{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"json\", data_files=\"data-flattened.json\", split=\"train\")\n",
    "\n",
    "labels = [\"datetime\", \"description\", \"location\"]\n",
    "dataset = dataset.train_test_split(test_size=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of T5ForSequenceClassification were not initialized from the model checkpoint at google-t5/t5-small and are newly initialized: ['classification_head.dense.bias', 'classification_head.dense.weight', 'classification_head.out_proj.bias', 'classification_head.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f612c075ba5465b85b56fa25e5c8e91",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/69 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "No chat template is defined for this tokenizer - using a default chat template that implements the ChatML format (without BOS/EOS tokens!). If the default is not appropriate for your model, please set `tokenizer.chat_template` to an appropriate template. See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n",
      "\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'summary'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[60], line 28\u001b[0m\n\u001b[1;32m     25\u001b[0m     model_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m labels[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m model_inputs\n\u001b[0;32m---> 28\u001b[0m tokenized_data_set \u001b[38;5;241m=\u001b[39m \u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreprocess_function\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatched\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     30\u001b[0m \u001b[38;5;66;03m# Training setup (assuming you have data in optimal JSON format)\u001b[39;00m\n\u001b[1;32m     31\u001b[0m training_args \u001b[38;5;241m=\u001b[39m TrainingArguments(\n\u001b[1;32m     32\u001b[0m     output_dir\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcalendar_model\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     33\u001b[0m     evaluation_strategy\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mepoch\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[38;5;66;03m# push_to_hub=True,\u001b[39;00m\n\u001b[1;32m     43\u001b[0m )\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/dataset_dict.py:869\u001b[0m, in \u001b[0;36mDatasetDict.map\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\u001b[0m\n\u001b[1;32m    865\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache_file_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    866\u001b[0m     cache_file_names \u001b[38;5;241m=\u001b[39m {k: \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m}\n\u001b[1;32m    867\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DatasetDict(\n\u001b[1;32m    868\u001b[0m     {\n\u001b[0;32m--> 869\u001b[0m         k: \u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    870\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    871\u001b[0m \u001b[43m            \u001b[49m\u001b[43mwith_indices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwith_indices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    872\u001b[0m \u001b[43m            \u001b[49m\u001b[43mwith_rank\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwith_rank\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    873\u001b[0m \u001b[43m            \u001b[49m\u001b[43minput_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_columns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbatched\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatched\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdrop_last_batch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_last_batch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43mremove_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremove_columns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mkeep_in_memory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_in_memory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m            \u001b[49m\u001b[43mload_from_cache_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mload_from_cache_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcache_file_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_file_names\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[43m            \u001b[49m\u001b[43mwriter_batch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwriter_batch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    882\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdisable_nullable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisable_nullable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfn_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfn_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdesc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdesc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    888\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, dataset \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m    889\u001b[0m     }\n\u001b[1;32m    890\u001b[0m )\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_dataset.py:593\u001b[0m, in \u001b[0;36mtransmit_tasks.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    591\u001b[0m     \u001b[38;5;28mself\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    592\u001b[0m \u001b[38;5;66;03m# apply actual function\u001b[39;00m\n\u001b[0;32m--> 593\u001b[0m out: Union[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDatasetDict\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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    594\u001b[0m datasets: List[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(out\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m [out]\n\u001b[1;32m    595\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m datasets:\n\u001b[1;32m    596\u001b[0m     \u001b[38;5;66;03m# Remove task templates if a column mapping of the template is no longer valid\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_dataset.py:558\u001b[0m, in \u001b[0;36mtransmit_format.