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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
" "
]
},
{
"cell_type": "markdown",
"source": [
"# 1. Dataset Preparation"
],
"metadata": {
"id": "LNsZ3c69cGnY"
}
},
{
"cell_type": "code",
"source": [
"!nvidia-smi "
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Loz2U2x_xxpj",
"outputId": "e550a634-f73e-420a-d281-6a775f77099b"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mon Jun 5 04:18:42 2023 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 62C P8 11W / 70W | 0MiB / 15360MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install datasets"
],
"metadata": {
"colab": {
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},
"id": "oAkWgqvwc34x",
"outputId": "925b0b15-e1c2-4ee3-9776-97b8b8aa7cc4"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting datasets\n",
" Downloading datasets-2.12.0-py3-none-any.whl (474 kB)\n",
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"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.22.4)\n",
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" Downloading dill-0.3.6-py3-none-any.whl (110 kB)\n",
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"\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
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"Collecting xxhash (from datasets)\n",
" Downloading xxhash-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m24.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting multiprocess (from datasets)\n",
" Downloading multiprocess-0.70.14-py310-none-any.whl (134 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m17.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.4.0)\n",
"Collecting aiohttp (from datasets)\n",
" Downloading aiohttp-3.8.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m71.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting huggingface-hub<1.0.0,>=0.11.0 (from datasets)\n",
" Downloading huggingface_hub-0.15.1-py3-none-any.whl (236 kB)\n",
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" Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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"Collecting multidict<7.0,>=4.5 (from aiohttp->datasets)\n",
" Downloading multidict-6.0.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (114 kB)\n",
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"\u001b[?25hCollecting async-timeout<5.0,>=4.0.0a3 (from aiohttp->datasets)\n",
" Downloading async_timeout-4.0.2-py3-none-any.whl (5.8 kB)\n",
"Collecting yarl<2.0,>=1.0 (from aiohttp->datasets)\n",
" Downloading yarl-1.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (268 kB)\n",
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"\u001b[?25hCollecting frozenlist>=1.1.1 (from aiohttp->datasets)\n",
" Downloading frozenlist-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (149 kB)\n",
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"\u001b[?25hCollecting aiosignal>=1.1.2 (from aiohttp->datasets)\n",
" Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
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"Requirement already satisfied: python-dateutil>=2.8.1 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) (2022.7.1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
"Installing collected packages: xxhash, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, huggingface-hub, aiosignal, aiohttp, datasets\n",
"Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.12.0 dill-0.3.6 frozenlist-1.3.3 huggingface-hub-0.15.1 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0 yarl-1.9.2\n"
]
}
]
},
{
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"source": [
"from datasets import load_dataset\n",
"dataset = load_dataset(\"stsb_multi_mt\", name=\"en\", split=\"train\")"
],
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"Downloading and preparing dataset stsb_multi_mt/en to /root/.cache/huggingface/datasets/stsb_multi_mt/en/1.0.0/a5d260e4b7aa82d1ab7379523a005a366d9b124c76a5a5cf0c4c5365458b0ba9...\n"
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"text": [
"Dataset stsb_multi_mt downloaded and prepared to /root/.cache/huggingface/datasets/stsb_multi_mt/en/1.0.0/a5d260e4b7aa82d1ab7379523a005a366d9b124c76a5a5cf0c4c5365458b0ba9. Subsequent calls will reuse this data.\n"
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"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['sentence1', 'sentence2', 'similarity_score'],\n",
