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"cells": [
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"source": [
"## Load the dataset"
],
"metadata": {
"id": "so82WVQVy4ua"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import zipfile"
],
"metadata": {
"id": "vr1Ig6OKEs3m"
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"outputs": []
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{
"cell_type": "code",
"source": [
"# Path to the zipped file\n",
"zip_file_path = '/content/drive/MyDrive/Amazon_shoe_review/amazon_reviews_us_Shoes_v1_00.tsv (1).zip'\n",
"\n",
"# Open the ZIP file\n",
"with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
" # List all the files in the ZIP archive\n",
" print(zip_ref.namelist())\n",
"\n",
" # Read the TSV file from the ZIP archive, handling errors\n",
" with zip_ref.open('amazon_reviews_us_Shoes_v1_00.tsv') as tsv_file:\n",
" df = pd.read_csv(tsv_file, sep='\\t', on_bad_lines='skip') # Skip bad lines\n",
"\n",
"print(df.shape) # Check the number of rows and columns in the dataframe"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KTsxqqw6VkGk",
"outputId": "5123360f-e3c2-44a7-c55a-44dc68c47b0c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['amazon_reviews_us_Shoes_v1_00.tsv']\n",
"(4358820, 15)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
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],
"metadata": {
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"height": 504
},
"id": "kLm1IIqSV9bb",
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"output_type": "execute_result",
"data": {
"text/plain": [
" marketplace customer_id review_id product_id product_parent \\\n",
"0 US 18069663 R3P2HIOQCIN5ZU B000XB31C0 265024781 \n",
"1 US 16251825 R12VVR0WH5Q24V B00CFYZH5W 259035853 \n",
"2 US 20381037 RNCCKB6TV5EEF B00S8JNN3Q 666066660 \n",
"3 US 108364 R2NZXYIVCGB13W B00XFBPOQG 448483263 \n",
"4 US 45449350 R2EQ1TG9IT3OEQ B00SW64Y9W 7853171 \n",
"\n",
" product_title product_category \\\n",
"0 Minnetonka Men's Double Deerskin Softsole Mocc... Shoes \n",
"1 Teva Men's Pajaro Flip-Flop Shoes \n",
"2 Anne Klein Perfect Pair Wristlet Shoes \n",
"3 adidas Men's 10K Lifestyle Runner Sneaker Shoes \n",
"4 OverBling Sneakers for Men Casual Men Shoes Ge... Shoes \n",
"\n",
" star_rating helpful_votes total_votes vine verified_purchase \\\n",
"0 1 0 0 N Y \n",
"1 5 0 0 N Y \n",
"2 4 0 0 N Y \n",
"3 5 0 6 N Y \n",
"4 3 0 0 N Y \n",
"\n",
" review_headline review_body \\\n",
"0 . Do not buy: really didn't start to wear them u... \n",
"1 super flip flop provides great cushion as well as archsupport \n",
"2 Great clutch purse! It's perfect if you need something small for c... \n",
"3 Badass Getting what u see \n",
"4 Three Stars small \n",
"\n",
" review_date \n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df"
}
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"df.columns"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AbhT3KdNldiW",
"outputId": "fa04feae-e24e-4148-adf1-c24baae67935"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['marketplace', 'customer_id', 'review_id', 'product_id',\n",
" 'product_parent', 'product_title', 'product_category', 'star_rating',\n",
" 'helpful_votes', 'total_votes', 'vine', 'verified_purchase',\n",
" 'review_headline', 'review_body', 'review_date'],\n",
" dtype='object')"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"df.value_counts('star_rating')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QHWdHtb3lrrD",
"outputId": "50b8e4ea-dfe7-4d17-843b-13986ee5550c"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"star_rating\n",
"5 2635010\n",
"4 846239\n",
"3 403443\n",
"2 242379\n",
"1 231749\n",
"Name: count, dtype: int64"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"source": [
"## Balance the dataset"
],
"metadata": {
"id": "HnIHHrgKy_6a"
}
},
{
"cell_type": "markdown",
"source": [
"The dataset is quite unbalanced. We have too many 4 and 5-star ratings. Let's balance the dataset, and keep only reviews in each class."
