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(from typer->huggingface-hub>=0.24.0->datasets) (0.0.4)\n","Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub>=0.24.0->datasets) (4.0.0)\n","Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub>=0.24.0->datasets) (2.19.2)\n","Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface-hub>=0.24.0->datasets) (0.1.2)\n"]}],"source":["!pip install datasets tqdm"]},{"cell_type":"code","source":["\n","from datasets import load_dataset\n","from tqdm import tqdm\n","import json\n","import re\n","\n","OUTPUT_FILE_EN = \"dact_scientific_en.jsonl\"\n","OUTPUT_FILE_ID = \"dact_scientific_id.jsonl\"\n","MAX_WIKI = 200000\n","MAX_ARXIV = 200000\n","\n","SCIENCE_TERMS = [\n","    \"study\",\"experiment\",\"analysis\",\"method\",\"model\",\n","    \"data\",\"system\",\"theory\",\"research\",\"result\",\n","    \"algorithm\",\"network\",\"process\",\"measurement\",\n","    \"simulation\",\"parameter\",\"function\",\"variable\",\"physics\"\n","]\n","\n","\n","def clean_text(text):\n","\n","    text = re.sub(r\"\\s+\", \" \", text).strip()\n","\n","    if len(text.split()) < 30:\n","        return None\n","\n","    if \"isbn\" in text.lower():\n","        return None\n","\n","    return text\n","\n","\n","def scientific_filter(text):\n","    t = text.lower()\n","    return any(w in t for w in SCIENCE_TERMS)\n","\n","out_en = open(OUTPUT_FILE_ID, \"w\", encoding=\"utf-8\")\n","out_id = open(OUTPUT_FILE_ID, \"w\", encoding=\"utf-8\")\n","def write_line(text, out):\n","    out.write(json.dumps({\"text\": text}, ensure_ascii=False) + \"\\n\")\n","\n","print(\"Streaming Wikipedia EN...\")\n","\n","wiki_en = load_dataset(\n","    \"wikimedia/wikipedia\",\n","    \"20231101.en\",\n","    split=\"train\",\n","    streaming=True\n",")\n","\n","count = 0\n","\n","for item in tqdm(wiki_en):\n","\n","    text = clean_text(item[\"text\"])\n","    if not text:\n","        continue\n","\n","    if scientific_filter(text):\n","        write_line(text, out_en)\n","        count += 1\n","\n","    if count >= MAX_WIKI:\n","        break\n","\n","print(\"EN done:\", count)\n","\n","\n","print(\"Streaming Wikipedia ID...\")\n","\n","wiki_id = load_dataset(\n","    \"wikimedia/wikipedia\",\n","    \"20231101.id\",\n","    split=\"train\",\n","    streaming=True\n",")\n","\n","count = 0\n","\n","for item in tqdm(wiki_id):\n","\n","    text = clean_text(item[\"text\"])\n","    if not text:\n","        continue\n","\n","    if scientific_filter(text, out_id):\n","        write_line(text)\n","        count += 1\n","\n","    if count >= MAX_WIKI:\n","        break\n","\n","print(\"ID done:\", count)\n","\n","\n"],"metadata":{"id":"3Wbz0jbXeAtc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(\"Streaming arXiv abstracts...\")\n","\n","arxiv = load_dataset(\n","    \"ccdv/arxiv-summarization\",\n","    split=\"train\",\n","    streaming=True\n",")\n","\n","count = 0\n","\n","for item in tqdm(arxiv):\n","\n","    text = clean_text(item[\"abstract\"])\n","    if not text:\n","        continue\n","\n","    write_line(text,out_en)\n","    count += 1\n","\n","    if count >= MAX_ARXIV:\n","        break\n","\n","print(\"arXiv done:\", count)\n","\n","out.close()\n","\n","print(\"\\n DATASET SELESAI (LOW RAM MODE)\")\n","print(\"Output:\", OUTPUT_FILE)"],"metadata":{"id":"jU_hj1nUlnHu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from datasets import load_dataset, Dataset\n","from langdetect import detect, LangDetectException\n","from pathlib import Path\n","import os\n","\n","#data = load_dataset(\"Ik45/data-science-en-id\", verification_mode='no_checks') # Removed trust_remote_code\n","\n","def