" num_rows: 5749\n",
"})"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
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"source": [
"df = dataset.to_pandas()"
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"execution_count": 5,
"outputs": []
},
{
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{
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" sentence1 \\\n",
"0 A plane is taking off. \n",
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"\n",
" sentence2 similarity_score \n",
"0 An air plane is taking off. 5.00 \n",
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"25 The boy fell off his bike. \n",
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"33 A man is slicing a bun. \n",
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" sentence2 score \n",
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"18 A cat is rubbing against a baby. 3.800 \n",
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"35 A lion is playing with two men. 3.400 \n",
"36 A dog is riding a skateboard. 5.000 \n",
"37 A man is carving a statue. 3.750 \n",
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" A man is playing a flute. \n",
" A man is playing a bamboo flute. \n",
" 3.867 \n",
" \n",
" \n",
" 13 \n",
" A person is folding a piece of paper. \n",
" Someone is folding a piece of paper. \n",
" 4.667 \n",
" \n",
" \n",
" 14 \n",
" A man is running on the road. \n",
" A panda dog is running on the road. \n",
" 1.667 \n",
" \n",
" \n",
" 15 \n",
" A dog is trying to get bacon off his back. \n",
" A dog is trying to eat the bacon on its back. \n",
" 3.750 \n",
" \n",
" \n",
" 16 \n",
" The polar bear is sliding on the snow. \n",
" A polar bear is sliding across the snow. \n",
" 5.000 \n",
" \n",
" \n",
" 17 \n",
" A woman is writing. \n",
" A woman is swimming. \n",
" 0.500 \n",
" \n",
" \n",
" 18 \n",
" A cat is rubbing against baby's face. \n",
" A cat is rubbing against a baby. \n",
" 3.800 \n",
" \n",
" \n",
" 19 \n",
" The man is riding a horse. \n",
" A man is riding on a horse. \n",
" 5.000 \n",
" \n",
" \n",
" 20 \n",
" A man pours oil into a pot. \n",
" A man pours wine in a pot. \n",
" 3.200 \n",
" \n",
" \n",
" 21 \n",
" A man is playing a guitar. \n",
" A girl is playing a guitar. \n",
" 2.800 \n",
" \n",
" \n",
" 22 \n",
" A panda is sliding down a slide. \n",
" A panda slides down a slide. \n",
" 4.600 \n",
" \n",
" \n",
" 23 \n",
" A woman is eating something. \n",
" A woman is eating meat. \n",
" 3.000 \n",
" \n",
" \n",
" 24 \n",
" A woman peels a potato. \n",
" A woman is peeling a potato. \n",
" 5.000 \n",
" \n",
" \n",
" 25 \n",
" The boy fell off his bike. \n",
" A boy falls off his bike. \n",
" 4.800 \n",
" \n",
" \n",
" 26 \n",
" The woman is playing the flute. \n",
" A woman is playing a flute. \n",
" 5.000 \n",
" \n",
" \n",
" 27 \n",
" A rabbit is running from an eagle. \n",
" A hare is running from a eagle. \n",
" 4.200 \n",
" \n",
" \n",
" 28 \n",
" The woman is frying a breaded pork chop. \n",
" A woman is cooking a breaded pork chop. \n",
" 4.200 \n",
" \n",
" \n",
" 29 \n",
" A girl is flying a kite. \n",
" A girl running is flying a kite. \n",
" 4.000 \n",
" \n",
" \n",
" 30 \n",
" A man is riding a mechanical bull. \n",
" A man rode a mechanical bull. \n",
" 4.000 \n",
" \n",
" \n",
" 31 \n",
" The man is playing the guitar. \n",
" A man is playing a guitar. \n",
" 4.909 \n",
" \n",
" \n",
" 32 \n",
" A woman is dancing and singing with other women. \n",
" A woman is dancing and singing in the rain. \n",
" 3.000 \n",
" \n",
" \n",
" 33 \n",
" A man is slicing a bun. \n",
" A man is slicing an onion. \n",
" 2.400 \n",
" \n",
" \n",
" 34 \n",
" A man is pouring oil into a pan. \n",
" A man is pouring oil into a skillet. \n",
" 4.200 \n",
" \n",
" \n",
" 35 \n",
" A lion is playing with people. \n",
" A lion is playing with two men. \n",
" 3.400 \n",
" \n",
" \n",
" 36 \n",
" A dog rides a skateboard. \n",
" A dog is riding a skateboard. \n",
" 5.000 \n",
" \n",
" \n",
" 37 \n",
" Someone is carving a statue. \n",
" A man is carving a statue. \n",
" 3.750 \n",
" \n",
" \n",
" 38 \n",
" A woman is slicing an onion. \n",
" A man is cutting an onion. \n",
" 2.750 \n",
" \n",
" \n",
" 39 \n",
" A woman peels shrimp. \n",
" A woman is peeling shrimp. \n",
" 5.000 \n",
" \n",
" \n",
" 40 \n",
" A woman is frying fish. \n",
" A woman is cooking fish. \n",
