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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Processing an existing dataset for long-form question answering to filter out overly long answers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Cut off value for the maximum number of tokens in the answer\n",
"MAX_TOKENS_ANSWER = 512"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['question', 'answer', 'context'],\n",
" num_rows: 226147\n",
" })\n",
" validation: Dataset({\n",
" features: ['question', 'answer', 'context'],\n",
" num_rows: 3020\n",
" })\n",
"})"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import datasets\n",
"\n",
"# Long-form question answering dataset, nicely preprocessed already.\n",
"# Similar to ELI5: https://facebookresearch.github.io/ELI5/index.html (which is unavailable now)\n",
"dataset_lfqa = datasets.load_dataset(\"LLukas22/lfqa_preprocessed\")\n",
"dataset_lfqa"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>question</th>\n",
" <th>answer</th>\n",
" <th>context</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import random\n",
"import pandas as pd\n",
"from IPython.display import display, HTML\n",
"\n",
"def show_random_elements(dataset, num_examples=4):\n",
" assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
" picks = []\n",
" for _ in range(num_examples):\n",
" pick = random.randint(0, len(dataset)-1)\n",
" while pick in picks:\n",
" pick = random.randint(0, len(dataset)-1)\n",
" picks.append(pick)\n",
"\n",
" df = pd.DataFrame(dataset[picks])\n",
" for column, typ in dataset.features.items():\n",
" if isinstance(typ, datasets.ClassLabel):\n",
" df[column] = df[column].transform(lambda i: typ.names[i])\n",
" display(HTML(df.to_html()))\n",
"\n",
"show_random_elements(dataset_lfqa[\"train\"], num_examples=0)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"\n",
"answer_str_lens = [len(answer) for answer in dataset_lfqa[\"train\"][\"answer\"]]\n",
"sns_plot = sns.histplot(answer_str_lens).set_title(\"Answer lengths in characters\")\n",
"sns_plot.get_figure().savefig(\"plots/answer-lengths-chars-original.png\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"26123"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Max answer length in characters --> few answers are very long --> long-tail distribution\n",
"max(answer_str_lens)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 226147/226147 [01:47<00:00, 2106.62it/s]\n"
]
}
],
"source": [
"# Check max length (in tokens) of answers in the dataset\n",
"from transformers import AutoTokenizer\n",
"from tqdm import tqdm\n",
"import torch\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"allenai/led-base-16384\")\n",
"\n",
"answer_token_lens = torch.tensor(\n",
" [\n",
" len(tokenizer(answer)[\"input_ids\"])\n",
