW_DURATION_SAMPLES,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     21\u001b[0m     STATE_ACCUMULATE_WAIT,\n\u001b[1;32m     22\u001b[0m )\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning_app/api/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhttp_methods\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Delete, Get, Post, Put\n\u001b[1;32m      3\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDelete\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGet\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPost\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPut\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning_app/api/http_methods.py:10\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01muuid\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m uuid4\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FastAPI, HTTPException\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrequest_types\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m APIRequest, CommandRequest, RequestResponse\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/fastapi/__init__.py:5\u001b[0m\n\u001b[1;32m      3\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.17\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapplications\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FastAPI\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrouting\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m APIRouter\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/fastapi/applications.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Callable, Dict, List, Optional, Type\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m routing\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mopenapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdocs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_redoc_html, get_swagger_ui_html\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/fastapi/routing.py:6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Callable, List, Optional, Type\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m params\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdependencies\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Dependant\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/fastapi/params.py:4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Callable, Sequence\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpydantic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Schema\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mParamTypes\u001b[39;00m(Enum):\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'Schema' from 'pydantic' (/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/pydantic/__init__.py)",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 13\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m     11\u001b[0m     sys\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m..\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtext_recognizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01memnist\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m EMNIST\n",
+      "File \u001b[0;32m~/projects/text-recognizer/text_recognizer/data/emnist.py:14\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtoml\u001b[39;00m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mloguru\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m logger \u001b[38;5;28;01mas\u001b[39;00m log\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtext_recognizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase_data_module\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseDataModule, load_and_print_info\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtext_recognizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase_dataset\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseDataset, split_dataset\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtext_recognizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdownload_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m download_dataset\n",
+      "File \u001b[0;32m~/projects/text-recognizer/text_recognizer/data/base_data_module.py:5\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpathlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Callable, Dict, Optional, Tuple, TypeVar\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mlightning\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mL\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DataLoader\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtext_recognizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase_dataset\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseDataset\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning/__init__.py:31\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m__about__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m  \u001b[38;5;66;03m# noqa: E402, F401, F403\u001b[39;00m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m__version__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m version \u001b[38;5;28;01mas\u001b[39;00m __version__  \u001b[38;5;66;03m# noqa: E402, F401\u001b[39;00m\n\u001b[0;32m---> 31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapp\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m storage  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapp\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapp\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LightningApp  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapp\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mflow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LightningFlow  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning/app/__init__.py:37\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linesep\n\u001b[0;32m---> 37\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__\n\u001b[1;32m     38\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYour `lightning` package was built for `lightning_app==1.8.0rc0`, but you are running \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m__version__\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     39\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(err)(\u001b[38;5;28mstr\u001b[39m(err) \u001b[38;5;241m+\u001b[39m linesep \u001b[38;5;241m+\u001b[39m msg)\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning_app/__init__.py:31\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(__about__, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__version__\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m     29\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m__version__\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m version \u001b[38;5;28;01mas\u001b[39;00m __version__  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[0;32m---> 31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapp\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LightningApp  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mflow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LightningFlow  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mwork\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LightningWork  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning_app/core/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapp\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LightningApp\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mflow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LightningFlow\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mwork\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LightningWork\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning_app/core/app.py:15\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_utilities\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply_func\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m apply_to_collection\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _console\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrequest_types\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m APIRequest, CommandRequest, DeltaRequest\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstants\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     17\u001b[0m     DEBUG_ENABLED,\n\u001b[1;32m     18\u001b[0m     FLOW_DURATION_SAMPLES,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     21\u001b[0m     STATE_ACCUMULATE_WAIT,\n\u001b[1;32m     22\u001b[0m )\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mqueues\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseQueue, SingleProcessQueue\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning_app/api/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhttp_methods\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Delete, Get, Post, Put\n\u001b[1;32m      3\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDelete\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGet\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPost\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPut\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/lightning_app/api/http_methods.py:10\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Callable, Dict, List, Optional\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01muuid\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m uuid4\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FastAPI, HTTPException\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrequest_types\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m APIRequest, CommandRequest, RequestResponse\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightning_app\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutilities\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapp_helpers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Logger\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/fastapi/__init__.py:5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"FastAPI framework, high performance, easy to learn, fast to code, ready for production\"\"\"\u001b[39;00m\n\u001b[1;32m      3\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.17\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapplications\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FastAPI\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrouting\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m APIRouter\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mparams\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Body, Path, Query, Header, Cookie, Form, File, Security, Depends\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/fastapi/applications.