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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "666aeda0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Archive: /user/bhanucha/recipe_data.zip\n",
" creating: /user/bhanucha/data/dataset/\n",
" inflating: /user/bhanucha/data/dataset/full_dataset.csv \n"
]
}
],
"source": [
"!unzip '/user/bhanucha/recipe_data.zip' -d '/user/bhanucha/data'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7a4b917b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-04-20 19:46:56.235357: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX512_VNNI\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2024-04-20 19:47:03.051474: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n"
]
}
],
"source": [
"import pandas as pd\n",
"from transformers import BartTokenizer\n",
"from tqdm import tqdm \n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from transformers import TFBartForConditionalGeneration\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "84ebecc4",
"metadata": {},
"outputs": [],
"source": [
"model_checkpoint = \"facebook/bart-base\"\n",
"tokenizer = BartTokenizer.from_pretrained(model_checkpoint)\n",
"if tokenizer.pad_token is None:\n",
" tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})\n",
"\n",
"\n",
"data = pd.read_csv('/user/bhanucha/data/dataset/full_dataset.csv')\n",
"\n",
"\n",
"texts = [\"Ingredients: \" + row['ingredients'] + \" Directions: \" + row['directions'] for _, row in data.iterrows()]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5597cc7e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Tokenizing Data: 100%|██████████| 2231142/2231142 [43:29<00:00, 854.85it/s]\n"
]
}
],
"source": [
"\n",
"tokenized_inputs = []\n",
"for texts_text in tqdm(texts, desc=\"Tokenizing Data\"):\n",
" tokenized_input = tokenizer(\n",
" texts_text,\n",
" padding=\"max_length\",\n",
" truncation=True,\n",
" max_length=512,\n",
" return_tensors=\"np\"\n",
" )\n",
" tokenized_inputs.append(tokenized_input['input_ids'])\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "00678266",
"metadata": {},
"outputs": [],
"source": [
"\n",
"train_data = np.concatenate(tokenized_inputs, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56fcd8cd",
"metadata": {},
"outputs": [],
"source": [
"np.save('/user/bhanucha/train_data.npy', train_data)"
]
}
],
"metadata": {
"celltoolbar": "Attachments",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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