detect_and_tag_language(examples_en, examples_id):\n","    tagged_sentences = []\n","    for text_en in examples['text']:\n","        tagged_text_en = f'<en> {text_en}'\n","        tagged_sentences.append(tagged_text_en)\n","\n","\n","    for text_id in examples_id['text']:\n","        tagged_text_id = f'<id> {text_id}'\n","        tagged_sentences.append(tagged_text_id)\n","    return tagged_sentences\n","\n","out_data = open(\"dact_scientific.jsonl\",\"w\", encoding=\"utf-8\")\n","data_en = pd.read_json(\"/content/dact_scientific_en.jsonl\", lines=True)\n","data_id = pd.read_json(\"/content/dact_scientific_id.jsonl\", lines=True)\n","tag_data = detect_and_tag_language(data_en, data_id)\n","write_line(tag_data,out_data)\n"],"metadata":{"id":"l2V6SHLs4yqJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from datasets import load_dataset\n","dataset = load_dataset(\"json\", data_files=\"/content/dact_scientific.jsonl\")"],"metadata":{"id":"31UsAZU0asvS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","dataset = pd.DataFrame(dataset)\n","dataset.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"collapsed":true,"id":"pozlUbk4Ywey","executionInfo":{"status":"ok","timestamp":1773483397358,"user_tz":-420,"elapsed":24633,"user":{"displayName":"Pp Sdm","userId":"15268744271042622792"}},"outputId":"8814c3c5-e87d-4c5f-e7a7-95cdbe7486b9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                               train\n","0  {'text': 'Anarchism is a political philosophy ...\n","1  {'text': 'Albedo (; ) is the fraction of sunli...\n","2  {'text': 'A, or a, is the first letter and the...\n","3  {'text': 'Alabama () is a state in the Southea...\n","4  {'text': 'In Greek mythology, Achilles ( ) or ..."],"text/html":["\n","  <div 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'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-b1ed69a1-244c-4dde-b0a4-63d845b27fdc');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? 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en_core_web_sm"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1i6VK4xlVag8","executionInfo":{"status":"ok","timestamp":1773483001784,"user_tz":-420,"elapsed":13703,"user":{"displayName":"Pp Sdm","userId":"15268744271042622792"}},"outputId":"9e55a71d-9b05-4844-f40a-789c5d057b4b","collapsed":true},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: spacy in /usr/local/lib/python3.12/dist-packages (3.8.11)\n","Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.12/dist-packages (from spacy) (3.0.12)\n","Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.12/dist-packages (from spacy) (1.0.5)\n","Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.12/dist-packages (from spacy) (1.0.15)\n","Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.12/dist-packages (from spacy) 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/usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer>=0.24.0->typer-slim<1.0.0,>=0.3.0->spacy) (0.1.2)\n","Collecting en-core-web-sm==3.8.0\n","  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl (12.8 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m48.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}]},{"cell_type":"code","source":["import spacy\n","nlp = spacy.blank(\"en\")\n","nlp.add_pipe(\"sentencizer\")\n","def split_sentences(text):\n","    docs = []\n","    docs = list(nlp.pipe(text[\"text\"]))\n","\n","\n","\n","    sentences = []\n","\n","    for doc in docs:\n","        s = [sent.text.strip() for sent in doc.sents]\n","\n","        sentences.append(s)\n","\n","    return {\"sentences\" : sentences}\n","\n"],"metadata":{"id":"WAuY6At0aI59"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import