],
"metadata": {
"id": "EEhQGn8LoROc"
}
},
{
"cell_type": "code",
"source": [
"balanced_df = pd.DataFrame(columns=df.columns)"
],
"metadata": {
"id": "0pfd9NTHl3Jy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"balanced_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 73
},
"id": "thyGAsiGo3xv",
"outputId": "1b6ff7e9-1f31-4ce0-e049-10ad342c0726"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Empty DataFrame\n",
"Columns: [marketplace, customer_id, review_id, product_id, product_parent, product_title, product_category, star_rating, helpful_votes, total_votes, vine, verified_purchase, review_headline, review_body, review_date]\n",
"Index: []"
],
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" review_id | \n",
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" star_rating | \n",
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"cell_type": "code",
"source": [
"# for stars in range(1,6):\n",
"# data = df[df['star_rating'] == stars][:20000]\n",
"# balanced_df = pd.concat([balanced_df, data])"
],
"metadata": {
"id": "kPUy8X8To_JZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for stars in range(1, 6):\n",
" # Filter the DataFrame to include only rows with the current star rating and non-None review_body\n",
" data = df[(df['star_rating'] == stars) & (df['review_body'].notna())][:20000]\n",
" # Concatenate the filtered data to balanced_df\n",
" balanced_df = pd.concat([balanced_df, data], ignore_index=True)"
],
"metadata": {
"id": "59gwVpWwXKIZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"balanced_df.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "27GvEfbxrip5",
"outputId": "4d448926-9482-4c3a-f1d2-147509ea6512"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(100000, 15)"
]
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"metadata": {},
"execution_count": 19
}
]
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{
"cell_type": "code",
"source": [
"balanced_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 521
},
"id": "UYJ-yxEaruHn",
"outputId": "50a33ddb-d331-48ab-99f7-49f6dd4a55f6"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" marketplace customer_id review_id product_id product_parent \\\n",
"0 US 18069663 R3P2HIOQCIN5ZU B000XB31C0 265024781 \n",
"1 US 12334573 R9BHBB06QD6TM B008NCHMBW 138572112 \n",
"2 US 230032 RFN9JY10X50F3 B005VPABXA 598684491 \n",
"3 US 9088293 R13749PRFL9JKT B003E7QNTI 580898588 \n",
"4 US 22138690 RE9KJGO100EDW B00SENYY98 470125824 \n",
"\n",
" product_title product_category \\\n",
"0 Minnetonka Men's Double Deerskin Softsole Mocc... Shoes \n",
"1 Foot Sox Original Sanitary Disposable Try on S... Shoes \n",
"2 Comfy Feet Womens Snooki House Slippers Shoes \n",
"3 Ariat Women's Krista Pull-on Steel Toe Western... Shoes \n",
"4 Women's Fun Design, Lightweight & Comfortable ... Shoes \n",
"\n",
" star_rating helpful_votes total_votes vine verified_purchase \\\n",
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"0 . \n",
"1 Tissue paper is this thicker than this. It rip... \n",
"2 One Star \n",
"3 not wide width at all \n",
"4 giant and cheep \n",
"\n",
" review_body review_date \n",
"0 Do not buy: really didn't start to wear them u... 2015-08-31 \n",
"1 Tissue paper is this thicker than this. It ri... 2015-08-31 \n",
"2 Not what I expected would not order again 2015-08-31 \n",
"3 These boots were order as the 9c which is the ... 2015-08-31 \n",
"4 Cheep, made in China although they advertised ... 2015-08-31 "
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}
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"balanced_df.value_counts('star_rating')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ns4WyH04r0n2",
"outputId": "83b54b40-6e44-46be-e089-0fb7339cab83"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"star_rating\n",
"1 20000\n",
"2 20000\n",
"3 20000\n",
"4 20000\n",
"5 20000\n",
"Name: count, dtype: int64"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "markdown",
"source": [
"## Convert the pandas dataframe to huggingFace dataset"
],
"metadata": {
"id": "-W6667zwzJ0g"
}
},
{
"cell_type": "code",
"source": [
"!pip install datasets"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "w_Xvioc0zgBI",
"outputId": "4adbfb02-bfef-4a99-877d-0e4ed38b3bb0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.20.0)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.4)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n",
"Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (16.1.0)\n",
"Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n",
"Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.0.3)\n",
"Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n",
"Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.4)\n",
"Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.4.1)\n",
"Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n",
"Requirement already satisfied: fsspec[http]<=2024.5.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
"Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.5)\n",
"Requirement already satisfied: huggingface-hub>=0.21.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.23.4)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n",
"Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
"Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.2->datasets) (4.12.2)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.7)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.0.7)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.6.2)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
"Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install --upgrade pyarrow"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rE5e4CEsYBM8",
"outputId": "3e9c54e0-673c-4cc4-8ef4-002ca6b9a269"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: pyarrow in /usr/local/lib/python3.10/dist-packages (16.1.0)\n",
"Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.10/dist-packages (from pyarrow) (1.25.2)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from datasets import Dataset"
],
"metadata": {
"id": "N1rXtFWzsLE5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"We don't need all the columns. We'll keep only the star_rating and the review_body."
],
"metadata": {
"id": "ZZSRDYIf5pPp"
}
},
{
"cell_type": "code",
"source": [
"shoe_df = balanced_df[['star_rating', 'review_body']]"
],
"metadata": {
"id": "M_Gh9Trw52Le"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"shoe_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "jKeB46Re59Bk",
"outputId": "7f6ac01b-7ade-4928-f4d8-56979ca470d8"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" star_rating review_body\n",
"0 1 Do not buy: really didn't start to wear them u...\n",
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}
},
"metadata": {},
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}
]
},
{
"cell_type": "code",
"source": [
"shoe_df.value_counts('star_rating')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GGLmMYBK6Dts",
"outputId": "bc12acfa-b1fb-4441-b8bb-87d4c511473e"
},
"execution_count": null,
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{
"output_type": "execute_result",
"data": {
"text/plain": [
"star_rating\n",
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"2 20000\n",
"3 20000\n",
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},
"metadata": {},
"execution_count": 25
}
]
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{
"cell_type": "code",
"source": [
"# Adjust the star_rating to start from 0\n",
"shoe_df['star_rating'] = shoe_df['star_rating'] - 1"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OuYomlaI8YYG",
"outputId": "c3d1e60b-fa60-4bba-96c0-74076bacb721"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
":2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" shoe_df['star_rating'] = shoe_df['star_rating'] - 1\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"shoe_df.value_counts('star_rating')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ahfeMg078p85",
"outputId": "5dffbfcb-ebfd-41e5-b9b1-2b53bd2634f8"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"star_rating\n",
"0 20000\n",
"1 20000\n",
"2 20000\n",
"3 20000\n",
"4 20000\n",
"Name: count, dtype: int64"
]
},
"metadata": {},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"source": [
"shoe_df.head()"
],
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "-b7r8hAT8zqJ",
"outputId": "3818e842-2848-45e8-ffc5-d895b7e27ca5"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" star_rating review_body\n",
"0 0 Do not buy: really didn't start to wear them u...\n",
"1 0 Tissue paper is this thicker than this. It ri...\n",
"2 0 Not what I expected would not order again\n",
"3 0 These boots were order as the 9c which is the ...\n",
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],
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}
},
"metadata": {},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"source": [
"hf_dataset = Dataset.from_pandas(shoe_df, preserve_index=False)"
],
"metadata": {