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    551\u001b[0m self_format \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    552\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_type,\n\u001b[1;32m    553\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat_kwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_kwargs,\n\u001b[1;32m    554\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_columns,\n\u001b[1;32m    555\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutput_all_columns\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_all_columns,\n\u001b[1;32m    556\u001b[0m }\n\u001b[1;32m    557\u001b[0m \u001b[38;5;66;03m# apply actual function\u001b[39;00m\n\u001b[0;32m--> 558\u001b[0m out: Union[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDatasetDict\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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    559\u001b[0m datasets: List[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(out\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m [out]\n\u001b[1;32m    560\u001b[0m \u001b[38;5;66;03m# re-apply format to the output\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_dataset.py:3105\u001b[0m, in \u001b[0;36mDataset.map\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m   3099\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m transformed_dataset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3100\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m hf_tqdm(\n\u001b[1;32m   3101\u001b[0m         unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m examples\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   3102\u001b[0m         total\u001b[38;5;241m=\u001b[39mpbar_total,\n\u001b[1;32m   3103\u001b[0m         desc\u001b[38;5;241m=\u001b[39mdesc \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMap\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   3104\u001b[0m     ) \u001b[38;5;28;01mas\u001b[39;00m pbar:\n\u001b[0;32m-> 3105\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrank\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdone\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mDataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_single\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdataset_kwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m   3106\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdone\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m   3107\u001b[0m \u001b[43m                \u001b[49m\u001b[43mshards_done\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_dataset.py:3482\u001b[0m, in \u001b[0;36mDataset._map_single\u001b[0;34m(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset)\u001b[0m\n\u001b[1;32m   3478\u001b[0m indices \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\n\u001b[1;32m   3479\u001b[0m     \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m*\u001b[39m(\u001b[38;5;28mslice\u001b[39m(i, i \u001b[38;5;241m+\u001b[39m batch_size)\u001b[38;5;241m.\u001b[39mindices(shard\u001b[38;5;241m.\u001b[39mnum_rows)))\n\u001b[1;32m   3480\u001b[0m )  \u001b[38;5;66;03m# Something simpler?\u001b[39;00m\n\u001b[1;32m   3481\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3482\u001b[0m     batch \u001b[38;5;241m=\u001b[39m \u001b[43mapply_function_on_filtered_inputs\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3483\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3484\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3485\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_same_num_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mshard\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlist_indexes\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3486\u001b[0m \u001b[43m        \u001b[49m\u001b[43moffset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3487\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3488\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m NumExamplesMismatchError:\n\u001b[1;32m   3489\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m DatasetTransformationNotAllowedError(\n\u001b[1;32m   3490\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3491\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_dataset.py:3361\u001b[0m, in \u001b[0;36mDataset._map_single.<locals>.apply_function_on_filtered_inputs\u001b[0;34m(pa_inputs, indices, check_same_num_examples, offset)\u001b[0m\n\u001b[1;32m   3359\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m with_rank:\n\u001b[1;32m   3360\u001b[0m     additional_args \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (rank,)\n\u001b[0;32m-> 3361\u001b[0m processed_inputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfn_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madditional_args\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[43mfn_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3362\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(processed_inputs, LazyDict):\n\u001b[1;32m   3363\u001b[0m     processed_inputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m   3364\u001b[0m         k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m processed_inputs\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m processed_inputs\u001b[38;5;241m.\u001b[39mkeys_to_format\n\u001b[1;32m   3365\u001b[0m     }\n",