os\n","\n","\n","df = dataset[\"train\"].map(split_sentences, remove_columns=[\"text\"], batched=True, num_proc=2 ) # Reduced batch_size\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":61,"referenced_widgets":["4af7401661a24fbf9dc6c4b1fe05036a","cd85c363db1d456cab80d0df26e26a29","794abebd7e2344d9a62f1155e8cdb87b","acfc703637cc428abf1df4ac901ce019","34a4b4abfd5e41f8abb132cc9f2ec373","15466250c08a4201b4e4be8fd0e42a65","8bca507fc2124567ab6890c9d0bf31cd","86de327269b04195845972cb8b471b1c","f8f35ee231ff4b0787795f5fc8282f6c","bea2e58da4574a6593ec465793c1d446","aceecf40c51e4e4ca2e74683fd67aa7d"]},"id":"yiapi3ThepKF","executionInfo":{"status":"ok","timestamp":1773507826184,"user_tz":-420,"elapsed":3618231,"user":{"displayName":"Pp Sdm","userId":"15268744271042622792"}},"outputId":"f73fe225-cc9b-43b7-ec93-057e49f2f99a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map (num_proc=2):   0%|          | 0/535762 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4af7401661a24fbf9dc6c4b1fe05036a"}},"metadata":{}}]},{"cell_type":"code","source":["import os\n","from itertools import chain\n","from datasets import Dataset, Features, Value, load_dataset\n","import hashlib # For hashing sentences for deduplication\n","from tqdm import tqdm # For progress bar\n","\n","\n","if 'df_hf' not in locals() and 'df_hf' not in globals():\n","    print(\"df_hf not found, loading from processed_data.parquet...\")\n","    df_hf = load_dataset(\"parquet\", data_files={\n","        'train': \"/content/drive/MyDrive/processed_data.parquet\"\n","    })['train']\n","# --- End of added code ---\n","\n","MIN_CHARS = 15  # Minimum characters (to avoid fragments)\n","MIN_WORDS = 5   # Minimum words (to ensure it's a sentence)\n","\n","print(f\"Initial dataset size: {len(df_hf)}\")\n","\n","\n","def map_and_flatten(example_batch):\n","    all_sentences = list(chain.from_iterable(example_batch['sentences']))\n","    return {'sentence_text': all_sentences}\n","\n","df_exploded = df_hf.map(\n","    map_and_flatten,\n","    batched=True,\n","    # Remove original columns as we are creating a new 'sentence_text' column\n","    remove_columns=df_hf.column_names,\n","    features=Features({'sentence_text': Value('string')}),\n","    num_proc=os.cpu_count() # Use multiple processes for parallel execution\n",")\n","\n","print(f\"Dataset size after exploding: {len(df_exploded)}\")\n","\n","# Step 2: Apply filters\n","def filter_sentences_func(example):\n","    text = example[\"sentence_text\"]\n","    if not text: # Handle potential empty strings\n","        return False\n","    # Filter: Remove data that is too short\n","    if len(text) < MIN_CHARS:\n","        return False\n","    # Filter: Remove data with too few words\n","    if len(text.split()) < MIN_WORDS:\n","        return False\n","    return True\n","\n","df_filtered = df_exploded.filter(filter_sentences_func, num_proc=os.cpu_count())\n","\n","print(f\"Dataset size after filtering: {len(df_filtered)}\")\n","\n","\n","\n","class Deduplicator:\n","    def __init__(self):\n","        self.seen_hashes = set()\n","\n","    def generate_unique_examples(self, iterable_dataset):\n","        # Removed total=len(iterable_dataset) because IterableDataset does not have a len()\n","        for example in tqdm(iterable_dataset):\n","            sentence_text = example['sentence_text']\n","            # Use a cryptographic hash for uniqueness\n","            sentence_hash = hashlib.sha256(sentence_text.encode('utf-8')).hexdigest()\n","            if sentence_hash not in self.seen_hashes:\n","                self.seen_hashes.add(sentence_hash)\n","                yield example\n","\n","# Create an iterable version of df_filtered\n","iterable_df_filtered = df_filtered.to_iterable_dataset()\n","deduplicator = Deduplicator()\n","df_final = Dataset.from_generator(\n","    deduplicator.generate_unique_examples,\n","    gen_kwargs={'iterable_dataset': iterable_df_filtered},\n","    features=Features({'sentence_text': Value('string')}) # Specify features for the generated dataset\n",")\n","\n","\n","print(f\"Final cleaned rows will be calculated during save or full iteration.