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Callable, Dict, List, Optional, Type\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m routing\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mopenapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdocs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_redoc_html, get_swagger_ui_html\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mopenapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_openapi\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/fastapi/routing.py:6\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mlogging\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Callable, List, Optional, Type\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m params\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdependencies\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Dependant\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdependencies\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_body_field, get_dependant, solve_dependencies\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/fastapi/params.py:4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01menum\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Enum\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Any, Callable, Sequence\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpydantic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Schema\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mParamTypes\u001b[39;00m(Enum):\n\u001b[1;32m      8\u001b[0m     query \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquery\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'Schema' from 'pydantic' (/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/pydantic/__init__.py)"
      ]
     }
    ],
diff --git a/notebooks/03-look-at-iam-lines.ipynb b/notebooks/03-look-at-iam-lines.ipynb
index d652a6d..3d71c3c 100644
--- a/notebooks/03-look-at-iam-lines.ipynb
+++ b/notebooks/03-look-at-iam-lines.ipynb
@@ -4,16 +4,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: /home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torchvision/image.so: undefined symbol: _ZNK3c1010TensorImpl36is_contiguous_nondefault_policy_implENS_12MemoryFormatE\n",
-      "  warn(f\"Failed to load image Python extension: {e}\")\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -44,7 +35,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "zsh:ulimit:1: value exceeds hard limit\r\n"
+      "zsh:ulimit:1: value exceeds hard limit\n"
      ]
     }
    ],
@@ -59,8 +50,8 @@
    "outputs": [],
    "source": [
     "def load_config(path: Path):\n",
-    "    with initialize(config_path=path.parent):\n",
-    "        cfg = compose(config_name=path.name)\n",
+    "    with initialize(config_path=str(path.parent)):\n",
+    "        cfg = compose(config_name=str(path.name))\n",
     "    return cfg"
    ]
   },
@@ -73,16 +64,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_9140/3010195264.py:2: UserWarning: \n",
+      "/tmp/ipykernel_20508/3921102733.py:2: UserWarning: \n",
       "The version_base parameter is not specified.\n",
       "Please specify a compatability version level, or None.\n",
       "Will assume defaults for version 1.1\n",
-      "  with initialize(config_path=path.parent):\n",
-      "/tmp/ipykernel_9140/3010195264.py:2: UserWarning: \n",
+      "  with initialize(config_path=str(path.parent)):\n",
+      "/tmp/ipykernel_20508/3921102733.py:2: UserWarning: \n",
       "The version_base parameter is not specified.\n",
       "Please specify a compatability version level, or None.\n",
       "Will assume defaults for version 1.1\n",
-      "  with initialize(config_path=path.parent):\n"
+      "  with initialize(config_path=str(path.parent)):\n"
      ]
     }
    ],
@@ -106,9 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -119,7 +108,7 @@
       "Input dims: (1, 56, 1024)\n",
       "Output dims: (89, 1)\n",
       "Train/val/test sizes: 10255, 1140, 1958\n",
-      "Train Batch x stats: (torch.Size([8, 1, 56, 1024]), torch.float32, tensor(0.), tensor(0.0402), tensor(0.1080), tensor(1.))\n",
+      "Train Batch x stats: (torch.Size([8, 1, 56, 1024]), torch.float32, tensor(0.), tensor(0.0388), tensor(0.1288), tensor(1.))\n",
       "Train Batch y stats: (torch.Size([8, 89]), torch.int64, tensor(1), tensor(52))\n",
       "Test Batch x stats: (torch.Size([8, 1, 56, 1024]), torch.float32, tensor(0.), tensor(0.0333), tensor(0.0951), tensor(0.8627))\n",
       "Test Batch y stats: (torch.Size([8, 89]), torch.int64, tensor(1), tensor(52))\n",
@@ -136,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,52 +157,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x, y = dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f4c73757490>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 4000x2000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(40, 20))\n",
-    "plt.imshow(x.squeeze(), cmap='gray')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 4000x2000 with 1 Axes>"
       ]
@@ -223,7 +172,7 @@
     },
     {
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       "text/plain": [
        "<Figure size 4000x2000 with 1 Axes>"
       ]
@@ -233,7 +182,7 @@
     },
     {
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       "text/plain": [
        "<Figure size 4000x2000 with 1 Axes>"
       ]
@@ -243,7 +192,7 @@
     },
     {
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       "text/plain": [
        "<Figure size 4000x2000 with 1 Axes>"
       ]
@@ -253,7 +202,7 @@
     },
     {
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       "text/plain": [
        "<Figure size 4000x2000 with 1 Axes>"
       ]
@@ -263,7 +212,7 @@
     },
     {
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       "text/plain": [
        "<Figure size 4000x2000 with 1 Axes>"
       ]
@@ -273,7 +222,7 @@
     },
     {
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       "text/plain": [
        "<Figure size 4000x2000 with 1 Axes>"
       ]
@@ -283,7 +232,7 @@
     },
     {
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",
       "text/plain": [
        "<Figure size 4000x2000 with 1 Axes>"
       ]
diff --git a/notebooks/03-look-at-iam-paragraphs.ipynb b/notebooks/03-look-at-iam-paragraphs.ipynb
index c5fe378..53054dc 100644
--- a/notebooks/03-look-at-iam-paragraphs.ipynb
+++ b/notebooks/03-look-at-iam-paragraphs.ipynb
@@ -2,19 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "6ce2519f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
     "import os\n",
     "os.environ['CUDA_VISIBLE_DEVICE'] = ''\n",
@@ -41,9 +34,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "726ac25b",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "def _plot(image, figsize=(12,12), title='', vmin=0, vmax=255):\n",
@@ -58,9 +53,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "ec16e41f-3d12-4da2-bf02-7429b41cf98e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from hydra import compose, initialize\n",
@@ -70,9 +67,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "a2523c35",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "path = \"../training/conf/datamodule/iam_extended_paragraphs.yaml\"\n",
@@ -82,20 +81,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "id": "e9386367-2b49-4633-9936-57081132e59e",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_22889/42406231.py:2: UserWarning: \n",
+      "/tmp/ipykernel_23754/3617549544.py:2: UserWarning: \n",
       "The version_base parameter is not specified.\n",
       "Please specify a compatability version level, or None.\n",
       "Will assume defaults for version 1.1\n",
       "  with initialize(config_path=\"../training/conf/\"):\n"
      ]
+    },
+    {
+     "ename": "ConfigCompositionException",