      "Cell \u001b[0;32mIn[60], line 23\u001b[0m, in \u001b[0;36mpreprocess_function\u001b[0;34m(examples)\u001b[0m\n\u001b[1;32m     20\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [doc \u001b[38;5;28;01mfor\u001b[39;00m doc \u001b[38;5;129;01min\u001b[39;00m examples[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\n\u001b[1;32m     21\u001b[0m model_inputs \u001b[38;5;241m=\u001b[39m tokenizer(inputs, max_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1024\u001b[39m, truncation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_length\u001b[39m\u001b[38;5;124m\"\u001b[39m) \n\u001b[0;32m---> 23\u001b[0m labels \u001b[38;5;241m=\u001b[39m tokenizer(text_target\u001b[38;5;241m=\u001b[39m\u001b[43mexamples\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msummary\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, max_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m128\u001b[39m, truncation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_length\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     25\u001b[0m model_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m labels[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model_inputs\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/formatting/formatting.py:270\u001b[0m, in \u001b[0;36mLazyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[0;32m--> 270\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    271\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys_to_format:\n\u001b[1;32m    272\u001b[0m         value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'summary'"
     ]
    }
   ],
   "source": [
    "from transformers import (\n",
    "    AutoModelForSequenceClassification,\n",
    "    AutoTokenizer,\n",
    "    Trainer,\n",
    "    TextClassificationPipeline,\n",
    "    TrainingArguments,\n",
    ")\n",
    "\n",
    "# Model and tokenizer selection\n",
    "checkpoint = \"google-t5/t5-small\"  # Ensure correct model name\n",
    "\n",
    "\n",
    "# Configure model for multi-label classification\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    checkpoint, num_labels=len(labels)\n",
    ")\n",
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
    "\n",
    "def preprocess_function(examples):\n",
    "    inputs = [doc for doc in examples[\"message\"]]\n",
    "    model_inputs = tokenizer(inputs, max_length=1024, truncation=True, padding=\"max_length\")\n",
    "\n",
    "    labels = tokenizer(text_target=examples[\"summary\"], max_length=128, truncation=True, padding=\"max_length\")\n",
    "\n",
    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
    "    return model_inputs\n",
    "\n",
    "tokenized_data_set = dataset.map(preprocess_function, batched=True)\n",
    "\n",
    "# Training setup (assuming you have data in optimal JSON format)\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=\"calendar_model\",\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    learning_rate=5e-5,\n",
    "    per_device_train_batch_size=16,\n",
    "    per_device_eval_batch_size=16,\n",
    "    weight_decay=0.01,\n",
    "    save_total_limit=3,\n",
    "    num_train_epochs=1,\n",
    "    use_mps_device=True,\n",
    "    # fp16=True,\n",
    "    # push_to_hub=True,\n",
    ")\n",
    "\n",
    "# Train the model\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=dataset[\"train\"],\n",
    "    eval_dataset=dataset[\"test\"],\n",
    ")\n",
    "trainer.train()\n",
    "\n",
    "# Create pipeline for multi-label prediction\n",
    "pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, labels=labels)\n",
    "\n",
    "# Example usage for multi-label prediction\n",
    "text = \"Meeting with John at 2 pm tomorrow in the conference room\"\n",
    "calendar_entry = pipe(text)\n",
    "\n",
    "print(calendar_entry)  # Output will be a list of dictionaries, one per label\n",
    "\n",
    "# Example: Accessing scores for the \"datetime\" label\n",
    "datetime_predictions = calendar_entry[0]\n",
    "print(datetime_predictions[\"score\"])  # List of prediction scores for \"datetime\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "506a9ad72c324024a186fda4e1fd7156",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/69 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ValueError",
     "evalue": "text input must be of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m tokenized_data_set \u001b[38;5;241m=\u001b[39m \u001b[43mdata_set\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreprocess_function\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatched\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",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/dataset_dict.py:869\u001b[0m, in \u001b[0;36mDatasetDict.map\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\u001b[0m\n\u001b[1;32m    865\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache_file_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    866\u001b[0m     cache_file_names \u001b[38;5;241m=\u001b[39m {k: \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m}\n\u001b[1;32m    867\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DatasetDict(\n\u001b[1;32m    868\u001b[0m     {\n\u001b[0;32m--> 869\u001b[0m         k: \u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    870\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    871\u001b[0m \u001b[43m            \u001b[49m\u001b[43mwith_indices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwith_indices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    872\u001b[0m \u001b[43m            \u001b[49m\u001b[43mwith_rank\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwith_rank\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    873\u001b[0m \u001b[43m            \u001b[49m\u001b[43minput_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_columns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbatched\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatched\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdrop_last_batch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_last_batch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43mremove_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremove_columns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mkeep_in_memory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_in_memory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m            \u001b[49m\u001b[43mload_from_cache_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mload_from_cache_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    880\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcache_file_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_file_names\u001b[49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    881\u001b[0m \u001b[43m            \u001b[49m\u001b[43mwriter_batch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwriter_batch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    882\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdisable_nullable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisable_nullable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfn_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfn_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdesc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdesc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    888\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m k, dataset \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m    889\u001b[0m     }\n\u001b[1;32m    890\u001b[0m )\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_dataset.py:593\u001b[0m, in \u001b[0;36mtransmit_tasks.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    591\u001b[0m     \u001b[38;5;28mself\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    592\u001b[0m \u001b[38;5;66;03m# apply actual function\u001b[39;00m\n\u001b[0;32m--> 593\u001b[0m out: Union[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDatasetDict\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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    594\u001b[0m datasets: List[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(out\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m [out]\n\u001b[1;32m    595\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m datasets:\n\u001b[1;32m    596\u001b[0m     \u001b[38;5;66;03m# Remove task templates if a column mapping of the template is no longer valid\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_dataset.py:558\u001b[0m, in \u001b[0;36mtransmit_format.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    551\u001b[0m self_format \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    552\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_type,\n\u001b[1;32m    553\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat_kwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_kwargs,\n\u001b[1;32m    554\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_columns,\n\u001b[1;32m    555\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutput_all_columns\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_all_columns,\n\u001b[1;32m    556\u001b[0m }\n\u001b[1;32m    557\u001b[0m \u001b[38;5;66;03m# apply actual function\u001b[39;00m\n\u001b[0;32m--> 558\u001b[0m out: Union[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDatasetDict\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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    559\u001b[0m datasets: List[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(out\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m [out]\n\u001b[1;32m    560\u001b[0m \u001b[38;5;66;03m# re-apply format to the output\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_dataset.py:3105\u001b[0m, in \u001b[0;36mDataset.map\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m   3099\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m transformed_dataset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3100\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m hf_tqdm(\n\u001b[1;32m   3101\u001b[0m         unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m examples\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   3102\u001b[0m         total\u001b[38;5;241m=\u001b[39mpbar_total,\n\u001b[1;32m   3103\u001b[0m         desc\u001b[38;5;241m=\u001b[39mdesc \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMap\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   3104\u001b[0m     ) \u001b[38;5;28;01mas\u001b[39;00m pbar:\n\u001b[0;32m-> 3105\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrank\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdone\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mDataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_single\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdataset_kwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m   3106\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdone\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m   3107\u001b[0m \u001b[43m                \u001b[49m\u001b[43mshards_done\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_dataset.py:3482\u001b[0m, in \u001b[0;36mDataset._map_single\u001b[0;34m(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset)\u001b[0m\n\u001b[1;32m   3478\u001b[0m indices \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\n\u001b[1;32m   3479\u001b[0m     \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m*\u001b[39m(\u001b[38;5;28mslice\u001b[39m(i, i \u001b[38;5;241m+\u001b[39m batch_size)\u001b[38;5;241m.\u001b[39mindices(shard\u001b[38;5;241m.\u001b[39mnum_rows)))\n\u001b[1;32m   3480\u001b[0m )  \u001b[38;5;66;03m# Something simpler?