\")\n","\n","# Now df_final is the cleaned datasets.Dataset. Save it to a Parquet file.\n","output_parquet_path = \"/content/drive/MyDrive/final_cleaned_sentences.parquet\"\n","print(f\"Saving final cleaned dataset to {output_parquet_path}...\")\n","df_final.to_parquet(output_parquet_path)\n","print(\"Save complete.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["43deca538d2545b1b5d519dbb914f027","f59d8ff473b24c5cbaf9a1ce8b565610","b9957b2e79ed48889200ee926059cdaa","58ab907817df4a64acde01d139991cf2","a777ba3f4c09468cb4dbbf415bb77a2d","7251f5b9d5204fce9591d8fd036da337","f62d166a03324fe88956c511f9faceb7","a3e014c426b04081b6fb83cf87f87977","ccaca57d8c6c44d6b9cc57fc51d7b532","17ec005af60e4eb885df3f15a4d11301","29db4d4dc1a4400b9d3ad0f9d0054530"]},"collapsed":true,"id":"YRcJwwUYXVOm","outputId":"fe663885-9855-413f-f7a5-b6ad7849870f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["df_hf not found, loading from processed_data.parquet...\n","Initial dataset size: 535762\n","Dataset size after exploding: 14506360\n","Dataset size after filtering: 13832335\n"]},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"43deca538d2545b1b5d519dbb914f027"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","7677593it [23:38, 4526.64it/s]\u001b[A\n","7678055it [23:38, 4223.72it/s]\u001b[A\n","7678486it [23:38, 4242.10it/s]\u001b[A\n","7678971it [23:39, 4406.06it/s]\u001b[A\n","7679442it [23:39, 4486.54it/s]\u001b[A\n","7679896it [23:39, 4438.71it/s]\u001b[A\n","7680344it [23:39, 4362.39it/s]\u001b[A\n","7680783it [23:39, 4139.29it/s]\u001b[A\n","7681201it [23:39, 3736.16it/s]\u001b[A\n","7681583it [23:39, 3655.91it/s]\u001b[A\n","7681957it [23:39, 3663.16it/s]\u001b[A\n","7682328it [23:39, 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load_dataset, Dataset # Import load_dataset\n","import os\n","\n","# Create the 'data' directory if it doesn't exist\n","os.makedirs(\"data\", exist_ok=True)\n","\n","\n","df_hf = load_dataset(\"parquet\", data_files={\n","    'train': \"/content/drive/MyDrive/final_cleaned_sentences.parquet\"\n","})['train']\n","\n","df_hf = df_hf.shuffle(seed=42)\n","print(len(df_hf))\n","# Define the number of shards\n","num_shards = 20\n","\n","# Iterate through shards using the memory-efficient Dataset.shard() method\n","for i in range(num_shards):\n","    # Get a specific shard of the dataset\n","    shard_dataset = df_hf.shard(num_shards=num_shards, index=i)\n","\n","    shard_df = shard_dataset.to_pandas()\n","    # Save the shard to a Parquet file with the specified naming convention\n","    shard_df.to_parquet(f\"data/train-{i:05d}-of-{num_shards:05d}.parquet\", index=False)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OVespKKKhtuc","outputId":"15c09d6a-a09f-43a0-acbd-c184829cb49a","executionInfo":{"status":"ok","timestamp":1773553933531,"user_tz":-420,"elapsed":122446,"user":{"displayName":"Pp Sdm","userId":"15268744271042622792"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["13494098\n"]}]},{"cell_type":"code","source":["from huggingface_hub import HfApi\n","api = HfApi()\n","api.upload_folder(\n","    folder_path=\"/content/data\",\n","    repo_id=\"Ik45/data-science-en-id\",\n","    