+     "evalue": "Multiple values for experiment. To override a value use 'override experiment: conv_transformer_paragraphs'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mConfigCompositionException\u001b[0m                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# context initialization\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m initialize(config_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../training/conf/\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m----> 3\u001b[0m     cfg \u001b[38;5;241m=\u001b[39m \u001b[43mcompose\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mconfig\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m+experiment=conv_transformer_paragraphs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m cfg \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mdatamodule\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/compose.py:38\u001b[0m, in \u001b[0;36mcompose\u001b[0;34m(config_name, overrides, return_hydra_config, strict)\u001b[0m\n\u001b[1;32m     36\u001b[0m gh \u001b[38;5;241m=\u001b[39m GlobalHydra\u001b[38;5;241m.\u001b[39minstance()\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m gh\u001b[38;5;241m.\u001b[39mhydra \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 38\u001b[0m cfg \u001b[38;5;241m=\u001b[39m \u001b[43mgh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhydra\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompose_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     39\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     40\u001b[0m \u001b[43m    \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     41\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrun_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mRunMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mRUN\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     42\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfrom_shell\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     43\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwith_log_configuration\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(cfg, DictConfig)\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_hydra_config:\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/hydra.py:594\u001b[0m, in \u001b[0;36mHydra.compose_config\u001b[0;34m(self, config_name, overrides, run_mode, with_log_configuration, from_shell, validate_sweep_overrides)\u001b[0m\n\u001b[1;32m    576\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompose_config\u001b[39m(\n\u001b[1;32m    577\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    578\u001b[0m     config_name: Optional[\u001b[38;5;28mstr\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    583\u001b[0m     validate_sweep_overrides: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    584\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DictConfig:\n\u001b[1;32m    585\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    586\u001b[0m \u001b[38;5;124;03m    :param config_name:\u001b[39;00m\n\u001b[1;32m    587\u001b[0m \u001b[38;5;124;03m    :param overrides:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;124;03m    :return:\u001b[39;00m\n\u001b[1;32m    592\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 594\u001b[0m     cfg \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig_loader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_configuration\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    596\u001b[0m \u001b[43m        \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    597\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrun_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    598\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfrom_shell\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_shell\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    599\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalidate_sweep_overrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_sweep_overrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    600\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    601\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m with_log_configuration:\n\u001b[1;32m    602\u001b[0m         configure_log(cfg\u001b[38;5;241m.\u001b[39mhydra\u001b[38;5;241m.\u001b[39mhydra_logging, cfg\u001b[38;5;241m.\u001b[39mhydra\u001b[38;5;241m.\u001b[39mverbose)\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/config_loader_impl.py:142\u001b[0m, in \u001b[0;36mConfigLoaderImpl.load_configuration\u001b[0;34m(self, config_name, overrides, run_mode, from_shell, validate_sweep_overrides)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_configuration\u001b[39m(\n\u001b[1;32m    134\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    135\u001b[0m     config_name: Optional[\u001b[38;5;28mstr\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    139\u001b[0m     validate_sweep_overrides: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    140\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DictConfig:\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 142\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_configuration_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    143\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    144\u001b[0m \u001b[43m            \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    145\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    146\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfrom_shell\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_shell\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    147\u001b[0m \u001b[43m            \u001b[49m\u001b[43mvalidate_sweep_overrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_sweep_overrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    148\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m OmegaConfBaseException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    150\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConfigCompositionException()\u001b[38;5;241m.\u001b[39mwith_traceback(sys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m2\u001b[39m]) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/config_loader_impl.py:253\u001b[0m, in \u001b[0;36mConfigLoaderImpl._load_configuration_impl\u001b[0;34m(self, config_name, overrides, run_mode, from_shell, validate_sweep_overrides)\u001b[0m\n\u001b[1;32m    248\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m validate_sweep_overrides:\n\u001b[1;32m    249\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_sweep_overrides_legal(\n\u001b[1;32m    250\u001b[0m         overrides\u001b[38;5;241m=\u001b[39mparsed_overrides, run_mode\u001b[38;5;241m=\u001b[39mrun_mode, from_shell\u001b[38;5;241m=\u001b[39mfrom_shell\n\u001b[1;32m    251\u001b[0m     )\n\u001b[0;32m--> 253\u001b[0m defaults_list \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_defaults_list\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcaching_repo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    256\u001b[0m \u001b[43m    \u001b[49m\u001b[43moverrides_list\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparsed_overrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    257\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprepend_hydra\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    258\u001b[0m \u001b[43m    \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_mode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mRunMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mMULTIRUN\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    259\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    261\u001b[0m config_overrides \u001b[38;5;241m=\u001b[39m defaults_list\u001b[38;5;241m.\u001b[39mconfig_overrides\n\u001b[1;32m    263\u001b[0m cfg \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compose_config_from_defaults_list(\n\u001b[1;32m    264\u001b[0m     defaults\u001b[38;5;241m=\u001b[39mdefaults_list\u001b[38;5;241m.\u001b[39mdefaults, repo\u001b[38;5;241m=\u001b[39mcaching_repo\n\u001b[1;32m    265\u001b[0m )\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:745\u001b[0m, in \u001b[0;36mcreate_defaults_list\u001b[0;34m(repo, config_name, overrides_list, prepend_hydra, skip_missing)\u001b[0m\n\u001b[1;32m    736\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    737\u001b[0m \u001b[38;5;124;03m:param repo:\u001b[39;00m\n\u001b[1;32m    738\u001b[0m \u001b[38;5;124;03m:param config_name:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    742\u001b[0m \u001b[38;5;124;03m:return:\u001b[39;00m\n\u001b[1;32m    743\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    744\u001b[0m overrides \u001b[38;5;241m=\u001b[39m Overrides(repo\u001b[38;5;241m=\u001b[39mrepo, overrides_list\u001b[38;5;241m=\u001b[39moverrides_list)\n\u001b[0;32m--> 745\u001b[0m defaults, tree \u001b[38;5;241m=\u001b[39m \u001b[43m_create_defaults_list\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    746\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    747\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    748\u001b[0m \u001b[43m    \u001b[49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    749\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprepend_hydra\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprepend_hydra\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    750\u001b[0m \u001b[43m    \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_missing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    751\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    752\u001b[0m overrides\u001b[38;5;241m.\u001b[39mensure_overrides_used()\n\u001b[1;32m    753\u001b[0m overrides\u001b[38;5;241m.