\u001b[39;00m\n\u001b[1;32m   3481\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3482\u001b[0m     batch \u001b[38;5;241m=\u001b[39m \u001b[43mapply_function_on_filtered_inputs\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3483\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3484\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3485\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_same_num_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mshard\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlist_indexes\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3486\u001b[0m \u001b[43m        \u001b[49m\u001b[43moffset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3487\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3488\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m NumExamplesMismatchError:\n\u001b[1;32m   3489\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m DatasetTransformationNotAllowedError(\n\u001b[1;32m   3490\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3491\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/datasets/arrow_dataset.py:3361\u001b[0m, in \u001b[0;36mDataset._map_single.<locals>.apply_function_on_filtered_inputs\u001b[0;34m(pa_inputs, indices, check_same_num_examples, offset)\u001b[0m\n\u001b[1;32m   3359\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m with_rank:\n\u001b[1;32m   3360\u001b[0m     additional_args \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (rank,)\n\u001b[0;32m-> 3361\u001b[0m processed_inputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfn_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madditional_args\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[43mfn_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3362\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(processed_inputs, LazyDict):\n\u001b[1;32m   3363\u001b[0m     processed_inputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m   3364\u001b[0m         k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m processed_inputs\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m processed_inputs\u001b[38;5;241m.\u001b[39mkeys_to_format\n\u001b[1;32m   3365\u001b[0m     }\n",
      "Cell \u001b[0;32mIn[5], line 14\u001b[0m, in \u001b[0;36mpreprocess_function\u001b[0;34m(examples)\u001b[0m\n\u001b[1;32m     11\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [doc \u001b[38;5;28;01mfor\u001b[39;00m doc \u001b[38;5;129;01min\u001b[39;00m examples[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\n\u001b[1;32m     12\u001b[0m model_inputs \u001b[38;5;241m=\u001b[39m tokenizer(inputs, max_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m128\u001b[39m, truncation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_length\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 14\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[43mtokenizer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexamples\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlabels\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m128\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtruncation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpadding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_length\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     16\u001b[0m model_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m labels[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model_inputs\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/transformers/tokenization_utils_base.py:2829\u001b[0m, in \u001b[0;36mPreTrainedTokenizerBase.__call__\u001b[0;34m(self, text, text_pair, text_target, text_pair_target, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\u001b[0m\n\u001b[1;32m   2827\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_in_target_context_manager:\n\u001b[1;32m   2828\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_switch_to_input_mode()\n\u001b[0;32m-> 2829\u001b[0m     encodings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_one\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext_pair\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtext_pair\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[43mall_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2830\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m text_target \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[1;32m   2831\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_switch_to_target_mode()\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/transformers/tokenization_utils_base.py:2887\u001b[0m, in \u001b[0;36mPreTrainedTokenizerBase._call_one\u001b[0;34m(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\u001b[0m\n\u001b[1;32m   2884\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m   2886\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_valid_text_input(text):\n\u001b[0;32m-> 2887\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   2888\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext input must be of type `str` (single example), `List[str]` (batch or single pretokenized example) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2889\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor `List[List[str]]` (batch of pretokenized examples).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2890\u001b[0m     )\n\u001b[1;32m   2892\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m text_pair \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_valid_text_input(text_pair):\n\u001b[1;32m   2893\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   2894\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext input must be of type `str` (single example), `List[str]` (batch or single pretokenized example) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2895\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor `List[List[str]]` (batch of pretokenized examples).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2896\u001b[0m     )\n",
      "\u001b[0;31mValueError\u001b[0m: text input must be of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples)."