"id": "y6l8qFVYzVoM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"hf_dataset"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uT6riW9M0YtF",
"outputId": "042edcbb-04a3-4c52-cef8-6173e5cded99"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['star_rating', 'review_body'],\n",
" num_rows: 100000\n",
"})"
]
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"source": [
"hf_dataset = hf_dataset.rename_column('star_rating', 'labels')\n",
"hf_dataset = hf_dataset.rename_column('review_body', 'text')"
],
"metadata": {
"id": "j1uruYeu1vIk"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"hf_dataset[0]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1rn1PG6C-Eu4",
"outputId": "91734ffb-afdb-4527-d59e-092ac5ea53de"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'labels': 0,\n",
" 'text': \"Do not buy: really didn't start to wear them until May of 2016. Junk, they are falling apart. The outer sole is so thin that although I wear them almost completely in the house on rugs the inner padding is showing through in the heel. My previous pair from the same company lasted 5 years before I threw them out. I'm sorry I didn't wear them more often when I first got them as I would have returned them immediately\"}"
]
},
"metadata": {},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"source": [
"dataset_split = hf_dataset.train_test_split(test_size=0.1, shuffle=True, seed=42)"
],
"metadata": {
"id": "AG64Ai-X-HjZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"dataset_split"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nThO-PV8-srZ",
"outputId": "b57607f4-10d0-4016-852b-1d97dbb18afe"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['labels', 'text'],\n",
" num_rows: 90000\n",
" })\n",
" test: Dataset({\n",
" features: ['labels', 'text'],\n",
" num_rows: 10000\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 35
}
]
},
{
"cell_type": "code",
"source": [
"# Save the dataset\n",
"dataset_split.save_to_disk(\"/content/drive/MyDrive/Amazon_shoe_review/shoe_hfdataset\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
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},
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"outputId": "f2f2bd6d-a6d3-4fa6-bc8f-62a452c20ca3"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Saving the dataset (0/1 shards): 0%| | 0/90000 [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9abbf6b11b8f4766bdc874cb7ae6eaf6"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Saving the dataset (0/1 shards): 0%| | 0/10000 [00:00, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "79c8257e4bf34f35ab9553ecf6d417fe"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Save the dataset in csv format\n",
"dataset_split['train'].to_csv('/content/drive/MyDrive/Amazon_shoe_review/shoe_dataset_train.csv')\n",
"dataset_split['test'].to_csv('/content/drive/MyDrive/Amazon_shoe_review/shoe_dataset_test.csv')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 99,
"referenced_widgets": [
"b6415b93673a4eeea818deaeadfdd27b",
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},
"id": "fZQRFU5DNGaV",
"outputId": "aca58afb-569c-4dfa-fc9c-b509c015cd89"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Creating CSV from Arrow format: 0%| | 0/90 [00:00, ?ba/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b6415b93673a4eeea818deaeadfdd27b"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Creating CSV from Arrow format: 0%| | 0/10 [00:00, ?ba/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8ae2aaa7aefb46f6bbc5ec84f9a95e21"
}
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1770307"
]
},
"metadata": {},
"execution_count": 45
}
]
},
{
"cell_type": "markdown",
"source": [
"## Push the dataset to huggingface hub"
],
"metadata": {
"id": "kueqEHUpXrin"
}
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import notebook_login"
],
"metadata": {
"id": "5fiWe0xVTSfE"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"notebook_login()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145,
"referenced_widgets": [
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"471e2aca3b054797aeba09b0912186ae",
"246b085da0054d77b91b0da8728a46ec",
"acda64910be1407b9e06733da7b6eb4f",
"7698055225324eba915babf9f5bc2174",
"c0c861866de348819c0f09cd15e48c54",
"cc6e29d9987a4182afa77f1fc5ec39d0",
"6c7f1ec533fd45b2b5fcf88fab0797de",
"04e41c296ed344408e2594795f594434"
]
},
"id": "uYydzuysTeAZ",
"outputId": "8895d836-5b9e-4071-870c-456527acfb1b"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"VBox(children=(HTML(value='