diff --git a/nlg_train.ipynb b/nlg_train.ipynb
index 1ac16e5..eb21c60 100644
--- a/nlg_train.ipynb
+++ b/nlg_train.ipynb
@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/filnow/anaconda3/envs/jarvis/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "from transformers import (\n",
     "    AutoModelForSeq2SeqLM,\n",
@@ -22,25 +31,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating train split: 20628 examples [00:00, 237850.07 examples/s]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "DatasetDict({\n",
        "    train: Dataset({\n",
        "        features: ['meaning_representation', 'human_reference'],\n",
-       "        num_rows: 18546\n",
+       "        num_rows: 18565\n",
        "    })\n",
        "    test: Dataset({\n",
        "        features: ['meaning_representation', 'human_reference'],\n",
-       "        num_rows: 2061\n",
+       "        num_rows: 2063\n",
        "    })\n",
        "})"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -52,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -61,7 +77,7 @@
      "text": [
       "/home/filnow/anaconda3/envs/jarvis/lib/python3.9/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
       "  warnings.warn(\n",
-      "Map:  75%|███████▌  | 14000/18546 [00:02<00:00, 4940.94 examples/s]\n"
+      "Map:  70%|███████   | 13000/18565 [00:02<00:01, 4762.33 examples/s]\n"
      ]
     },
     {
@@ -71,7 +87,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[5], line 33\u001b[0m\n\u001b[1;32m     29\u001b[0m     tokenized_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     30\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m tokenized_inputs\n\u001b[0;32m---> 33\u001b[0m tokenized_dataset \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\n\u001b[1;32m     34\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtokenize_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     35\u001b[0m \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     36\u001b[0m \u001b[43m    \u001b[49m\u001b[43mremove_columns\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;43mmeaning_representation\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhuman_reference\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     37\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     39\u001b[0m tokenized_dataset\n",
+      "Cell \u001b[0;32mIn[3], line 33\u001b[0m\n\u001b[1;32m     29\u001b[0m     tokenized_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     30\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m tokenized_inputs\n\u001b[0;32m---> 33\u001b[0m tokenized_dataset \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\n\u001b[1;32m     34\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtokenize_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     35\u001b[0m \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     36\u001b[0m \u001b[43m    \u001b[49m\u001b[43mremove_columns\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;43mmeaning_representation\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhuman_reference\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     37\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     39\u001b[0m tokenized_dataset\n",
       "File \u001b[0;32m~/anaconda3/envs/jarvis/lib/python3.9/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    866\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    867\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    868\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DatasetDict(\n\u001b[0;32m--> 869\u001b[0m     {\n\u001b[1;32m    870\u001b[0m         k: dataset\u001b[38;5;241m.\u001b[39mmap(\n\u001b[1;32m    871\u001b[0m             function\u001b[38;5;241m=\u001b[39mfunction,\n\u001b[1;32m    872\u001b[0m             with_indices\u001b[38;5;241m=\u001b[39mwith_indices,\n\u001b[1;32m    873\u001b[0m             with_rank\u001b[38;5;241m=\u001b[39mwith_rank,\n\u001b[1;32m    874\u001b[0m             input_columns\u001b[38;5;241m=\u001b[39minput_columns,\n\u001b[1;32m    875\u001b[0m             batched\u001b[38;5;241m=\u001b[39mbatched,\n\u001b[1;32m    876\u001b[0m             batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m    877\u001b[0m             drop_last_batch\u001b[38;5;241m=\u001b[39mdrop_last_batch,\n\u001b[1;32m    878\u001b[0m             remove_columns\u001b[38;5;241m=\u001b[39mremove_columns,\n\u001b[1;32m    879\u001b[0m             keep_in_memory\u001b[38;5;241m=\u001b[39mkeep_in_memory,\n\u001b[1;32m    880\u001b[0m             load_from_cache_file\u001b[38;5;241m=\u001b[39mload_from_cache_file,\n\u001b[1;32m    881\u001b[0m             cache_file_name\u001b[38;5;241m=\u001b[39mcache_file_names[k],\n\u001b[1;32m    882\u001b[0m             writer_batch_size\u001b[38;5;241m=\u001b[39mwriter_batch_size,\n\u001b[1;32m    883\u001b[0m             features\u001b[38;5;241m=\u001b[39mfeatures,\n\u001b[1;32m    884\u001b[0m             disable_nullable\u001b[38;5;241m=\u001b[39mdisable_nullable,\n\u001b[1;32m    885\u001b[0m             fn_kwargs\u001b[38;5;241m=\u001b[39mfn_kwargs,\n\u001b[1;32m    886\u001b[0m             num_proc\u001b[38;5;241m=\u001b[39mnum_proc,\n\u001b[1;32m    887\u001b[0m             desc\u001b[38;5;241m=\u001b[39mdesc,\n\u001b[1;32m    888\u001b[0m         )\n\u001b[1;32m    889\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    890\u001b[0m     }\n\u001b[1;32m    891\u001b[0m )\n",
       "File \u001b[0;32m~/anaconda3/envs/jarvis/lib/python3.9/site-packages/datasets/dataset_dict.py:870\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    866\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    867\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    868\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DatasetDict(\n\u001b[1;32m    869\u001b[0m     {\n\u001b[0;32m--> 870\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    871\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    872\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    873\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    874\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    875\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    876\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    877\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    878\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    879\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    880\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    881\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    882\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    883\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    884\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    885\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    886\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    887\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    888\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    889\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    890\u001b[0m     }\n\u001b[1;32m    891\u001b[0m )\n",
       "File \u001b[0;32m~/anaconda3/envs/jarvis/lib/python3.9/site-packages/datasets/arrow_dataset.py:602\u001b[0m, in \u001b[0;36mtransmit_tasks.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    600\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    601\u001b[0m \u001b[38;5;66;03m# apply actual function\u001b[39;00m\n\u001b[0;32m--> 602\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    603\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    604\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m datasets:\n\u001b[1;32m    605\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",
@@ -79,7 +95,7 @@