" 4.000 \n",
" \n",
" \n",
" 41 \n",
" A woman is playing an electric guitar. \n",
" A woman is playing a guitar. \n",
" 3.600 \n",
" \n",
" \n",
" 42 \n",
" A baby tiger is playing with a ball. \n",
" A baby is playing with a doll. \n",
" 1.600 \n",
" \n",
" \n",
" 43 \n",
" A person is slicing a tomato. \n",
" A person is slicing some meat. \n",
" 1.750 \n",
" \n",
" \n",
" 44 \n",
" A person cuts an onion. \n",
" A person is cutting an onion. \n",
" 5.000 \n",
" \n",
" \n",
" 45 \n",
" A man is playing the piano. \n",
" A woman is playing the violin. \n",
" 1.000 \n",
" \n",
" \n",
" 46 \n",
" A woman is playing the flute. \n",
" A man is playing the guitar. \n",
" 1.000 \n",
" \n",
" \n",
" 47 \n",
" A man is cutting up a potato. \n",
" A man is cutting up carrots. \n",
" 2.375 \n",
" \n",
" \n",
" 48 \n",
" A kid is playing guitar. \n",
" A boy is playing a guitar. \n",
" 3.800 \n",
" \n",
" \n",
" 49 \n",
" A boy is playing guitar. \n",
" A man is playing a guitar. \n",
" 3.200 \n",
" \n",
" \n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
"
\n",
"
\n",
" "
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"source": [
"## 1.1 Getting translator to translate english sentences into nepali"
],
"metadata": {
"id": "d7ThAP8_c_CT"
}
},
{
"cell_type": "code",
"source": [
"!pip install deep-translator"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wPI57eArdvHI",
"outputId": "2da908a3-ca74-445a-c2f0-fb8e31035307"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting deep-translator\n",
" Downloading deep_translator-1.11.1-py3-none-any.whl (37 kB)\n",
"Requirement already satisfied: beautifulsoup4<5.0.0,>=4.9.1 in /usr/local/lib/python3.10/dist-packages (from deep-translator) (4.11.2)\n",
"Requirement already satisfied: requests<3.0.0,>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from deep-translator) (2.27.1)\n",
"Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4<5.0.0,>=4.9.1->deep-translator) (2.4.1)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.23.0->deep-translator) (1.26.15)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.23.0->deep-translator) (2022.12.7)\n",
"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.23.0->deep-translator) (2.0.12)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.23.0->deep-translator) (3.4)\n",
"Installing collected packages: deep-translator\n",
"Successfully installed deep-translator-1.11.1\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from deep_translator import GoogleTranslator \n",
"from tqdm import tqdm"
],
"metadata": {
"id": "NMivpnstdzDf"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"text = df['sentence2'][100]\n",
"\n",
"translated = GoogleTranslator(source=\"auto\", target=\"ne\").translate(text)\n",
"print(translated)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HwJppundpnZ-",
"outputId": "fb79d275-8f8e-413e-ed42-552b509dc016"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"कोही गितार बजाइरहेका छन्।\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def translate_text(text, target_lang):\n",
" translator = GoogleTranslator(source=\"auto\", target=target_lang)\n",
" translation = translator.translate(text)\n",
"\n",
" return translation\n",
"\n",
"\n",
"target_lang = \"ne\"\n",
"columns_to_translate = [\"sentence1\", \"sentence2\"]\n",
"\n",
"for column in columns_to_translate:\n",
" tqdm.pandas(desc=f\"Translating {column}\")\n",
" df[column] = df[column].progress_apply(lambda x: translate_text(x, target_lang))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RYYa17PEfliw",
"outputId": "a84f2a94-d602-42f5-b237-cda87e9bdd17"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Translating sentence1: 100%|██████████| 5749/5749 [20:59<00:00, 4.56it/s]\n",
"Translating sentence2: 100%|██████████| 5749/5749 [20:18<00:00, 4.72it/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LVT8wvq5Ssal",
"outputId": "9ff4d321-189e-4310-c690-429780078c88"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# import pandas as pd\n",
"\n",
"\n",
"# csv_path = '/content/drive/My Drive/Datasets/stsb_multi_mt_nepali_cleaned.csv'\n",
"# df = pd.read_csv(csv_path)"
],
"metadata": {
"id": "kK0a63FHS1pW"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df.head(100)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "FCrVcMuXip-O",
"outputId": "b4542263-1ce2-478d-fc21-ed85f5c5bffc"
},
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" sentence1 \\\n",