     ]
    }
   ],
   "source": [
    "tokenized_data_set = data_set.map(preprocess_function, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import DataCollatorForSeq2Seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rouge = evaluate.load(\"rouge\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    predictions, labels = eval_pred\n",
    "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
    "\n",
    "    result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
    "\n",
    "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]\n",
    "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
    "\n",
    "    return {k: round(v, 4) for k, v in result.items()}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model moved to MPS device\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# Check that MPS is available\n",
    "if not torch.backends.mps.is_available():\n",
    "    if not torch.backends.mps.is_built():\n",
    "        print(\"MPS not available because the current PyTorch install was not \"\n",
    "              \"built with MPS enabled.\")\n",
    "    else:\n",
    "        print(\"MPS not available because the current MacOS version is not 12.3+ \"\n",
    "              \"and/or you do not have an MPS-enabled device on this machine.\")\n",
    "\n",
    "else:\n",
    "    mps_device = torch.device(\"mps\")\n",
    "    model.to(mps_device)\n",
    "    print(\"Model moved to MPS device\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/transformers/training_args.py:1951: UserWarning: `use_mps_device` is deprecated and will be removed in version 5.0 of 🤗 Transformers. `mps` device will be used by default if available similar to the way `cuda` device is used.Therefore, no action from user is required. \n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "training_args = Seq2SeqTrainingArguments(\n",
    "    output_dir=\"calendar_model\",\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=16,\n",
    "    per_device_eval_batch_size=16,\n",
    "    weight_decay=0.01,\n",
    "    save_total_limit=3,\n",
    "    num_train_epochs=3,\n",
    "    predict_with_generate=True,\n",
    "    use_mps_device=True,\n",
    "    # fp16=True,\n",
    "    # push_to_hub=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['details', 'message'],\n",
      "        num_rows: 69\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['details', 'message'],\n",
      "        num_rows: 8\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "print(data_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = Seq2SeqTrainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_data_set[\"train\"],\n",
    "    eval_dataset=tokenized_data_set[\"test\"],\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    "    compute_metrics=compute_metrics,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9452caa67e26493eb4c189fd55a68c32",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/transformers/generation/utils.py:1178: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9829c1db68244e7b827c76f106a353a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 14.770042419433594, 'eval_rouge1': 0.2492, 'eval_rouge2': 0.132, 'eval_rougeL': 0.2098, 'eval_rougeLsum': 0.2078, 'eval_gen_len': 18.5, 'eval_runtime': 3.1599, 'eval_samples_per_second': 2.532, 'eval_steps_per_second': 0.316, 'epoch': 1.0}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e8f24890d4848e5958d51b2fad39827",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 13.279829978942871, 'eval_rouge1': 0.191, 'eval_rouge2': 0.0841, 'eval_rougeL': 0.171, 'eval_rougeLsum': 0.1669, 'eval_gen_len': 18.5, 'eval_runtime': 0.6868, 'eval_samples_per_second': 11.648, 'eval_steps_per_second': 1.456, 'epoch': 2.0}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "812b05e2e2234a87ab283f4771f8f615",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 12.672184944152832, 'eval_rouge1': 0.1767, 'eval_rouge2': 0.0792, 'eval_rougeL': 0.1555, 'eval_rougeLsum': 0.1518, 'eval_gen_len': 19.0, 'eval_runtime': 0.6063, 'eval_samples_per_second': 13.195, 'eval_steps_per_second': 1.649, 'epoch': 3.0}\n",
      "{'train_runtime': 12.159, 'train_samples_per_second': 17.024, 'train_steps_per_second': 1.234, 'train_loss': 12.712192789713542, 'epoch': 3.0}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=15, training_loss=12.712192789713542, metrics={'train_runtime': 12.159, 'train_samples_per_second': 17.024, 'train_steps_per_second': 1.234, 'train_loss': 12.712192789713542, 'epoch': 3.0})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7350298fb1d24de696d2fdce2b167cb7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bdd17cc0c7624ab0babcf12b19157c75",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/242M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "040b39bf6bdc4e939cf56a47e1f4451e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "training_args.bin:   0%|          | 0.00/4.98k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/joshcarp/calendar_model/commit/ef13304ccc7e109ab97007e944f01405ce9b1409', commit_message='End of training', commit_description='', oid='ef13304ccc7e109ab97007e944f01405ce9b1409', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.push_to_hub()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "convert to summary: Doctor's appointment on Friday at 9:00 AM.\n",
      "[{'generated_text': \"Umgekehrt: Doctor's appointment on Friday at 9:00 AM.\"}]\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "hub_model_id = \"joshcarp/calendar_model\"\n",
    "summarizer = pipeline(\"textclassificationpipeline\", model=hub_model_id)\n",
    "text = \"convert to summary: Doctor's appointment on Friday at 9:00 AM.\"\n",
    "summary = summarizer(text, max_length=50, min_length=6)\n",
    "print(text)\n",
    "print(summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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