\u001b[39mensure_deletions_used()\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:715\u001b[0m, in \u001b[0;36m_create_defaults_list\u001b[0;34m(repo, config_name, overrides, prepend_hydra, skip_missing)\u001b[0m\n\u001b[1;32m    706\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_create_defaults_list\u001b[39m(\n\u001b[1;32m    707\u001b[0m     repo: IConfigRepository,\n\u001b[1;32m    708\u001b[0m     config_name: Optional[\u001b[38;5;28mstr\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    711\u001b[0m     skip_missing: \u001b[38;5;28mbool\u001b[39m,\n\u001b[1;32m    712\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[List[ResultDefault], DefaultsTreeNode]:\n\u001b[1;32m    713\u001b[0m     root \u001b[38;5;241m=\u001b[39m _create_root(config_name\u001b[38;5;241m=\u001b[39mconfig_name, with_hydra\u001b[38;5;241m=\u001b[39mprepend_hydra)\n\u001b[0;32m--> 715\u001b[0m     defaults_tree \u001b[38;5;241m=\u001b[39m \u001b[43m_create_defaults_tree\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    716\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrepo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    717\u001b[0m \u001b[43m        \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    718\u001b[0m \u001b[43m        \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    719\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_root_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    720\u001b[0m \u001b[43m        \u001b[49m\u001b[43minterpolated_subtree\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    721\u001b[0m \u001b[43m        \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_missing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    722\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    724\u001b[0m     output \u001b[38;5;241m=\u001b[39m _tree_to_list(tree\u001b[38;5;241m=\u001b[39mdefaults_tree)\n\u001b[1;32m    725\u001b[0m     ensure_no_duplicates_in_list(output)\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:356\u001b[0m, in \u001b[0;36m_create_defaults_tree\u001b[0;34m(repo, root, is_root_config, skip_missing, interpolated_subtree, overrides)\u001b[0m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_create_defaults_tree\u001b[39m(\n\u001b[1;32m    349\u001b[0m     repo: IConfigRepository,\n\u001b[1;32m    350\u001b[0m     root: DefaultsTreeNode,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    354\u001b[0m     overrides: Overrides,\n\u001b[1;32m    355\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DefaultsTreeNode:\n\u001b[0;32m--> 356\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43m_create_defaults_tree_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    357\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrepo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    358\u001b[0m \u001b[43m        \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    359\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_root_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_root_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    360\u001b[0m \u001b[43m        \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_missing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    361\u001b[0m \u001b[43m        \u001b[49m\u001b[43minterpolated_subtree\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolated_subtree\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    362\u001b[0m \u001b[43m        \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    363\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    365\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:457\u001b[0m, in \u001b[0;36m_create_defaults_tree_impl\u001b[0;34m(repo, root, is_root_config, skip_missing, interpolated_subtree, overrides)\u001b[0m\n\u001b[1;32m    455\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m parent\u001b[38;5;241m.\u001b[39mis_virtual():\n\u001b[1;32m    456\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_root_config:\n\u001b[0;32m--> 457\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_expand_virtual_root\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrepo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    458\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    459\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m root\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:280\u001b[0m, in \u001b[0;36m_expand_virtual_root\u001b[0;34m(repo, root, overrides, skip_missing)\u001b[0m\n\u001b[1;32m    277\u001b[0m new_root \u001b[38;5;241m=\u001b[39m DefaultsTreeNode(node\u001b[38;5;241m=\u001b[39md, parent\u001b[38;5;241m=\u001b[39mroot)\n\u001b[1;32m    278\u001b[0m d\u001b[38;5;241m.\u001b[39mupdate_parent(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 280\u001b[0m subtree \u001b[38;5;241m=\u001b[39m \u001b[43m_create_defaults_tree_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    281\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    282\u001b[0m \u001b[43m    \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_root\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    283\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_root_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43md\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprimary\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    284\u001b[0m \u001b[43m    \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_missing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    285\u001b[0m \u001b[43m    \u001b[49m\u001b[43minterpolated_subtree\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    286\u001b[0m \u001b[43m    \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    287\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m subtree\u001b[38;5;241m.\u001b[39mchildren \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    289\u001b[0m     children\u001b[38;5;241m.\u001b[39mappend(d)\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:573\u001b[0m, in \u001b[0;36m_create_defaults_tree_impl\u001b[0;34m(repo, root, is_root_config, skip_missing, interpolated_subtree, overrides)\u001b[0m\n\u001b[1;32m    570\u001b[0m                 \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m    572\u001b[0m             new_root \u001b[38;5;241m=\u001b[39m DefaultsTreeNode(node\u001b[38;5;241m=\u001b[39md, parent\u001b[38;5;241m=\u001b[39mroot)\n\u001b[0;32m--> 573\u001b[0m             \u001b[43madd_child\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchildren\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_root\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    575\u001b[0m \u001b[38;5;66;03m# processed deferred interpolations\u001b[39;00m\n\u001b[1;32m    576\u001b[0m known_choices \u001b[38;5;241m=\u001b[39m _create_interpolation_map(overrides, defaults_list, self_added)\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:520\u001b[0m, in \u001b[0;36m_create_defaults_tree_impl.<locals>.add_child\u001b[0;34m(child_list, new_root_)\u001b[0m\n\u001b[1;32m    516\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_child\u001b[39m(\n\u001b[1;32m    517\u001b[0m     child_list: List[Union[InputDefault, DefaultsTreeNode]],\n\u001b[1;32m    518\u001b[0m     new_root_: DefaultsTreeNode,\n\u001b[1;32m    519\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 520\u001b[0m     subtree_ \u001b[38;5;241m=\u001b[39m \u001b[43m_create_defaults_tree_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    521\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrepo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    522\u001b[0m \u001b[43m        \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_root_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    523\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_root_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    524\u001b[0m \u001b[43m        \u001b[49m\u001b[43minterpolated_subtree\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolated_subtree\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    525\u001b[0m \u001b[43m        \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_missing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    526\u001b[0m \u001b[43m        \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    527\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    528\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m subtree_\u001b[38;5;241m.\u001b[39mchildren \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    529\u001b[0m         child_list\u001b[38;5;241m.\u001b[39mappend(new_root_\u001b[38;5;241m.\u001b[39mnode)\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:472\u001b[0m, in \u001b[0;36m_create_defaults_tree_impl\u001b[0;34m(repo, root, is_root_config, skip_missing, interpolated_subtree, overrides)\u001b[0m\n\u001b[1;32m    469\u001b[0m     overrides\u001b[38;5;241m.\u001b[39mdelete(parent)\n\u001b[1;32m    470\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m root\n\u001b[0;32m--> 472\u001b[0m \u001b[43moverrides\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_known_choice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    474\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m parent\u001b[38;5;241m.\u001b[39mget_name() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    475\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m root\n",
+      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:185\u001b[0m, in \u001b[0;36mOverrides.set_known_choice\u001b[0;34m(self, default)\u001b[0m\n\u001b[1;32m    183\u001b[0m     prev \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mknown_choices[key]\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m default\u001b[38;5;241m.\u001b[39mget_name() \u001b[38;5;241m!=\u001b[39m prev:\n\u001b[0;32m--> 185\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConfigCompositionException(\n\u001b[1;32m    186\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMultiple values for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    187\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m To override a value use \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moverride \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprev\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    188\u001b[0m         )\n\u001b[1;32m    190\u001b[0m group \u001b[38;5;241m=\u001b[39m default\u001b[38;5;241m.\u001b[39mget_group_path()\n\u001b[1;32m    191\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m group \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mknown_choices_per_group:\n",
+      "\u001b[0;31mConfigCompositionException\u001b[0m: Multiple values for experiment. To override a value use 'override experiment: conv_transformer_paragraphs'"
+     ]
     }
    ],
    "source": [
@@ -168,9 +193,7 @@