" for answer in tqdm(dataset_lfqa[\"train\"][\"answer\"])\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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QCuO5NEeKmqv0WBqS7YcBTT2gpplwqhnTr1jV7Ki2TAEyP6dV0VB7NZlpHh7VtKhfhGYu1pwwOpAVJb+WT52nFVx0oNVnQYFG/dd0wNYB9/TTT3flowOwml2jpxoI/2xqHQUqhXeFTB2YdfDV+6bPkB5fn1/VkqjJUGUeHbxyo/5XChZqZlS5qU+T5jxSsMytRkDzbul15TTcXYFNcyhFUxOgaqB8Csp+bVm4s88+O6KTs74rqnlVU5KeT7Uw6jiscKDPT176PB3s86bt9Oez0rbp+6waMgVC1dYdzLBhw1w/nb59+7ofQ5o4sii+v5rOQqcsUf8hfUbC6fOk4KJO3xpCryY9fe8U+hRiVZOtclNznz5XeaGy0evI61QIOAIFOGINiKAh4GXKlPH27t170HVuuOEGLyUlxfv1119Dw+5zGk6v5RoOLlpXw4AbN27shvJqyHPr1q29KVOmZPu7kSNHur/t06dPxHIN79by2bNnZ/ubtLQ0N8RVw3o1BLlKlSpuqPTgwYO9nTt35nk4cm7D7gviuaKHr4tek4Z3a0jx8ccf773yyituGLnei3Br1qxxw5rLli3rHt9/HH/YffRw+ujhx3qeyy+/3Ktbt657Ll1fc8013rp16w572L2GVIebO3euW67rQ9Gw9TvvvNOrUaOGG04fvmvTcHkN39f26bN24oknus9Y+PD+g5WxpnzQlAVdu3b1MjMzQ9vUsWNH97lTmaqM9TnW9BK5DX33y9b31ltveR06dPBq1qzpylDDt2+99VY3rDw3W7Zscdumoebh/CH/OV20rbkNu9dF94eXi3/RkHZNPaDPl4bb53XKiUN93mTZsmWuTCtUqOCmjbjgggu8zz//POLvc/qM6D3RZ07loCkWCuM7Ff25P1T5Rn8Xx44d655br1nTCGho/sCBA91Q+vDn69SpU45lpukm9LnQdxiFL0H/HEmgAhD/1NdBE+D5I6VQMmiySdXe+BOMomTRZI+qJVMT/5H2RULu6EMElDB+nxefQpD6xKijMEoWzeitAQNHMjs44pOaPdW8qKY5wlDRoIYIKGE0qZ3OqaQ+IOqTon4v6o+hUwaceOKJsd48AIhLdKoGShh1cH3jjTfcCXA1iaU6eWpkFGEIAA6OGiIAABB49CECAACBRyACAACBRx+iPNB0+pr5V5N/cYI9AACKB/UK0gTBmgw0t5NOE4jyQGEo+nw6AACgeNi4caObDf5QCER54J+0UAWa03l1AABA/NGpZlShkdvJh4VAlAd+M5nCEIEIAIDiJS/dXehUDQAAAo9ABAAAAo9ABAAAAo9ABAAAAo9ABAAAAo9ABAAAAo9ABAAAAo9ABAAAAo9ABAAAAo9ABAAAAo9ABAAAAo9ABAAAAo9ABAAAAo9ABAAAAo9AFGfS0tLcBQAAFB0CEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCUZzwPM+dskPXAACgaBGI4kR6erp1HTnXXQMAgKJFIIojiUnJsd4EAAACiUAEAAACj0AEAAACj0AEAAACj0AEAAACj0AEAAACj0AEAAACj0AEAAACj0AEAAACj0AEAAACL6aBKDMz0x566CFr2LChlS1b1o4//nj7xz/+EXE+L/3/4Ycftjp16rh12rdvb+vXr494nO3bt1v37t2tYsWKVrlyZevVq5ft2bMnYp0VK1bYeeedZ2XKlLF69erZ008/XWSvEwAAxLeYBqKnnnrKRo8ebSNGjLBvvvnG3VZQGT58eGgd3X7xxRdtzJgxtmjRIitfvrx17NjR9u/fH1pHYWj16tU2a9Yse//99+3TTz+1W265JXT/rl27rEOHDtagQQP78ssvbdiwYfboo4/a2LFji/w1AwCAOOTFUKdOnbybbropYtmVV17pde/e3f0/KyvLq127tjds2LDQ/ampqV7p0qW9N954w93++uuvVZ3kLVmyJLTO9OnTvYSEBG/Tpk3u9qhRo7wqVap4Bw4cCK1z3333eY0aNcrTdu7cudM9h64Li7btyhc+9nbv3h2xnQAA4PDk5/gd0xqis88+22bPnm3r1q1zt7/66iv77LPP7OKLL3a3N2zYYJs3b3bNZL5KlSpZ69atbeHChe62rtVM1rJly9A6Wj8xMdHVKPnrtG3b1kqVKhVaR7VMa9eutR07dmTbrgMHDrhapfALAAAouWJ6evX777/fhY3GjRtbUlKS61P0+OOPuyYwURiSWrVqRfydbvv36bpmzZoR9ycnJ1vVqlUj1lE/pejH8O+rUqVKxH1Dhw61wYMHF/jrBQAA8SmmNURTpkyxCRMm2MSJE23ZsmU2fvx4++c//+muY2nQoEG2c+fO0GXjxo0x3R4AAFCCa4gGDBjgaom6devmbjdt2tR+/PFHV0PTs2dPq127tlu+ZcsWN8rMp9vNmjVz/9c6W7dujXjcjIwMN/LM/3td62/C+bf9dcKVLl3aXQAAQDDEtIbo999/d319wqnpLCsry/1fzVwKLOpn5FMTm/oGtWnTxt3WdWpqqhs95pszZ457DPU18tfRyLP09PTQOhqR1qhRo2zNZQAAIHhiGoguu+wy12fogw8+sB9++MGmTp1qzz77rF1xxRXu/oSEBOvXr5899thjNm3aNFu5cqVdf/31VrduXevcubNbp0mTJnbRRRdZ7969bfHixbZgwQK74447XK2T1pNrr73WdajW/EQanj958mR74YUXrH///rF8+QAAIE7EtMlM8w1pYsbbb7/dNXspwNx6661uIkbfwIEDbe/evW5eIdUEnXvuuTZjxgw3waJP/ZAUgtq1a+dqnLp06eLmLgofmfbRRx9Z3759rUWLFla9enX3HOFzFcWTtLQ0dx0+Kg4AABSeBI29L8THLxHUTKdQpQ7Wmg27sELQNWPm2/ibWofCEIEIAICiOX5zLjMAABB4BCIAABB4BCIAABB4BCIAABB4BCIAABB4BCIAABB4BCIAABB4BCIAABB4BKI4ojkyNUEjc2UCAFC0CERxxMvMsBteXhBxEloAAFD4CERxJjEppqeXAwAgkAhEAAAg8AhEAAAg8AhEAAAg8AhEAAAg8AhEAAAg8AhEAAAg8AhEAAAg8AhEAAAg8AhEAAAg8JgWOY5kZaSbJZBRAQAoahx9AQBA4BGIAABA4BGIAABA4BGIAABA4BGIAABA4BGIAABA4BGIAABA4BGIAABA4BGI4ozneZaWluauAQBA0SAQxRkvM8N6j1tk6enpsd4UAAACg0AUhxKTOKMKAABFiUAEAAACj0AEAAACj0AEAAACj0AEAAACj0AEAAACj0AUJ9zcQ1nMPQQAQCwQiAAAQOARiAAAQOARiAAAQOARiAAAQOARiAAAQOARiAAAQOARiAAAQOARiAAAQOARiOKU53l/TNboMVkjAACFjUAUp9LT063ryLnuGgAAFC4CURxLTEqO9SYAABAIBCIAABB4BCIAABB4BCIAABB4BCIAABB4BCIAABB4BCIAABB4BCIAABB4BCIAABB4BCIAABB4BCIAABB4BCIAABB4BCIAABB4BKI45HmepaWlxXozAAAIDAJRHPIyM6z3uEXmZXmx3hQAAAKBQBSnEpOSY70JAAAEBoEoDtBEBgBAbFENEQfS09Ot50vzzCwh1psCAEAgUUMUJ2giAwAgdghEAAAg8AhEAAAg8AhEAAAg8AhEAAAg8GIeiDZt2mQ9evSwatWqWdmyZa1p06a2dOnSiCHpDz/8sNWpU8fd3759e1u/fn3EY2zfvt26d+9uFStWtMqVK1uvXr1sz549EeusWLHCzjvvPCtTpozVq1fPnn766SJ7jQAAIL7FNBDt2LHDzjnnHEtJSbHp06fb119/bc8884xVqVIltI6Cy4svvmhjxoyxRYsWWfny5a1jx462f//+0DoKQ6tXr7ZZs2bZ+++/b59++qndcsstoft37dplHTp0sAYNGtiXX35pw4YNs0cffdTGjh1b5K8ZAADEnwRPVTAxcv/999uCBQts/vz5Od6vTatbt67de++99re//c0t27lzp9WqVcvGjRtn3bp1s2+++cZOPvlkW7JkibVs2dKtM2PGDLvkkkvsv//9r/v70aNH2wMPPGCbN2+2UqVKhZ77nXfesTVr1mR73gMHDrhLeKBSrZKeW7VQBU2TMl79/EzLyswMLUtMTrGklNI26fa2oW0GAAB5p+N3pUqV8nT8jmkN0bRp01yIufrqq61mzZrWvHlze/nll0P3b9iwwYUYNZP59MJat25tCxcudLd1rWYyPwyJ1k9MTHQ1Sv46bdtGBgvVMq1du9bVUkUbOnSoex7/ojAEAABKrpgGou+//97V3px44ok2c+ZM69Onj9111102fvx4d7/CkKhGKJxu+/fpWmEqXHJyslWtWjVinZweI/w5wg0aNMilSf+ycePGAn3dAAAgvsR0euSsrCxXs/PEE0+426ohWrVqlesv1LNnz5htV+nSpd0llrIy0l2zGQAAKOE1RBo5pv4/4Zo0aWI//fST+3/t2rXd9ZYtWyLW0W3/Pl1v3bo14v6MjAw38ix8nZweI/w5AABAcMU0EGmEmfrxhFu3bp0bDSYNGzZ0gWX27NkRHaTUN6hNmzbutq5TU1Pd6DHfnDlzXO2T+hr562jkmU6i6tOItEaNGkWMaAMAAMEU00B0zz332BdffOGazL799lubOHGiGwrft29fd39CQoL169fPHnvsMdcBe+XKlXb99de7kWOdO3cO1ShddNFF1rt3b1u8eLEbtXbHHXe4EWhaT6699lrXoVrzE2l4/uTJk+2FF16w/v37x/LlAwCAOBHTPkStWrWyqVOnuk7MQ4YMcTVCzz//vJtXyDdw4EDbu3evm1dINUHnnnuuG1avCRZ9EyZMcCGoXbt2bnRZly5d3NxFPo0U++ijj1zQatGihVWvXt1N9hg+VxEAAAiumM5DVBLnMSioeYgkuUw5m9z3T8xDBABASZ6HCAAAIB4QiAAAQOARiAAAQOARiAAAQOARiAAAQOARiAAAQOARiAAAQOARiAAAQOARiAAAQOARiAAAQOARiAAAQOARiOKcznOmCwAAKDwEIgAAEHgEIgAAEHgEIgAAEHgEIgAAEHgEIgAAEHgEIgAAEHgEIgAAEHgEIgAAEHgEojiWlZHOpIwAABQBAhEAAAg8AlEc8zzP1RDpGgAAFB4CURzzMjOs97hFlp6eHutNAQCgRCMQxbnEpORYbwIAACUegQgAAAQegQgAAAQegSgOuI7TWXScBgAgVghEAAAg8AhEAAAg8AhEAAAg8AhEAAAg8AhEAAAg8AhEAAAg8A4rEB133HH222+/ZVuemprq7gMAACjxgeiHH36wzMzMbMsPHDhgmzZtKojtAgAAKDL5OlHWtGnTQv+fOXOmVapUKXRbAWn27Nl27LHHFuwWAgAAxFMg6ty5s7tOSEiwnj17RtyXkpLiwtAzzzxTsFsIAAAQT4EoKyvLXTds2NCWLFli1atXL6ztAgAAiM9A5NuwYUPBbwkAAEBxCkSi/kK6bN26NVRz5PvXv/5VENsGAAAQv4Fo8ODBNmTIEGvZsqXVqVPH9SkCAAAIVCAaM2aMjRs3zq677rqC3yIAAIDiMA9RWlqanX322QW/NciR53muzHUNAADiJBDdfPPNNnHixILfGuQoPT3duo6c664BAECcNJnt37/fxo4dax9//LGddtppbg6icM8++2xBbR/+T2LSYfd/BwAAuTiso+yKFSusWbNm7v+rVq2KuI8O1gAAIBCBaO7cuQW/JQAAAMWpDxEAAIAFvYboggsuOGTT2Jw5c45kmwAAAOI/EPn9h3wa/bR8+XLXnyj6pK8AAAAlMhA999xzOS5/9NFHbc+ePUe6TQAAAMW3D1GPHj04j1khTcoIAACKSSBauHChlSlTpiAfMvC8zAzrPW6ReVnMUg0AQFw1mV155ZXZajF++eUXW7p0qT300EMFtW34P0zKCABA4TqsI22lSpUibicmJlqjRo1syJAh1qFDh4LaNgAAgPgNRK+++mrBbwkAAECMHFFbzJdffmnffPON+/8pp5xizZs3L6jtAgAAiO9AtHXrVuvWrZt98sknVrlyZbcsNTXVTdg4adIkq1GjRkFvJwAAQHyNMrvzzjtt9+7dtnr1atu+fbu7aFLGXbt22V133VXwWxlwWRnp5nlZsd4MAABKrMOqIZoxY4Z9/PHH1qRJk9Cyk08+2UaOHEmnagAAEIwaoqysLEtJScm2XMt0HwAAQIkPRBdeeKHdfffd9vPPP4eWbdq0ye655x5r165dQW4fAABAfAaiESNGuP5Cxx57rB1//PHu0rBhQ7ds+PDhBb+VCJ3CQ9cAACAO+hDVq1fPli1b5voRrVmzxi1Tf6L27dsX8OYh/BQePV76zKbceaGVKlUq1psDAEBwa4jmzJnjOk+rJighIcH+/Oc/uxFnurRq1crNRTR//vzC29qA4xQeAADEQSB6/vnnrXfv3laxYsUcT+dx66232rPPPluQ2wcAABBfgeirr76yiy666KD3a8i9Zq8GAAAosYFoy5YtOQ639yUnJ9u2bdsKYrsAAADiMxAdffTRbkbqg1mxYoXVqVOnILYLAAAgPgPRJZdcYg899JDt378/23379u2zRx55xC699NLD2pAnn3zSddTu169faJmep2/fvlatWjWrUKGCdenSxdVShfvpp5+sU6dOVq5cOatZs6YNGDDAMjIyItbROdfOOOMMK126tJ1wwgk2bty4w9pGAABQMuUrED344IPuvGUnnXSSPf300/buu++6y1NPPWWNGjVy9z3wwAP53oglS5bYSy+9ZKeddlrEck30+N5779mbb75p8+bNcxNBXnnllaH7MzMzXRjS/Dyff/65jR8/3oWdhx9+OLTOhg0b3Do68ezy5ctd4Lr55ptt5syZ+d5OAABQMiV4+Zzp78cff7Q+ffq4QOH/qWp2Onbs6M5lpgka82PPnj2u9mbUqFH22GOPWbNmzdxotp07d1qNGjVs4sSJdtVVV7l1NeeR5jtauHChnXXWWTZ9+nRXI6WgVKtWLbfOmDFj7L777nN9mTRfj/7/wQcfRDT1devWzVJTU9052fJC0wxoFJ22KacRdkdKZXDtiDmHPIFrYnKKJaWUtkm3t2UeIgAACvj4ne+Zqhs0aGAffvih/frrr7Zo0SL74osv3P+1LL9hSNQkphqc6EkdNVotPT09Ynnjxo2tfv36LhCJrps2bRoKQ6JgpgJYvXp1aJ3ox9Y6/mPk5MCBA+4xwi+xxhnvAQAoPIc901+VKlXcZIxHYtKkSW7GazWZRdu8ebOrCalcuXLEcoUf3eevEx6G/Pv9+w61jkKO+j2VLVs223MPHTrUBg8efESvDQAAlPBzmRWEjRs3uhPETpgwwcqUKWPxZNCgQa56zb9oWwEAQMkVs0CkJrGtW7e6/kOav0gXdZx+8cUX3f9Vi6PO0urrE06jzGrXru3+r+voUWf+7dzWUVtiTrVDotFouj/8AgAASq6YBaJ27drZypUr3cgv/9KyZUvr3r176P+aBHL27Nmhv1m7dq0bZt+mTRt3W9d6DAUr36xZs1yA0TnX/HXCH8Nfx38MAACAmJ0t9KijjrJTTz01Yln58uXdnEP+8l69eln//v2tatWqLuToJLIKMhph5p8qRMHnuuuuc9MAqL+QpgZQR23V8shtt91mI0aMsIEDB9pNN93kTlA7ZcoUN/IMAABA4vr06c8995wlJia6CRk18kujwzQ835eUlGTvv/++mwZAQUmBqmfPnjZkyJDQOhr5pvCjOY1eeOEFO+aYY+yVV15xjwUAAHBY8xAFUTzMQyTJZcrZ5L5/Yh4iAABiPQ8RAABASUMgAgAAgUcgAgAAgUcgAgAAgUcgAgAAgUcgAgAAgUcgAgAAgUcgAgAAgUcgAgAAgUcgAgAAgUcgKmbS0tLcBQAAFBwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCUTGSlZHOaTsAACgEBCIAABB4BCIAABB4BCIAABB4BCIAABB4BKJixPM816la1wAAoOAQiIoRLzPDeo9bZOnp6bHeFAAAShQCUTGTmJTsaokYfg8AQMEhEMVJMxgAAIid5Bg+N8xc81fPl+aZWUKsNwUAgMCihihOmsEAAEDsEIgAAEDgEYgAAEDgEYgAAEDgEYgAAEDgEYgAAEDgEYiKmayMdOYtAgCggBGIAABA4BGIAABA4BGIihnOeA8AQMEjEBXDM97f/voyzngPAEABIhAVQ4nJnOoDAICCRCACAACBRyACAACBRyACAACBRyACAACBRyACAACBRyACAACBRyACAACBRyAqppixGgCAgkMgKoYUgvbu3WtdR85lxmoAAAoAgaiYnr6j97hFlpCQFOtNAQCgRCAQFVOJSZy+AwCAgkIgAgAAgUcgAgAAgUcgKqayMtLN87JivRkAAJQIBCIAABB4BCIAABB4BKJijMkZAQAoGASiYj4fUY+XPmNyRgAAjhCBqJhjPiIAAI4cgQgAAAQegQgAAAQegQgAAAQegQgAAAQegQgAAAQegagY4/QdAAAUDAIRAAAIPAIRAAAIvJgGoqFDh1qrVq3sqKOOspo1a1rnzp1t7dq1Eevs37/f+vbta9WqVbMKFSpYly5dbMuWLRHr/PTTT9apUycrV66ce5wBAwZYRkZGxDqffPKJnXHGGVa6dGk74YQTbNy4cVZS6PQdugAAgGIYiObNm+fCzhdffGGzZs1yp6Do0KGD7d27N7TOPffcY++99569+eabbv2ff/7ZrrzyytD9mZmZLgwpEHz++ec2fvx4F3Yefvjh0DobNmxw61xwwQW2fPly69evn9188802c+bMIn/NAAAg/iR4cXRm0G3btrkaHgWftm3b2s6dO61GjRo2ceJEu+qqq9w6a9assSZNmtjChQvtrLPOsunTp9ull17qglKtWrXcOmPGjLH77rvPPV6pUqXc/z/44ANbtWpV6Lm6detmqampNmPGjFy3a9euXVapUiW3PRUrVizQ16wgd/XzMy0rMzPPHakTkpItISHB3U4uU85e6322e526AACA/B+/46oPkTZYqlat6q6//PJLV2vUvn370DqNGze2+vXru0Akum7atGkoDEnHjh1dIaxevTq0Tvhj+Ov4jxHtwIED7u/DLwAAoOSKm0CUlZXlmrLOOeccO/XUU92yzZs3u1qPypUrR6yr8KP7/HXCw5B/v3/fodZR0Nm3b1+OfZuUKP1LvXr1CvjVAgCAeBI3gUh9idSkNWnSpFhvig0aNMjVVvmXjRs3xnqTAABAIUq2OHDHHXfY+++/b59++qkdc8wxoeW1a9d2fWzU1ye8lkijzHSfv87ixYsjHs8fhRa+TvTINN1We2LZsmWzbY9GoukCAACCIaY1ROrPrTA0depUmzNnjjVs2DDi/hYtWlhKSorNnj07tEzD8jXMvk2bNu62rleuXGlbt24NraMRawo7J598cmid8Mfw1/EfozhTGSo0xlHfeAAAip3EWDeTvf76624UmeYiUl8fXfx+Peq/06tXL+vfv7/NnTvXdbK+8cYbXZDRCDPRMH0Fn+uuu86++uorN5T+wQcfdI/t1/Lcdttt9v3339vAgQPdKLVRo0bZlClT3JD+4i7zwD7rOXa+63wOAACKYSAaPXq066Pzpz/9yerUqRO6TJ48ObTOc88954bVa0JGDcVX89fbb78duj8pKck1t+laQalHjx52/fXX25AhQ0LrqOZJw+5VK3T66afbM888Y6+88oobaVYSJCYlU1MEAEBJmYcoXsXzPESSmJxir/ZqY73GL7HJfS9gPiIAAKwYz0OEI68pAgAA+UcgAgAAgUcgAgAAgUcgKgH8DtUAAODwEIhKAC8zw3qPW2ReFv3jAQA4HASiEoIO1QAAHD4CUQnCXEQAABweAlEJazrr8dJnzFoNAEA+EYhKCE3Y6HlZNJ0BAHAYCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQAACDwCEQlcOi9Jmfk3GYAAOQdgQgAAAQegQgAAAQegaiEyUxPsz179nA+MwAA8oFAVALPZ9Z73CLOZwYAQD4QiEognc9MNUTqWE1NEQAAuSMQlVCqIeo6ci41RQAA5AGBqATya4dUUwQAAHJHIIox16yV5RVKP6KCflwAAEoqAlEJRe0QAAB5RyACAACBRyACAACBRyAqoTivGQAAeUcgCgBCEQAAh0YgKuG1RAQhAAByRyAqwZitGgCAvCEQlWCc1wwAgLwhEJVwzEcEAEDuCEQBQcdqAAAOjkAEAAACj0BUwtGxGgCA3BGISjg6VgMAkDsCUQB4WVm2Z88ey8r6Y+ZqaosAAIhEIApILdHtry+z33//3bqOnEttEQAAUQhEAZGY/Mfwe4bhAwCQHYEIAAAEHoEoYKPNhDmJAACIRCAK2GgzL4sO1QAARKNDSYCo/1D0vESlSpWK9WYBABBz1BAFSFZGumVlpNkNLy9gpBkAAGEIRAHkjzRjFmsAAP5AIApgLZHnZbn/q5aIeYkAAKAPUSCpRujAgQPumnmJAAAgEAV2xNnN/2+BJSanWEqZ8rHeHAAAYo4ms4BSzRC1QwAA/IFAFPRRZ1mZ7sSv+/fvp4M1ACCwCEQBl3lgn/UcO99SU1Ptr8Pn0MEaABBIBCJYQmKSqx3yr6klAgAEDYEIrpN1n38vdpM29njpM2qJAACBQyCCow7W6lOUkMhHAgAQPBz9ECEzPc22b9/u5ikCACAoCETI1nzWe9wiN/JMl6ysLPoVAQBKPAIRslHn6h07dtg1I2a70Wec3gMAUNIRiJBjLdGdry9xwUg4GSwAoKQjECFHCkF+ABL9f+/evfbXEXPcNaEIAFCSEIhwyJqiXq98ZlmZmS4EXTtyrlmWx9B8AECJw8mskGtNkUaeqYO133Sma7/pLCEhwVJSUtw1AADFFTVEyPPEjZnpB0IXBSLVEtGEBgAoCQhEyBO/dkgUfjRPkS6qF6IJDQBQ3NFkhsOqMbr5/y0IBaWk0mVt9+7dLiiVKlXKXdSEpmtRWKJZDQAQzwhEOPIao8wM6zlmnnle1h/3JadYYnIpm3DbeS4IdRv1iU3ue0EoIAEAEG9oMsMRC50DTZ2sE5P+GLKfkW7dhn/sJnjUctUg6bJ//37X1EafIwBAPKGGCIUSkEQNZDoNSGJSiut4rVqkhKQkS0opbZPvuMCt4wcjv4mNZjUAQCwQiFC4PM+yMtLcKDXXzKYAlJDgao5u/n+fu2Y2NbEpJE2588L/+5P/1R4RkgAARYFAhCLvc5R5YJ+b8FHLEizRstLTdAI1++2330Kdr28Y+6lbNrHvhS4Uhc+aXbp0abdeRkaGuxY6bgMAjkSgAtHIkSNt2LBhtnnzZjv99NNt+PDhduaZZ8Z6syzoASl65Jp/v/ojuRmyR3zsZsv2aVTbiK5N7Y7JK21crzYuCF338gJ7rfc5LjyFhyU/IClQEZoAABb0TtWTJ0+2/v372yOPPGLLli1zgahjx462devWWG8awkKQfwk/dYh6I4Xfpw7bt/5rgatZuv6lT13zW1ZamnV7YaZ1eXa6e081YeT27dtt165driO3Ztq+6vmZLgz7y9S5Oysry9U86Vq3tTz8Er5OdEdwfz6m8HXD1+FkuABQfCR4Adlbt27d2lq1amUjRoxwt3WQq1evnt155512//33H/JvdQCtVKmS7dy50ypWrFig26UD9bUj5oSGrOdpRJdqT/JRyxHPf1MQz5WVmRFR4+Tf1nUEz/ujC1PiH8+jvx/V/QzrO3G5jby2md3+2tKI90F9mxTGXr7hTLvl30vs1V5nW/ny5UNNd3ruHmPmWWZamussrqkGXr/13IiO4n7Nld+0lxf+6VBUo1UQX8/ox6MDO4Cg2JWP43cgApEOYOXKlbO33nrLOnfuHFres2dPS01NtXfffTdifX8WZp8Ksn79+rZx48ZCCUQ3js57IPIy080S1JyU94NZPP9NrLdPyxKSUkLXOf2NviEKR+7vo0T/jdZR856a+5Q3/McOX99zzX9ZEcuy0g6YJSaG/mZkzzbWd9wC87L+11Tor5uQkPh/I/myf2YO9vh6vDtfX2xe1h9f95d6netCkb4b/mSaABBLhbEfUiBS5YeO9QpGFvQ+RL/++qtlZmZarVq1Ipbr9po1a7KtP3ToUBs8eHC25