       "File \u001b[0;32m~/anaconda3/envs/jarvis/lib/python3.9/site-packages/datasets/arrow_dataset.py:3156\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   3150\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   3151\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m hf_tqdm(\n\u001b[1;32m   3152\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   3153\u001b[0m         total\u001b[38;5;241m=\u001b[39mpbar_total,\n\u001b[1;32m   3154\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   3155\u001b[0m     ) \u001b[38;5;28;01mas\u001b[39;00m pbar:\n\u001b[0;32m-> 3156\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m rank, done, content \u001b[38;5;129;01min\u001b[39;00m Dataset\u001b[38;5;241m.\u001b[39m_map_single(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdataset_kwargs):\n\u001b[1;32m   3157\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m done:\n\u001b[1;32m   3158\u001b[0m                 shards_done \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
       "File \u001b[0;32m~/anaconda3/envs/jarvis/lib/python3.9/site-packages/datasets/arrow_dataset.py:3547\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   3543\u001b[0m indices \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\n\u001b[1;32m   3544\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   3545\u001b[0m )  \u001b[38;5;66;03m# Something simpler?\u001b[39;00m\n\u001b[1;32m   3546\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3547\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   3548\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3549\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3550\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   3551\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   3552\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3553\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m NumExamplesMismatchError:\n\u001b[1;32m   3554\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m DatasetTransformationNotAllowedError(\n\u001b[1;32m   3555\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   3556\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
       "File \u001b[0;32m~/anaconda3/envs/jarvis/lib/python3.9/site-packages/datasets/arrow_dataset.py:3416\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   3414\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m with_rank:\n\u001b[1;32m   3415\u001b[0m     additional_args \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (rank,)\n\u001b[0;32m-> 3416\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   3417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(processed_inputs, LazyDict):\n\u001b[1;32m   3418\u001b[0m     processed_inputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m   3419\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   3420\u001b[0m     }\n",
-      "Cell \u001b[0;32mIn[5], line 14\u001b[0m, in \u001b[0;36mtokenize_samples\u001b[0;34m(samples)\u001b[0m\n\u001b[1;32m      5\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerate text: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m mr \u001b[38;5;129;01min\u001b[39;00m samples[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmeaning_representation\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\n\u001b[1;32m      7\u001b[0m tokenized_inputs \u001b[38;5;241m=\u001b[39m tokenizer(\n\u001b[1;32m      8\u001b[0m     inputs,\n\u001b[1;32m      9\u001b[0m     max_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m128\u001b[39m,\n\u001b[1;32m     10\u001b[0m     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     11\u001b[0m     truncation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m     12\u001b[0m )\n\u001b[0;32m---> 14\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[43mtokenizer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtext_target\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msamples\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhuman_reference\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     16\u001b[0m \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\n\u001b[1;32m     17\u001b[0m \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     18\u001b[0m \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\n\u001b[1;32m     19\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     21\u001b[0m labels[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     22\u001b[0m     [\n\u001b[1;32m     23\u001b[0m         (token_id \u001b[38;5;28;01mif\u001b[39;00m token_id \u001b[38;5;241m!=\u001b[39m tokenizer\u001b[38;5;241m.\u001b[39mpad_token_id \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m100\u001b[39m)\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m label \u001b[38;5;129;01min\u001b[39;00m labels[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m     27\u001b[0m ]\n\u001b[1;32m     29\u001b[0m tokenized_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",
+      "Cell \u001b[0;32mIn[3], line 14\u001b[0m, in \u001b[0;36mtokenize_samples\u001b[0;34m(samples)\u001b[0m\n\u001b[1;32m      5\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerate text: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m mr \u001b[38;5;129;01min\u001b[39;00m samples[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmeaning_representation\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\n\u001b[1;32m      7\u001b[0m tokenized_inputs \u001b[38;5;241m=\u001b[39m tokenizer(\n\u001b[1;32m      8\u001b[0m     inputs,\n\u001b[1;32m      9\u001b[0m     max_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m128\u001b[39m,\n\u001b[1;32m     10\u001b[0m     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     11\u001b[0m     truncation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m     12\u001b[0m )\n\u001b[0;32m---> 14\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[43mtokenizer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtext_target\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msamples\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhuman_reference\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     16\u001b[0m \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\n\u001b[1;32m     17\u001b[0m \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     18\u001b[0m \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\n\u001b[1;32m     19\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     21\u001b[0m labels[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     22\u001b[0m     [\n\u001b[1;32m     23\u001b[0m         (token_id \u001b[38;5;28;01mif\u001b[39;00m token_id \u001b[38;5;241m!=\u001b[39m tokenizer\u001b[38;5;241m.\u001b[39mpad_token_id \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m100\u001b[39m)\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m label \u001b[38;5;129;01min\u001b[39;00m labels[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m     27\u001b[0m ]\n\u001b[1;32m     29\u001b[0m tokenized_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",
       "File \u001b[0;32m~/anaconda3/envs/jarvis/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:2491\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   2489\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   2490\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_switch_to_target_mode()\n\u001b[0;32m-> 2491\u001b[0m     target_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_target\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_target\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   2492\u001b[0m \u001b[38;5;66;03m# Leave back tokenizer in input mode\u001b[39;00m\n\u001b[1;32m   2493\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_switch_to_input_mode()\n",