    "cell_type": "code",
    "execution_count": 15,
    "id": "e7778ae2",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -236,9 +259,7 @@
    "cell_type": "code",
    "execution_count": 17,
    "id": "9d11ca56",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
diff --git a/notebooks/04-conv-transformer-experiment.ipynb b/notebooks/04-conv-transformer-experiment.ipynb
index 3dd1cf0..9a9be5d 100644
--- a/notebooks/04-conv-transformer-experiment.ipynb
+++ b/notebooks/04-conv-transformer-experiment.ipynb
@@ -108,7 +108,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from text_recognizer.models.transformer import LitTransformer"
+    "from text_recognizer.model.transformer import LitTransformer"
    ]
   },
   {
diff --git a/notebooks/04-conv-transformer.ipynb b/notebooks/04-conv-transformer.ipynb
deleted file mode 100644
index 0d8b370..0000000
--- a/notebooks/04-conv-transformer.ipynb
+++ /dev/null
@@ -1,234 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "7c02ae76-b540-4b16-9492-e9210b3b9249",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ['CUDA_VISIBLE_DEVICE'] = ''\n",
-    "import random\n",
-    "\n",
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import numpy as np\n",
-    "from omegaconf import OmegaConf\n",
-    "import torch\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "from importlib.util import find_spec\n",
-    "if find_spec(\"text_recognizer\") is None:\n",
-    "    import sys\n",
-    "    sys.path.append('..')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ccdb6dde-47e5-429a-88f2-0764fb7e259a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from hydra import compose, initialize\n",
-    "from omegaconf import OmegaConf\n",
-    "from hydra.utils import instantiate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3cf50475-39f2-4642-a7d1-5bcbc0a036f7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = \"../training/conf/network/conv_transformer.yaml\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e52ecb01-c975-4e55-925d-1182c7aea473",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path, \"rb\") as f:\n",
-    "    cfg = OmegaConf.load(f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "f939aa37-7b1d-45cc-885c-323c4540bda1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'_target_': 'text_recognizer.networks.ConvTransformer', 'encoder': {'_target_': 'text_recognizer.networks.image_encoder.ImageEncoder', 'encoder': {'_target_': 'text_recognizer.networks.convnext.ConvNext', 'dim': 16, 'dim_mults': [2, 4, 8], 'depths': [3, 3, 6], 'downsampling_factors': [[2, 2], [2, 2], [2, 2]]}, 'pixel_embedding': {'_target_': 'text_recognizer.networks.transformer.embeddings.axial.AxialPositionalEmbeddingImage', 'dim': 128, 'axial_shape': [7, 128], 'axial_dims': [64, 64]}}, 'decoder': {'_target_': 'text_recognizer.networks.text_decoder.TextDecoder', 'hidden_dim': 128, 'num_classes': 58, 'pad_index': 3, 'decoder': {'_target_': 'text_recognizer.networks.transformer.Decoder', 'dim': 128, 'depth': 10, 'block': {'_target_': 'text_recognizer.networks.transformer.decoder_block.DecoderBlock', 'self_attn': {'_target_': 'text_recognizer.networks.transformer.Attention', 'dim': 128, 'num_heads': 12, 'dim_head': 64, 'dropout_rate': 0.2, 'causal': True}, 'cross_attn': {'_target_': 'text_recognizer.networks.transformer.Attention', 'dim': 128, 'num_heads': 12, 'dim_head': 64, 'dropout_rate': 0.2, 'causal': False}, 'norm': {'_target_': 'text_recognizer.networks.transformer.RMSNorm', 'dim': 128}, 'ff': {'_target_': 'text_recognizer.networks.transformer.FeedForward', 'dim': 128, 'dim_out': None, 'expansion_factor': 2, 'glu': True, 'dropout_rate': 0.2}}, 'rotary_embedding': {'_target_': 'text_recognizer.networks.transformer.RotaryEmbedding', 'dim': 64}}, 'token_pos_embedding': {'_target_': 'text_recognizer.networks.transformer.embeddings.fourier.PositionalEncoding', 'dim': 128, 'dropout_rate': 0.1, 'max_len': 89}}}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cfg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "aaeab329-aeb0-4a1b-aa35-5a2aab81b1d0",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "net = instantiate(cfg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchinfo import summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "7daf1f49",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "==============================================================================================================\n",
-       "Layer (type:depth-idx)                                       Output Shape              Param #\n",
-       "==============================================================================================================\n",
-       "ConvTransformer                                              [1, 58, 89]               --\n",
-       "├─ImageEncoder: 1-1                                          [1, 896, 128]             --\n",
-       "│    └─ConvNext: 2-1                                         [1, 128, 7, 128]          --\n",
-       "│    │    └─Conv2d: 3-1                                      [1, 16, 56, 1024]         800\n",
-       "│    │    └─ModuleList: 3-2                                  --                        --\n",
-       "│    │    │    └─ModuleList: 4-1                             --                        42,400\n",
-       "│    │    │    └─ModuleList: 4-2                             --                        162,624\n",
-       "│    │    │    └─ModuleList: 4-3                             --                        1,089,280\n",
-       "│    │    └─Identity: 3-3                                    [1, 128, 7, 128]          --\n",
-       "│    │    └─LayerNorm: 3-4                                   [1, 128, 7, 128]          128\n",
-       "│    └─AxialPositionalEmbeddingImage: 2-2                    [1, 128, 7, 128]          --\n",
-       "│    │    └─AxialPositionalEmbedding: 3-5                    [1, 896, 128]             8,640\n",
-       "├─TextDecoder: 1-2                                           [1, 58, 89]               --\n",
-       "│    └─Embedding: 2-3                                        [1, 89, 128]              7,424\n",
-       "│    └─PositionalEncoding: 2-4                               [1, 89, 128]              --\n",
-       "│    │    └─Dropout: 3-6                                     [1, 89, 128]              --\n",
-       "│    └─Decoder: 2-5                                          [1, 89, 128]              --\n",
-       "│    │    └─ModuleList: 3-7                                  --                        --\n",
-       "│    │    │    └─DecoderBlock: 4-4                           [1, 89, 128]              525,568\n",
-       "│    │    │    └─DecoderBlock: 4-5                           [1, 89, 128]              525,568\n",
-       "│    │    │    └─DecoderBlock: 4-6                           [1, 89, 128]              525,568\n",
-       "│    │    │    └─DecoderBlock: 4-7                           [1, 89, 128]              525,568\n",
-       "│    │    │    └─DecoderBlock: 4-8                           [1, 89, 128]              525,568\n",
-       "│    │    │    └─DecoderBlock: 4-9                           [1, 89, 128]              525,568\n",
-       "│    │    │    └─DecoderBlock: 4-10                          [1, 89, 128]              525,568\n",
-       "│    │    │    └─DecoderBlock: 4-11                          [1, 89, 128]              525,568\n",
-       "│    │    │    └─DecoderBlock: 4-12                          [1, 89, 128]              525,568\n",
-       "│    │    │    └─DecoderBlock: 4-13                          [1, 89, 128]              525,568\n",
-       "│    │    └─LayerNorm: 3-8                                   [1, 89, 128]              256\n",
-       "│    └─Linear: 2-6                                           [1, 89, 58]               7,482\n",
-       "==============================================================================================================\n",
-       "Total params: 6,574,714\n",
-       "Trainable params: 6,574,714\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (G): 8.45\n",
-       "==============================================================================================================\n",
-       "Input size (MB): 0.23\n",
-       "Forward/backward pass size (MB): 330.38\n",
-       "Params size (MB): 26.30\n",
-       "Estimated Total Size (MB): 356.91\n",
-       "=============================================================================================================="
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary(net, ((1, 1, 56, 1024), (1, 89)), device=\"cpu\", depth=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "25759b7b-8deb-4163-b75d-a1357c9fe88f",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "ename": "RuntimeError",
-     "evalue": "Failed to run torchinfo. See above stack traces for more details. Executed layers up to: [EfficientNet: 1, Sequential: 2, ZeroPad2d: 3, Conv2d: 3, BatchNorm2d: 3, Mish: 3, MBConvBlock: 3, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Sequential: 2, Conv2d: 3, BatchNorm2d: 3, Dropout: 3, Conv2d: 1]",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torchinfo/torchinfo.py:290\u001b[0m, in \u001b[0;36mforward_pass\u001b[0;34m(model, x, batch_dim, cache_forward_pass, device, mode, **kwargs)\u001b[0m\n\u001b[1;32m    289\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[0;32m--> 290\u001b[0m     _ \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mdict\u001b[39m):\n",
-      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1148\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1146\u001b[0m     \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(\u001b[38;5;28minput\u001b[39m)\n\u001b[0;32m-> 1148\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n",