"0 एउटा विमान उडिरहेको छ। \n",
"1 एउटा मान्छे ठूलो बाँसुरी बजाइरहेको छ। \n",
"2 एक व्यक्ति पिज्जामा टुक्रा चिज फैलाउँदै छ। \n",
"3 तीन जना चेस खेल्दै छन्। \n",
"4 एउटा मान्छे सेलो बजाउँदै छ। \n",
".. ... \n",
"95 दुई जना केटाले गाडी चलाइरहेका छन् । \n",
"96 एउटा मानिस घोडामा सवार छ। \n",
"97 एकजना मानिस साइकल चलाइरहेका छन्। \n",
"98 एउटा मान्छे आलु काट्दै छ। \n",
"99 एउटी महिला आलु बोक्दै छिन्। \n",
"\n",
" sentence2 score \n",
"0 हवाई जहाज उडिरहेको छ। 5.00 \n",
"1 एउटा मान्छे बाँसुरी बजाउँदै छ। 3.80 \n",
"2 एक जना मानिसले न पकाएको पिज्जामा टुक्रा पारेको... 3.80 \n",
"3 दुई जना पुरुष चेस खेलिरहेका छन्। 2.60 \n",
"4 बसेको मान्छे सेलो खेलिरहेको छ। 4.25 \n",
".. ... ... \n",
"95 दुई खाडीहरू नाचिरहेका छन्। 0.60 \n",
"96 एउटी केटी घोडा चढ्दै छिन्। 2.60 \n",
"97 बाँदर बाइक चलाउँदै छ। 2.00 \n",
"98 एउटी महिला आलु बोक्दै छिन्। 2.20 \n",
"99 एउटा मान्छे आलु काट्दै छ। 2.40 \n",
"\n",
"[100 rows x 3 columns]"
],
"text/html": [
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\n",
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"
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" sentence2 \n",
" score \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" एउटा विमान उडिरहेको छ। \n",
" हवाई जहाज उडिरहेको छ। \n",
" 5.00 \n",
" \n",
" \n",
" 1 \n",
" एउटा मान्छे ठूलो बाँसुरी बजाइरहेको छ। \n",
" एउटा मान्छे बाँसुरी बजाउँदै छ। \n",
" 3.80 \n",
" \n",
" \n",
" 2 \n",
" एक व्यक्ति पिज्जामा टुक्रा चिज फैलाउँदै छ। \n",
" एक जना मानिसले न पकाएको पिज्जामा टुक्रा पारेको... \n",
" 3.80 \n",
" \n",
" \n",
" 3 \n",
" तीन जना चेस खेल्दै छन्। \n",
" दुई जना पुरुष चेस खेलिरहेका छन्। \n",
" 2.60 \n",
" \n",
" \n",
" 4 \n",
" एउटा मान्छे सेलो बजाउँदै छ। \n",
" बसेको मान्छे सेलो खेलिरहेको छ। \n",
" 4.25 \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 95 \n",
" दुई जना केटाले गाडी चलाइरहेका छन् । \n",
" दुई खाडीहरू नाचिरहेका छन्। \n",
" 0.60 \n",
" \n",
" \n",
" 96 \n",
" एउटा मानिस घोडामा सवार छ। \n",
" एउटी केटी घोडा चढ्दै छिन्। \n",
" 2.60 \n",
" \n",
" \n",
" 97 \n",
" एकजना मानिस साइकल चलाइरहेका छन्। \n",
" बाँदर बाइक चलाउँदै छ। \n",
" 2.00 \n",
" \n",
" \n",
" 98 \n",
" एउटा मान्छे आलु काट्दै छ। \n",
" एउटी महिला आलु बोक्दै छिन्। \n",
" 2.20 \n",
" \n",
" \n",
" 99 \n",
" एउटी महिला आलु बोक्दै छिन्। \n",
" एउटा मान्छे आलु काट्दै छ। \n",
" 2.40 \n",
" \n",
" \n",
"
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"
100 rows × 3 columns
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"
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"
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" \n",
" \n",
"\n",
" \n",
"
\n",
"
\n",
" "
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
"df.to_csv('stsb_multi_mt_nepali.csv', index=False)"
],
"metadata": {
"id": "eBWzpbhV4Bpx"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"\n",
"## 1.2 Removing alphabets \n"
],
"metadata": {
"id": "BFvBWyvU4g-G"
}
},
{
"cell_type": "code",
"source": [
"import re\n",
"\n",
"def remove_alphabets(text):\n",
" if isinstance(text, str):\n",
" pattern = r'[a-zA-Z]'\n",
" return re.sub(pattern, '', text)\n",
" else:\n",
" return text\n",
"\n",
"df = df.applymap(remove_alphabets)"
],
"metadata": {
"id": "xEL7Jx7Q6bPk"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df.iloc[843]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gCKIdgj_61a-",
"outputId": "d3b803c8-bbf4-4dc1-afc3-e6b36aa6c833"
},
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"sentence1 केटीहरू स्टेजमा हिँडिरहेका छन्।\n",
"sentence2 महिला मोडलहरू क्याटवाक गर्दै छन्।\n",
"score 2.6\n",
"Name: 843, dtype: object"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"source": [
"df.to_csv('stsb_multi_mt_nepali_cleaned.csv', index=False)"
],
"metadata": {
"id": "Y2-sWE7F7TZA"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# 2. Training Sentence Transformer"
],
"metadata": {
"id": "Ke9ieYET8SgR"
}
},
{
"cell_type": "code",
"source": [
"!pip install sentence_transformers"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qVWtQfPL83WO",
"outputId": "e5828243-1fd3-4ef9-87d3-50147c5499ae"
},
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting sentence_transformers\n",
" Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Collecting transformers<5.0.0,>=4.6.0 (from sentence_transformers)\n",
" Downloading transformers-4.29.2-py3-none-any.whl (7.1 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m110.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.65.0)\n",
"Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (2.0.1+cu118)\n",
"Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (0.15.2+cu118)\n",
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"Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers<5.0.0,>=4.6.0->sentence_transformers)\n",
" Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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"Building wheels for collected packages: sentence_transformers\n",
" Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sentence_transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125926 sha256=c1978172d984e68d5e0f3c105af8ac3e5c47e15b597e2c37d96e38e54f05961d\n",
" Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
"Successfully built sentence_transformers\n",
"Installing collected packages: tokenizers, sentencepiece, transformers, sentence_transformers\n",
"Successfully installed sentence_transformers-2.2.2 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.29.2\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"from torch.utils.data import DataLoader\n",
"import math\n",
"from sentence_transformers import SentenceTransformer, LoggingHandler, losses, models, util\n",
"from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator\n",
"from sentence_transformers.readers import InputExample\n",
"import logging\n",
"from datetime import datetime"
],
"metadata": {
"id": "BXROqyqO822p"
},
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"source": [
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"device"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TNA_j5t7QrTg",
"outputId": "54d86822-b2aa-4915-8adf-2dcb6833b7f8"
},
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"device(type='cuda')"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"source": [
"model_name = 'Rajan/NepaliBERT'\n",
"\n",
"train_batch_size = 16\n",
"num_epochs = 8\n",
"model_save_path = 'output/sentence_transformer_nepali'"
],
"metadata": {
"id": "rNd6o8Su73Lm"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"word_embedding_model = models.Transformer(model_name)\n",
"pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), \n",
" pooling_mode_mean_tokens=True,\n",
" pooling_mode_cls_token=False,\n",
" pooling_mode_max_tokens=False,\n",
" )"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 220,
"referenced_widgets": [
"be5105b59501495e8e11da141dbcf5e7",
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},
"id": "zHF933dR8fPl",
"outputId": "87eb198e-0457-455f-971e-9fdf2a782b8a"
},
"execution_count": 23,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/569 [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "be5105b59501495e8e11da141dbcf5e7"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading pytorch_model.bin: 0%| | 0.00/328M [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "41419856109b49758f9182af84dd5e69"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at Rajan/NepaliBERT were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight']\n",
"- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertModel were not initialized from the model checkpoint at Rajan/NepaliBERT and are newly initialized: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)solve/main/vocab.txt: 0%| | 0.00/987k [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8d3aafb4e6bd48c4b18064f509099084"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"model = SentenceTransformer(modules=[word_embedding_model, pooling_model])\n",
"model.to(device)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cqG--k5r9cuU",
"outputId": "ecc5f497-d3a7-4d9f-db12-cbedf1e4ef41"
},
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"SentenceTransformer(\n",
" (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel \n",
" (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
")"
]
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"source": [
"input_example_samples = []\n",
"\n",
"for index, row in df.iterrows():\n",
" score = float(row['score']) / 5.0 # Normalize score between 0 to 1\n",
" inp_example = InputExample(texts=[row['sentence1'], row['sentence2']], label=score)\n",
"\n",
" input_example_samples.append(inp_example)"
],
"metadata": {
"id": "rkuGzfFO9rbv"
},
"execution_count": 25,
"outputs": []
},
{
"cell_type": "code",
"source": [
"len(input_example_samples)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ginh8gQXBpDR",