SpUoCic9HjhPl7Dxwr28QEgnmkePALRYRg0aJDrb+RT85r6o1SrVq3Amxn89FoYtU8lDWWVP5RX/lBeeUdZ5Q/lFbvyUiOYwlDdunVzXTcQgah69eqWlJRkW7ZsiViu27Vr1862voZ16xKucuXKhbqNetP5ouQNZZU/lFf+UF55R1nlD+UVm/LKrWYoUKPM1C7ZokULmz17dkStj263adMmptsGAABiLxA1RKImMHWibtmypZt76Pnnn3dz3Nx4442x3jQAABBjgQlEXbt2tW3bttnDDz/s5qJp1qyZzZgxI1tH66KmpjnNjRTdRIfsKKv8obzyh/LKO8oqfyiv4lFegRh2DwAAYEHvQwQAAHAoBCIAABB4BCIAABB4BCIAABB4BCIAABB4BKIYGjlypB177LFWpkwZa926tS1evNhKuk8//dQuu+wyN426ToPyzjvvRNyvQY+aGqFOnTpWtmxZa9++va1fvz5iHZ1GpXv37m4GU80g3qtXL3eS3HArVqyw8847z5WtpoB/+umnrbjROfVatWplRx11lNWsWdOdmHjt2rUR6+zfv9/69u3rTitToUIF69KlS7YZ2X/66Sfr1KmTO8GxHmfAgAGWkZERsc4nn3xiZ5xxhhvmesIJJ9i4ceOsuBk9erSddtppodltNenq9OnTQ/dTVgf35JNPuu9jv379Qssor/959NFHXfmEXxo3bhy6n7LKbtOmTdajRw9XJtqXN23a1JYuXRrf+3oNu0fRmzRpkleqVCnvX//6l7d69Wqvd+/eXuXKlb0tW7Z4JdmHH37oPfDAA97bb7+t6R68qVOnRtz/5JNPepUqVfLeeecd76uvvvL+8pe/eA0bNvT27dsXWueiiy7yTj/9dO+LL77w5s+f751wwgneNddcE7p/586dXq1atbzu3bt7q1at8t544w2vbNmy3ksvveQVJx07dvReffVV9xqWL1/uXXLJJV79+vW9PXv2hNa57bbbvHr16nmzZ8/2li5d6p111lne2WefHbo/IyPDO/XUU7327dt7//nPf1z5V69e3Rs0aFBone+//94rV66c179/f+/rr7/2hg8f7iUlJXkzZszwipNp06Z5H3zwgbdu3Tpv7dq13t///ncvJSXFlZ9QVjlbvHixd+yxx3qnnXaad/fdd4eWU17/88gjj3innHKK98svv4Qu27ZtC91PWUXavn2716BBA++GG27wFi1a5F7bzJkzvW+//Tau9/UEohg588wzvb59+4ZuZ2ZmenXr1vWGDh3qBUV0IMrKyvJq167tDRs2LLQsNTXVK126tPugi3YU+rslS5aE1pk+fbqXkJDgbdq0yd0eNWqUV6VKFe/AgQOhde677z6vUaNGXnG2detW99rnzZsXKhsd8N98883QOt98841bZ+HChe62dryJiYne5s2bQ+uMHj3aq1ixYqh8Bg4c6Hb24bp27eoCWXGnz8Err7xCWR3E7t27vRNPPNGbNWuWd/7554cCEeWVPRDpwJwTyio77W/PPfdc72DidV9Pk1kMpKWl2ZdffumqCH2JiYnu9sKFCy2oNmzY4GYRDy8XnZRPzYl+uehaVac6BYtP66v8Fi1aFFqnbdu27hx2vo4dO7rmph07dlhxtXPnTnddtWpVd63PUHp6ekR5qRq/fv36EeWlqurwGdlVFjqb9OrVq0PrhD+Gv05x/ixmZmbapEmT3Ol51HRGWeVMzTxqxol+TZRXdmrOUVP/cccd55px1AQmlFV206ZNc/voq6++2jUPNm/e3F5++eW439cTiGLg119/dTvs6NOG6LY+JEHlv/ZDlYuu9QULl5yc7EJC+Do5PUb4cxQ3Ohmx+necc845duqpp4Zei3YE2mkcqrxyK4uDraOd9b59+6w4WblypevDoT4Yt912m02dOtVOPvlkyioHCozLli1zfdWiUV6RdKBWfx6d7kl91XRAV7+V3bt3U1Y5+P777105nXjiiTZz5kzr06eP3XXXXTZ+/Pi43tcH5lxmQHGmX/KrVq2yzz77LNabEtcaNWpky5cvd7Vpb731ljuh87x582K9WXFn48aNdvfdd9usWbNcZ1Qc2sUXXxz6vzruKyA1aNDApkyZ4joEI/sPONXsPPHEE+62aoi0/xozZoz7TsYraohioHr16paUlJRtFIJu165d24LKf+2HKhddb926NeJ+jdTQaITwdXJ6jPDnKE7uuOMOe//9923u3Ll2zDHHhJbrtaj5NTU19ZDllVtZHGwdjewobjt7/VLX6JwWLVq4mo/TTz/dXnjhBcoqipp59D3SiCb96tZFwfHFF190/9evbMrr4FQbdNJJJ9m3337LZysHGjmmmtlwTZo0CTUzxuu+nkAUo522dtizZ8+OSNS6rf4OQdWwYUP3IQ4vF1UXq73YLxdda8ejHbpvzpw5rvz0q81fR8P71a7v0y9h1R5UqVLFigv1O1cYUrOPXqPKJ5w+QykpKRHlpbZz7XTCy0vNSOE7FpWFdrL+DkvrhD+Gv05J+Czqc3HgwAHKKkq7du3ca1Vtmn/RL3r1jfH/T3kdnIZ+f/fdd+7Az2crOzXtR08Rsm7dOlerFtf7+sPqio0CGXavHvXjxo1zvelvueUWN+w+fBRCSaRRLRp2qos+fs8++6z7/48//hgaiqlyePfdd70VK1Z4l19+eY5DMZs3b+6Gc3722WdulEz4UEyNVtBQzOuuu84NxVRZazhrcRt236dPHzcs9ZNPPokY7vv7779HDPfVUPw5c+a44b5t2rRxl+jhvh06dHBD9zWEt0aNGjkO9x0wYIAbHTNy5MhiOdz3/vvvdyPwNmzY4D47uq0RKR999JG7n7I6tPBRZkJ5/c+9997rvof6bC1YsMANn9eweY38FMoq+1QOycnJ3uOPP+6tX7/emzBhgnttr7/+emideNzXE4hiSPNM6Euk+Yg0DF9zLZR0c+fOdUEo+tKzZ8/QcMyHHnrIfcgVGNu1a+fmlAn322+/uS9FhQoV3LDVG2+80QWtcJrXQsM+9RhHH320+/IVNzmVky6am8inncftt9/uhp5qR3DFFVe40BTuhx9+8C6++GI3P4d24tq5p6enZ3tfmjVr5j6Lxx13XMRzFBc33XSTm/tEr0EHG312/DAklFX+AhHlFTn8vU6dOu41aH+i2+Fz6lBW2b333nsuBGof3LhxY2/s2LER98fjvj5B/+S/XgkAAKDkoA8RAAAIPAIRAAAIPAIRAAAIPAIRAAAIPAIRAAAIPAIRAAAIPAIRAAAIPAIRAAAIPAIRAAAIPAIRAAAIPAIRAACwoPv/u3dIbIZCH/UAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns_plot = sns.histplot(answer_token_lens).set_title(\"Answer lengths in tokens (LED tokenizer)\")\n",