-      "File \u001b[0;32m~/projects/text-recognizer/text_recognizer/networks/conv_transformer.py:132\u001b[0m, in \u001b[0;36mConvTransformer.forward\u001b[0;34m(self, x, context)\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;124;03m\"\"\"Encodes images into word piece logtis.\u001b[39;00m\n\u001b[1;32m    117\u001b[0m \n\u001b[1;32m    118\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;124;03m    Tensor: Sequence of logits.\u001b[39;00m\n\u001b[1;32m    131\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 132\u001b[0m z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    133\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecode(z, context)\n",
-      "File \u001b[0;32m~/projects/text-recognizer/text_recognizer/networks/conv_transformer.py:82\u001b[0m, in \u001b[0;36mConvTransformer.encode\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     81\u001b[0m z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv(z)\n\u001b[0;32m---> 82\u001b[0m z \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpixel_embedding\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     83\u001b[0m z \u001b[38;5;241m=\u001b[39m z\u001b[38;5;241m.\u001b[39mflatten(start_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
-      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1148\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1146\u001b[0m     \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(\u001b[38;5;28minput\u001b[39m)\n\u001b[0;32m-> 1148\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n",
-      "File \u001b[0;32m~/projects/text-recognizer/text_recognizer/networks/transformer/embeddings/axial.py:40\u001b[0m, in \u001b[0;36mAxialPositionalEmbedding.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 40\u001b[0m     b, t, _ \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m     41\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m     42\u001b[0m         t \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_seq_len\n\u001b[1;32m     43\u001b[0m     ), \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSequence length (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mt\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) must be less than the maximum sequence length allowed (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_seq_len\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
-      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 3)",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [22]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msummary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m576\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m640\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m682\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcpu\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdepth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torchinfo/torchinfo.py:218\u001b[0m, in \u001b[0;36msummary\u001b[0;34m(model, input_size, input_data, batch_dim, cache_forward_pass, col_names, col_width, depth, device, dtypes, mode, row_settings, verbose, **kwargs)\u001b[0m\n\u001b[1;32m    211\u001b[0m validate_user_params(\n\u001b[1;32m    212\u001b[0m     input_data, input_size, columns, col_width, device, dtypes, verbose\n\u001b[1;32m    213\u001b[0m )\n\u001b[1;32m    215\u001b[0m x, correct_input_size \u001b[38;5;241m=\u001b[39m process_input(\n\u001b[1;32m    216\u001b[0m     input_data, input_size, batch_dim, device, dtypes\n\u001b[1;32m    217\u001b[0m )\n\u001b[0;32m--> 218\u001b[0m summary_list \u001b[38;5;241m=\u001b[39m \u001b[43mforward_pass\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    219\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_dim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_forward_pass\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    220\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    221\u001b[0m formatting \u001b[38;5;241m=\u001b[39m FormattingOptions(depth, verbose, columns, col_width, rows)\n\u001b[1;32m    222\u001b[0m results \u001b[38;5;241m=\u001b[39m ModelStatistics(\n\u001b[1;32m    223\u001b[0m     summary_list, correct_input_size, get_total_memory_used(x), formatting\n\u001b[1;32m    224\u001b[0m )\n",
-      "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torchinfo/torchinfo.py:299\u001b[0m, in \u001b[0;36mforward_pass\u001b[0;34m(model, x, batch_dim, cache_forward_pass, device, mode, **kwargs)\u001b[0m\n\u001b[1;32m    297\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    298\u001b[0m     executed_layers \u001b[38;5;241m=\u001b[39m [layer \u001b[38;5;28;01mfor\u001b[39;00m layer \u001b[38;5;129;01min\u001b[39;00m summary_list \u001b[38;5;28;01mif\u001b[39;00m layer\u001b[38;5;241m.\u001b[39mexecuted]\n\u001b[0;32m--> 299\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m    300\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to run torchinfo. See above stack traces for more details. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    301\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExecuted layers up to: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexecuted_layers\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    302\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    303\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    304\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m hooks:\n",
-      "\u001b[0;31mRuntimeError\u001b[0m: Failed to run torchinfo. See above stack traces for more details. Executed layers up to: [EfficientNet: 1, Sequential: 2, ZeroPad2d: 3, Conv2d: 3, BatchNorm2d: 3, Mish: 3, MBConvBlock: 3, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Sequential: 2, Conv2d: 3, BatchNorm2d: 3, Dropout: 3, Conv2d: 1]"
-     ]
-    }
-   ],
-   "source": [
-    "summary(net, ((1, 1, 576, 640), (1, 682)), device=\"cpu\", depth=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "248a0cb1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/04-convnext.ipynb b/notebooks/04-convnext.ipynb
index b8d4e56..5ab71c8 100644
--- a/notebooks/04-convnext.ipynb
+++ b/notebooks/04-convnext.ipynb
@@ -77,7 +77,7 @@
     {
      "data": {
       "text/plain": [
-       "{'_target_': 'text_recognizer.networks.convnext.ConvNext', 'dim': 16, 'dim_mults': [2, 4, 8], 'depths': [3, 3, 6], 'downsampling_factors': [[2, 2], [2, 2], [2, 2]], 'attn': {'_target_': 'text_recognizer.networks.convnext.TransformerBlock', 'attn': {'_target_': 'text_recognizer.networks.convnext.Attention', 'dim': 128, 'heads': 4, 'dim_head': 64, 'scale': 8}, 'ff': {'_target_': 'text_recognizer.networks.convnext.FeedForward', 'dim': 128, 'mult': 4}}}"
+       "{'_target_': 'text_recognizer.network.convnext.ConvNext', 'dim': 16, 'dim_mults': [2, 4, 8], 'depths': [3, 3, 6], 'downsampling_factors': [[2, 2], [2, 2], [2, 2]], 'attn': {'_target_': 'text_recognizer.network.convnext.TransformerBlock', 'attn': {'_target_': 'text_recognizer.network.convnext.Attention', 'dim': 128, 'heads': 4, 'dim_head': 64, 'scale': 8}, 'ff': {'_target_': 'text_recognizer.network.convnext.FeedForward', 'dim': 128, 'mult': 4}}}"
       ]
      },
      "execution_count": 38,
diff --git a/notebooks/04-vit-lines.ipynb b/notebooks/04-vit-lines.ipynb
new file mode 100644
index 0000000..b87f38c
--- /dev/null
+++ b/notebooks/04-vit-lines.ipynb
@@ -0,0 +1,305 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7c02ae76-b540-4b16-9492-e9210b3b9249",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['CUDA_VISIBLE_DEVICE'] = ''\n",
+    "import random\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import numpy as np\n",
+    "from omegaconf import OmegaConf\n",
+    "import torch\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "from importlib.util import find_spec\n",
+    "if find_spec(\"text_recognizer\") is None:\n",
+    "    import sys\n",
+    "    sys.path.append('..')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ccdb6dde-47e5-429a-88f2-0764fb7e259a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from hydra import compose, initialize\n",
+    "from omegaconf import OmegaConf\n",
+    "from hydra.utils import instantiate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3cf50475-39f2-4642-a7d1-5bcbc0a036f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = \"../training/conf/network/vit_lines.yaml\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "e52ecb01-c975-4e55-925d-1182c7aea473",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(path, \"rb\") as f:\n",
+    "    cfg = OmegaConf.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "f939aa37-7b1d-45cc-885c-323c4540bda1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'_target_': 'text_recognizer.network.vit.VisionTransformer', 'image_height': 56, 'image_width': 1024, 'patch_height': 28, 'patch_width': 32, 'dim': 256, 'num_classes': 57, 'encoder': {'_target_': 'text_recognizer.network.transformer.encoder.Encoder', 'dim': 256, 'inner_dim': 1024, 'heads': 8, 'dim_head': 64, 'depth': 6, 'dropout_rate': 0.0}, 'decoder': {'_target_': 'text_recognizer.network.transformer.decoder.Decoder', 'dim': 256, 'inner_dim': 1024, 'heads': 8, 'dim_head': 64, 'depth': 6, 'dropout_rate': 0.0}, 'token_embedding': {'_target_': 'text_recognizer.network.transformer.embedding.token.TokenEmbedding', 'num_tokens': 57, 'dim': 256, 'use_l2': True}, 'pos_embedding': {'_target_': 'text_recognizer.network.transformer.embedding.absolute.AbsolutePositionalEmbedding', 'dim': 256, 'max_length': 89, 'use_l2': True}, 'tie_embeddings': True, 'pad_index': 3}"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cfg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "aaeab329-aeb0-4a1b-aa35-5a2aab81b1d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = instantiate(cfg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchinfo import summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "7daf1f49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "====================================================================================================\n",