"outputId": "e650ee17-a51a-4c71-c21e-ea3b83babd33"
},
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"5749"
]
},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"source": [
"import random\n",
"\n",
"random.shuffle(input_example_samples)\n",
"\n",
"train_ratio = 0.8\n",
"test_ratio = 0.1\n",
"dev_ratio = 0.1\n",
"\n",
"num_examples = len(input_example_samples)\n",
"num_train = int(num_examples * train_ratio)\n",
"num_dev = int(num_examples * dev_ratio)\n",
"num_test = int(num_examples * test_ratio)\n",
"\n",
"\n",
"train_samples = input_example_samples[:num_train]\n",
"dev_samples = input_example_samples[num_train:num_train + num_dev]\n",
"test_samples = input_example_samples[num_train + num_dev:]\n",
"\n",
"\n"
],
"metadata": {
"id": "UPVQNUhaDyyX"
},
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(\"Train samples:\", len(train_samples))\n",
"print(\"Dev samples:\", len(dev_samples))\n",
"print(\"Test samples:\", len(test_samples))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BjwzPXkqD0ri",
"outputId": "95c7bbec-0c3f-4d82-fa39-05d8b64d3ad9"
},
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train samples: 4599\n",
"Dev samples: 574\n",
"Test samples: 576\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"train_dataloader = DataLoader(train_samples, shuffle=True, batch_size = train_batch_size)\n",
"train_loss = losses.CosineSimilarityLoss(model=model)"
],
"metadata": {
"id": "U9TyiDQyEzri"
},
"execution_count": 29,
"outputs": []
},
{
"cell_type": "code",
"source": [
"evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='stsb-dev-nepali')"
],
"metadata": {
"id": "6__-Dve0FEbB"
},
"execution_count": 30,
"outputs": []
},
{
"cell_type": "code",
"source": [
"warmup_steps = math.ceil(len(train_dataloader) * num_epochs * 0.1) # 10% of train data for warm-up"
],
"metadata": {
"id": "9_59Cnq2Fuh7"
},
"execution_count": 31,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model.fit(train_objectives=[(train_dataloader, train_loss)],\n",
" evaluator = evaluator,\n",
" epochs = num_epochs,\n",
" evaluation_steps = 1000,\n",
" warmup_steps = warmup_steps,\n",
" output_path = model_save_path\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 305,
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"id": "kp5FboP7F6aI",
"outputId": "1d113d58-c5af-47f8-dbda-4d30d0ddeb42"
},
"execution_count": 32,
"outputs": [
{
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"data": {
"text/plain": [
"Epoch: 0%| | 0/8 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
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},
{
"cell_type": "code",
"source": [
"!pip install huggingface_hub"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0D4DUTt4GWt2",
"outputId": "bf45e641-cce7-40cc-e7dc-d342bc5130e4"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.15.1)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.12.0)\n",
"Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.4.0)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.27.1)\n",
"Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.65.0)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.5.0)\n",
"Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.1)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (1.26.15)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2022.12.7)\n",
"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.12)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!huggingface-cli login"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GtfRTEd8bmA0",
"outputId": "001372b6-6d17-480b-e755-df15c5027e47"
},
"execution_count": 34,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
" _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
" _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
" _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
" _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
" \n",
" To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
"Token: \n",
"Add token as git credential? (Y/n) n\n",
"Token is valid (permission: write).\n",
"Your token has been saved to /root/.cache/huggingface/token\n",
"Login successful\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 113,
"referenced_widgets": [
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"outputId": "e1663053-3526-4b70-f6c2-6f24a6f7d47a"
},
"execution_count": 35,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"VBox(children=(HTML(value='