       "File \u001b[0;32m~/anaconda3/envs/jarvis/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:2574\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   2569\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   2570\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbatch length of `text`: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(text)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not match batch length of `text_pair`:\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2571\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(text_pair)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2572\u001b[0m         )\n\u001b[1;32m   2573\u001b[0m     batch_text_or_text_pairs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(text, text_pair)) \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;28;01melse\u001b[39;00m text\n\u001b[0;32m-> 2574\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[43mbatch_encode_plus\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2575\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch_text_or_text_pairs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_text_or_text_pairs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2576\u001b[0m \u001b[43m        \u001b[49m\u001b[43madd_special_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43madd_special_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2577\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpadding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2578\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtruncation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtruncation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2579\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmax_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_length\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2580\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstride\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2581\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_split_into_words\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_split_into_words\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2582\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpad_to_multiple_of\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpad_to_multiple_of\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2583\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_tensors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_tensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2584\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_token_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_token_type_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2585\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2586\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_overflowing_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_overflowing_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2587\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_special_tokens_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_special_tokens_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2588\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_offsets_mapping\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_offsets_mapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2589\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_length\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2590\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2591\u001b[0m \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   2592\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2593\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2594\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencode_plus(\n\u001b[1;32m   2595\u001b[0m         text\u001b[38;5;241m=\u001b[39mtext,\n\u001b[1;32m   2596\u001b[0m         text_pair\u001b[38;5;241m=\u001b[39mtext_pair,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2612\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   2613\u001b[0m     )\n",
       "File \u001b[0;32m~/anaconda3/envs/jarvis/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:2765\u001b[0m, in \u001b[0;36mPreTrainedTokenizerBase.batch_encode_plus\u001b[0;34m(self, batch_text_or_text_pairs, 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   2755\u001b[0m \u001b[38;5;66;03m# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'\u001b[39;00m\n\u001b[1;32m   2756\u001b[0m padding_strategy, truncation_strategy, max_length, kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_padding_truncation_strategies(\n\u001b[1;32m   2757\u001b[0m     padding\u001b[38;5;241m=\u001b[39mpadding,\n\u001b[1;32m   2758\u001b[0m     truncation\u001b[38;5;241m=\u001b[39mtruncation,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2762\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   2763\u001b[0m )\n\u001b[0;32m-> 2765\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_batch_encode_plus\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2766\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_text_or_text_pairs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_text_or_text_pairs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2767\u001b[0m \u001b[43m    \u001b[49m\u001b[43madd_special_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43madd_special_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2768\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpadding_strategy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpadding_strategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2769\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtruncation_strategy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtruncation_strategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2770\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_length\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2771\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstride\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2772\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_split_into_words\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_split_into_words\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2773\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpad_to_multiple_of\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpad_to_multiple_of\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2774\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_tensors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_tensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2775\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_token_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_token_type_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2776\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2777\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_overflowing_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_overflowing_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2778\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_special_tokens_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_special_tokens_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2779\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_offsets_mapping\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_offsets_mapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2780\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_length\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2781\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2782\u001b[0m \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   2783\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
@@ -147,7 +163,7 @@
    "outputs": [],
    "source": [
     "training_args = Seq2SeqTrainingArguments(\n",
-    "    output_dir=\"/kaggle/\",\n",
+    "    output_dir=\"/\",\n",
     "    per_device_train_batch_size=8,\n",
     "    per_device_eval_batch_size=16,\n",
     "    predict_with_generate=True,\n",
@@ -228,7 +244,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.save_pretrained(\"/kaggle/output\")"
+    "model.save_pretrained(\"/\")"
    ]
   }
  ],