+       "Layer (type:depth-idx)                             Output Shape              Param #\n",
+       "====================================================================================================\n",
+       "VisionTransformer                                  [1, 57, 89]               --\n",
+       "├─Sequential: 1-1                                  [1, 64, 256]              --\n",
+       "│    └─Rearrange: 2-1                              [1, 64, 896]              --\n",
+       "│    └─LayerNorm: 2-2                              [1, 64, 896]              1,792\n",
+       "│    └─Linear: 2-3                                 [1, 64, 256]              229,632\n",
+       "│    └─LayerNorm: 2-4                              [1, 64, 256]              512\n",
+       "├─Encoder: 1-2                                     [1, 64, 256]              --\n",
+       "│    └─ModuleList: 2-5                             --                        --\n",
+       "│    │    └─ModuleList: 3-1                        --                        --\n",
+       "│    │    │    └─Attention: 4-1                    [1, 64, 256]              525,824\n",
+       "│    │    │    └─FeedForward: 4-2                  [1, 64, 256]              526,080\n",
+       "│    │    └─ModuleList: 3-2                        --                        --\n",
+       "│    │    │    └─Attention: 4-3                    [1, 64, 256]              525,824\n",
+       "│    │    │    └─FeedForward: 4-4                  [1, 64, 256]              526,080\n",
+       "│    │    └─ModuleList: 3-3                        --                        --\n",
+       "│    │    │    └─Attention: 4-5                    [1, 64, 256]              525,824\n",
+       "│    │    │    └─FeedForward: 4-6                  [1, 64, 256]              526,080\n",
+       "│    │    └─ModuleList: 3-4                        --                        --\n",
+       "│    │    │    └─Attention: 4-7                    [1, 64, 256]              525,824\n",
+       "│    │    │    └─FeedForward: 4-8                  [1, 64, 256]              526,080\n",
+       "│    │    └─ModuleList: 3-5                        --                        --\n",
+       "│    │    │    └─Attention: 4-9                    [1, 64, 256]              525,824\n",
+       "│    │    │    └─FeedForward: 4-10                 [1, 64, 256]              526,080\n",
+       "│    │    └─ModuleList: 3-6                        --                        --\n",
+       "│    │    │    └─Attention: 4-11                   [1, 64, 256]              525,824\n",
+       "│    │    │    └─FeedForward: 4-12                 [1, 64, 256]              526,080\n",
+       "│    └─LayerNorm: 2-6                              [1, 64, 256]              512\n",
+       "├─TokenEmbedding: 1-3                              [1, 89, 256]              --\n",
+       "│    └─Embedding: 2-7                              [1, 89, 256]              14,592\n",
+       "├─AbsolutePositionalEmbedding: 1-4                 [89, 256]                 --\n",
+       "│    └─Embedding: 2-8                              [89, 256]                 22,784\n",
+       "├─Decoder: 1-5                                     [1, 89, 256]              --\n",
+       "│    └─ModuleList: 2-9                             --                        --\n",
+       "│    │    └─ModuleList: 3-7                        --                        --\n",
+       "│    │    │    └─Attention: 4-13                   [1, 89, 256]              525,824\n",
+       "│    │    │    └─FeedForward: 4-14                 [1, 89, 256]              526,080\n",
+       "│    │    │    └─Attention: 4-15                   [1, 89, 256]              525,824\n",
+       "│    │    └─ModuleList: 3-8                        --                        --\n",
+       "│    │    │    └─Attention: 4-16                   [1, 89, 256]              525,824\n",
+       "│    │    │    └─FeedForward: 4-17                 [1, 89, 256]              526,080\n",
+       "│    │    │    └─Attention: 4-18                   [1, 89, 256]              525,824\n",
+       "│    │    └─ModuleList: 3-9                        --                        --\n",
+       "│    │    │    └─Attention: 4-19                   [1, 89, 256]              525,824\n",
+       "│    │    │    └─FeedForward: 4-20                 [1, 89, 256]              526,080\n",
+       "│    │    │    └─Attention: 4-21                   [1, 89, 256]              525,824\n",
+       "│    │    └─ModuleList: 3-10                       --                        --\n",
+       "│    │    │    └─Attention: 4-22                   [1, 89, 256]              525,824\n",
+       "│    │    │    └─FeedForward: 4-23                 [1, 89, 256]              526,080\n",
+       "│    │    │    └─Attention: 4-24                   [1, 89, 256]              525,824\n",
+       "│    │    └─ModuleList: 3-11                       --                        --\n",
+       "│    │    │    └─Attention: 4-25                   [1, 89, 256]              525,824\n",
+       "│    │    │    └─FeedForward: 4-26                 [1, 89, 256]              526,080\n",
+       "│    │    │    └─Attention: 4-27                   [1, 89, 256]              525,824\n",
+       "│    │    └─ModuleList: 3-12                       --                        --\n",
+       "│    │    │    └─Attention: 4-28                   [1, 89, 256]              525,824\n",
+       "│    │    │    └─FeedForward: 4-29                 [1, 89, 256]              526,080\n",
+       "│    │    │    └─Attention: 4-30                   [1, 89, 256]              525,824\n",
+       "│    └─LayerNorm: 2-10                             [1, 89, 256]              512\n",
+       "====================================================================================================\n",
+       "Total params: 16,048,128\n",
+       "Trainable params: 16,048,128\n",
+       "Non-trainable params: 0\n",
+       "Total mult-adds (M): 18.03\n",
+       "====================================================================================================\n",
+       "Input size (MB): 0.23\n",
+       "Forward/backward pass size (MB): 46.52\n",
+       "Params size (MB): 64.16\n",
+       "Estimated Total Size (MB): 110.91\n",
+       "===================================================================================================="
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary(net, ((1, 1, 56, 1024), (1, 89)), device=\"cpu\", depth=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1b1a8ac0-bd05-4076-90c2-2de6b740490d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "248a0cb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = net(torch.randn(1, 1, 56, 1024), torch.randint(1, 4, (1, 4)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c251a954-00ac-4680-87e4-f27b6ce06023",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 58, 4])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "02d82c5e-4e67-4f87-a539-393e4cf59b6e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "loss = torch.nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "dc836993-a5d8-43b2-b41c-158a17990075",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(4.0604, grad_fn=<NllLoss2DBackward0>)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss(t.permute(0, 2, 1), torch.randint(0, 58, (1, 89)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "166bf656-aba6-4654-a530-dfce12666297",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Untitled.ipynb b/notebooks/Untitled.ipynb
new file mode 100644
index 0000000..7ea06ae
--- /dev/null
+++ b/notebooks/Untitled.ipynb
@@ -0,0 +1,276 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8468e45a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d1bc956b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.cuda.is_available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "652cfb26",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.cuda.device_count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0fc5e328",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.cuda.current_device()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4ed93c82",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<torch.cuda.device at 0x7fbf7bf7b9a0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.cuda.device(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c03841ce",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'NVIDIA GeForce RTX 2070'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.cuda.get_device_name(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2fba9680-0c73-4a62-b24d-ceea79874717",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'11.7'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.version.cuda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e62de10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = torch.randn((2, 1, 3, 64))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fc221b4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([2, 1, 3, 64])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ab80d75e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch.nn.functional as F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "bfe1fc90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([2, 1, 7, 128])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "F.interpolate(t, scale_factor=[2.5, 2], mode=\"nearest\").shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f69fa1fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = torch.nn.Conv2d(1, 1, 3, 1, padding=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "cd6df204",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([2, 1, 64, 64])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f(torch.randn(2, 1, 64, 64)).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41693566",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Untitled1.ipynb b/notebooks/Untitled1.ipynb