"sns_plot.get_figure().savefig(\"plots/answer-lengths-tokens-original.png\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(5964)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Max answer length in tokens\n",
"answer_token_lens.max()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(228.0979)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"answer_token_lens.float().mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.1034)\n"
]
}
],
"source": [
"# How many anwers are longer than MAX_TOKENS_ANSWER tokens?\n",
"print(sum(answer_token_lens > MAX_TOKENS_ANSWER) / len(answer_token_lens))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 3020/3020 [00:01<00:00, 1785.72it/s]\n"
]
},
{
"data": {
"text/plain": [
"tensor(2414)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check max length (in tokens) in the validation set\n",
"answer_token_lens_val = torch.tensor(\n",
" [\n",
" len(tokenizer(answer)[\"input_ids\"])\n",
" for answer in tqdm(dataset_lfqa[\"validation\"][\"answer\"])\n",
" ]\n",
")\n",
"answer_token_lens_val.max()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.1238)\n"
]
}
],
"source": [
"print(sum(answer_token_lens_val > MAX_TOKENS_ANSWER) / len(answer_token_lens_val))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f60cf8d9b72944008df6c5a46cca82c8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Filter: 0%| | 0/226147 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ba1c4306a7d9424bb3683944f2dfc3e8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Filter: 0%| | 0/3020 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['question', 'answer', 'context'],\n",
" num_rows: 202767\n",
" })\n",
" validation: Dataset({\n",
" features: ['question', 'answer', 'context'],\n",
" num_rows: 2646\n",
" })\n",
"})"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Drop answers longer than MAX_TOKENS_ANSWER tokens\n",
"dataset_lfqa_filtered = dataset_lfqa.filter(\n",
" lambda example: tokenizer(example[\"answer\"], return_tensors=\"pt\", return_length=True)[\"length\"] <= MAX_TOKENS_ANSWER\n",
")\n",
"dataset_lfqa_filtered"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Validate results\n",
"answer_token_lens_filtered = torch.tensor(\n",
" [\n",
" len(tokenizer(answer)[\"input_ids\"])\n",
" for answer in tqdm(dataset_lfqa_filtered[\"train\"][\"answer\"])\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"sns_plot = sns.histplot(answer_token_lens_filtered).set_title(\"Answer lengths in tokens (LED tokenizer)\")\n",