new file mode 100644
index 0000000..06129a3
--- /dev/null
+++ b/notebooks/Untitled1.ipynb
@@ -0,0 +1,567 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "a15a452c-bbbc-4227-90fb-ad573f82c43f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pathlib import Path\n",
+    "import torch\n",
+    "from omegaconf import OmegaConf\n",
+    "from hydra import compose, initialize\n",
+    "from omegaconf import OmegaConf\n",
+    "from hydra.utils import instantiate\n",
+    "from importlib.util import find_spec\n",
+    "if find_spec(\"text_recognizer\") is None:\n",
+    "    import sys\n",
+    "    sys.path.append('..')\n",
+    "\n",
+    "from text_recognizer.data.iam_lines import IAMLines\n",
+    "from text_recognizer.network.transformer.embeddings.sincos import sincos_2d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "42d80280-af2e-44f2-882d-bfd2083961c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = sincos_2d(20, 18, 128, 1e1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "aeb29a14-6dc8-430e-a1ea-6389cfb3cd93",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([360, 128])"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "f39383a7-759c-45f3-933d-061b67aaf33a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ 0.0000,  0.0000,  0.0000,  ...,  1.0000,  1.0000,  1.0000],\n",
+       "        [ 0.8415,  0.8007,  0.7591,  ...,  1.0000,  1.0000,  1.0000],\n",
+       "        [ 0.9093,  0.9594,  0.9883,  ...,  1.0000,  1.0000,  1.0000],\n",
+       "        ...,\n",
+       "        [ 0.6503,  0.9778,  0.3550,  ..., -0.5920, -0.4580, -0.3233],\n",
+       "        [-0.2879,  0.7535,  0.9408,  ..., -0.5920, -0.4580, -0.3233],\n",
+       "        [-0.9614, -0.0750,  0.8698,  ..., -0.5920, -0.4580, -0.3233]])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "10a04966-1c89-4598-9b7d-a66951f5a30a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f156ae4f820>"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 4000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(40, 20))\n",
+    "plt.imshow(t, cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "e61f0a34-2b69-42a7-9d19-3ca607432af0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tt = torch.randn(20 * 18, 128)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "2ec8d2e0-258e-4820-a7f6-41c3223e57f7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f155f2e0580>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 4000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(40, 20))\n",
+    "plt.imshow(tt, cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "28c39912-9de8-4262-b876-4a8a206b00e0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f155f262070>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 4000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(40, 20))\n",
+    "plt.imshow(tt + t, cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "99401335-cc5d-4a2a-a8c5-c918a3b05916",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "import torch\n",
+    "from torch import Tensor\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "\n",
+    "class PositionalEncodingImage(nn.Module):\n",
+    "    \"\"\"\n",
+    "    Module used to add 2-D positional encodings to the feature-map produced by the encoder.\n",
+    "\n",
+    "    Following https://arxiv.org/abs/2103.06450 by Sumeet Singh.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, d_model: int, max_h: int = 2000, max_w: int = 2000, persistent: bool = False) -> None:\n",
+    "        super().__init__()\n",
+    "        self.d_model = d_model\n",
+    "        assert d_model % 2 == 0, f\"Embedding depth {d_model} is not even\"\n",
+    "        pe = self.make_pe(d_model=d_model, max_h=max_h, max_w=max_w)  # (d_model, max_h, max_w)\n",
+    "        self.register_buffer(\n",
+    "            \"pe\", pe, persistent=persistent\n",
+    "        )  # not necessary to persist in state_dict, since it can be remade\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def make_pe(d_model: int, max_h: int, max_w: int) -> torch.Tensor:\n",
+    "        pe_h = PositionalEncoding.make_pe(d_model=d_model // 2, max_len=max_h)  # (max_h, 1 d_model // 2)\n",
+    "        pe_h = pe_h.permute(2, 0, 1).expand(-1, -1, max_w)  # (d_model // 2, max_h, max_w)\n",
+    "\n",
+    "        pe_w = PositionalEncoding.make_pe(d_model=d_model // 2, max_len=max_w)  # (max_w, 1, d_model // 2)\n",
+    "        pe_w = pe_w.permute(2, 1, 0).expand(-1, max_h, -1)  # (d_model // 2, max_h, max_w)\n",
+    "\n",
+    "        pe = torch.cat([pe_h, pe_w], dim=0)  # (d_model, max_h, max_w)\n",
+    "        return pe\n",
+    "\n",
+    "    def forward(self, x: Tensor) -> Tensor:\n",
+    "        \"\"\"pytorch.nn.module.forward\"\"\"\n",
+    "        # x.shape = (B, d_model, H, W)\n",
+    "        assert x.shape[1] == self.pe.shape[0]  # type: ignore\n",
+    "        x = x + self.pe[:, : x.size(2), : x.size(3)]  # type: ignore\n",
+    "        return x\n",
+    "\n",
+    "\n",
+    "class PositionalEncoding(torch.nn.Module):\n",
+    "    \"\"\"Classic Attention-is-all-you-need positional encoding.\"\"\"\n",
+    "\n",
+    "    def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000, persistent: bool = False) -> None:\n",
+    "        super().__init__()\n",
+    "        self.dropout = torch.nn.Dropout(p=dropout)\n",
+    "        pe = self.make_pe(d_model=d_model, max_len=max_len)  # (max_len, 1, d_model)\n",
+    "        self.register_buffer(\n",
+    "            \"pe\", pe, persistent=persistent\n",
+    "        )  # not necessary to persist in state_dict, since it can be remade\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def make_pe(d_model: int, max_len: int) -> torch.Tensor:\n",
+    "        pe = torch.zeros(max_len, d_model)\n",
+    "        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n",
+    "        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n",
+    "        pe[:, 0::2] = torch.sin(position * div_term)\n",
+    "        pe[:, 1::2] = torch.cos(position * div_term)\n",
+    "        pe = pe.unsqueeze(1)\n",
+    "        return pe\n",
+    "\n",
+    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+    "        # x.shape = (S, B, d_model)\n",
+    "        assert x.shape[2] == self.pe.shape[2]  # type: ignore\n",
+    "        x = x + self.pe[: x.size(0)]  # type: ignore\n",
+    "        return self.dropout(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "069a808c-e3e8-4595-87d1-efdd764d1893",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pe = PositionalEncodingImage(4, 20, 18)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "18d5a672-4d39-49b1-9dde-88534aebdf1c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "x = torch.randn(1, 4, 20, 18)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "38c30b5f-73e6-449b-800c-1316b19bf1ec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f155f1e5160>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 4000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(40, 20))\n",
+    "plt.imshow(x.squeeze().flatten(1,2), cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "9d1e12bd-a1b7-432f-9f6e-9becd1fc8ad0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "xx = pe(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "acd5be82-c14f-4e0b-9f0c-0648657706fd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f14a40687c0>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 4000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(40, 20))\n",
+    "plt.imshow(xx.squeeze().flatten(1,2), cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "4033beb9-f18d-4f33-9337-2c873f92fcdf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "xxx = pe.make_pe(128, 20, 18).flatten(1,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "a9b39615-cf7c-4e19-b77b-25d3f1348880",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f149cf1dd90>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 4000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(40, 20))\n",
+    "plt.imshow(xxx, cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "c7c30e67-0cd7-4c23-adcc-56c86450bd37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.device"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(t.device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "a6f270cc-20a2-4aae-8006-cb956eeed44c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "mv =-torch.finfo(t.dtype).max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "390d8a9d-2002-456f-93f9-b4e01b550024",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "a = torch.tensor([1., 1., 2., 2.])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "55efcc9d-9f61-46fb-8417-0a3443332b93",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "b = torch.tensor([1., 1., 2., 2.]) != 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "a0629f46-06b7-42dd-9fd7-d7d9da95faf6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([-3.4028e+38, -3.4028e+38,  2.0000e+00,  2.0000e+00])"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.masked_fill_(b, mv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "516339e8-445a-4459-8fec-f028e3201bce",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ True,  True, False, False])"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0e733d8-c17d-46f5-b484-9c74e46d7308",
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