"sns_plot.get_figure().savefig(\"plots/answer-lengths-tokens-filtered.png\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(512)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"answer_token_lens_filtered.max()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 2646/2646 [00:00<00:00, 2846.88it/s]\n"
]
},
{
"data": {
"text/plain": [
"tensor(512)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Same for validation set\n",
"answer_token_lens_val_filtered = torch.tensor(\n",
" [\n",
" len(tokenizer(answer)[\"input_ids\"])\n",
" for answer in tqdm(dataset_lfqa_filtered[\"validation\"][\"answer\"])\n",
" ]\n",
")\n",
"answer_token_lens_val_filtered.max()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"answer_str_lens_filtered = [len(answer) for answer in dataset_lfqa_filtered[\"train\"][\"answer\"]]\n",
"sns_plot = sns.histplot(answer_str_lens_filtered).set_title(\"Answer lengths in characters\")\n",
"sns_plot.get_figure().savefig(\"plots/answer-lengths-chars-filtered.png\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2771"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max(answer_str_lens_filtered)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We cut off the way out outliers without modifying the distribution and without losing too much data."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "17eba34a83454d4ab52018886b33bcc8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Uploading the dataset shards: 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "37ce41c54b9f45fe960e57224391a703",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Creating parquet from Arrow format: 0%| | 0/68 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cff3b84b79af496392b9637c7cbbb245",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Creating parquet from Arrow format: 0%| | 0/68 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6cb4ae2801b94e2d836caaf05d52b936",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Creating parquet from Arrow format: 0%| | 0/68 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "76373ab749cf4cfdbb4a7283fd01149e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Uploading the dataset shards: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "82025c8e39e74a8080057a66ecb9df69",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Creating parquet from Arrow format: 0%| | 0/3 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/datasets/stefanbschneider/lfqa-max-answer-length-512/commit/475c4f1ae4f28023c20851a54893c6a63aed5901', commit_message='Upload dataset', commit_description='', oid='475c4f1ae4f28023c20851a54893c6a63aed5901', pr_url=None, repo_url=RepoUrl('https://huggingface.co/datasets/stefanbschneider/lfqa-max-answer-length-512', endpoint='https://huggingface.co', repo_type='dataset', repo_id='stefanbschneider/lfqa-max-answer-length-512'), pr_revision=None, pr_num=None)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset_lfqa_filtered.push_to_hub(f\"stefanbschneider/lfqa-max-answer-length-{MAX_TOKENS_ANSWER}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "llm",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|