repo_type=\"dataset\"\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["c7f11fb9c8a048b39c856e2ebc20c8de","17b785e8247d4823a446fa0effd410b6","af57f16889cd46cebe22d2f08f29ffeb","de7f458e293244ee962e881644ba18b6","be3fcd52214449018db651fc95c9c37b","7feaf91179a74f2f91cbdde66530ec3b","70e7e242fd514c16a371138a8c41aa19","426382cf0e344925952b7282338e4d9b","04b813857fac41f69156f0fbc6784367","19aaeff06c184068af270f41318c0354","6a0adec3e6b54d0e927d39c7c20ed34a","a6c5b527eda848f9877e1a4bfece5567","525a2149ec204bf999d3ebaede1201e4","5352dabba6cc4dd29930f6952959a64d","d21dadf838b34ea08c1664add65cc04d","cc773b41719f4a87adb104d219180128","9c79a2d55392469083770c3a50972a9e","0cc0e2c28cad44c2bb377461121a2964","6a7504fb152c40deb5004ba6ed3a02f0","471faa754c054012adbc65b4e0f94391","05b48a714e554823b07d9847919f5c80","b7186cd997ed4cfe8be16ffdb4e48c48","3981142bfba54471808c7b8849aea5d9","af25fbd426c74a51a4031727f8388588","a2fbca90c1d84163b0e5e021dd6dee78","ff5c156ff8e143bca0a1b35b73a364a7","41b90d21598c4c42bc69295487f0411e","8ce374602a8e4c5cb4db43fe641dc7fd","92bf90ce69304fd2815481dd88bd5bb8","503bd475b0ea430d833ffe918ab8599c","b61be17f4c0349afa117c82556447672","0ee997d543ba495db51535e4b9ce1866","b0a2abbcbeda4868996a866ab2270771","b86257518d264fff8a59c0c45adfe24c","87baa9d9cfc74e39a056500578abf660","2f3fc8361e9d46179c9d0c1692f79732","5ec547b9992d4ce6a3121318c73eb3bf","1bfdcc9bdb3a4cce8ffff22cea587acd","96105fe83979402bb3bd656f0dfb5604","5df915bcd10b4962a0acd8be33b9d3b2","91e06b915bfc48319cc6d4a04594d7ce","518d0969c4784ca084155f939e1b69ce","e3a646b6065448dea5b60da9fd654ba8","8d134141a287495b8ba37a3961252b93","052d75ecec8947b8ae95e5b13801df38","6d883698797c4d2aa5e3061b28ade4f8","91a314cc9d3e4fa19f862a32dfef2c88","f6cf56cb236046b6b8ba23c105659213","83c1222e362f452ca0062c6ae8d71d27","adfbf8f1527648d395422f8b432e948e","18ccf75040e84e6ba4da0c2190851ab2","597fa5de10784eceb11499d5aa0b2fcb","ec1e719f7ba64ef4a89b9b959fccc6f6","c13f5443d3fd429ba2e30a238fcd8b33","bd3ccb5e502e43289246429a12a0a4dc","13b90745275d44adb3bcadf35540e15a","f31f83f11e4840a8adc769e2a3af05f9","607360de66b0453d8c4315cf0a2c5ae3","6cfcdde9eff241d59cc7acedf5ba0fa3","8e15b0524e834cb595130e8ad687ea7d","2990c405caa54ef89c203f49319428fb","cca28c23d63f4909aae19c72e990f37b","b0305f25c3d740da8d05c60b6286249d","79257571a6c047fb866d660d0e39f6b4","1d6f3cfa44aa4526bdd45eca693f122e","5027710f45344ea5bec32be294ad0885","e514931b4809405db0c3ad5ea9351aed","5b75e64c58e14c72b517c00e2f30147f","dfd1c461c50a4070ba34c3b54cb0eaaa","8a453908fb7240bf94177e7186caa19f","3de1e536050241cf8a2b86c60ada1601","25af5f85bb814d84a9c874b0e7238e75","3801822985b245b88844c052f22deee2","bfdfbe28c8de464d9ab9b3a7b0fd308f","ca3051a8a4384997bffbd27c48d562d8","4f671678f51a42e4b32f8700611d9543","0d52a07c27f8450fbc5786d7443da7f7","5c3fb3e0ffd74a44ba8977b1311b7349","661fce9ce380458288ee03082495db3a","617a26b17189475fb62cddd7edb34817","a2458645e2f349b388eb7c8f5def6479","63e209881fd44ecabb55051de80ca2b8","5cf8cb6cff2b4de39eb831ec87abdc42","242de9f684dc4d66a6cda01a9756c5fb","5c62f64f69b544a28b554037635d6191","688e8e636be7419f8569cd90128a0bed","3e53adde9b644b7b857b7a20f29b6794","b18ce004945b4de1b1a68914f707c21b","272f72306c97467280c39d54e9bf15c2","d38e09a5446e4bbca3c0ce44bfc832cb","a0aee6b9b67d47ca87f04cd19338ce45","2d1b158642dd4fa8bd0fb43afd367184","ba42e9630a704eb599d6c42fa8b14685","3ce3455a31dd430c9f3f9c73aeedeace","a005dea0d67c4e4d8f48fd8ab9e1db5c","75048d71050146fca6722782c9b93160","c5c2a315d97046188cab9a5e1b8613f4","f90363937eeb45649018c1b68c82d606","87c278eaa6ff4b63b110834ba6c738c8","92f1d3deabc4496fb42883be2ebb6a4e","540125097aee414380e69b62f3077760","287a47003618478b96b5a951ddc9519a","d118cafb89b44a048179bc656e96618d","f80c805bb5614c9d925cc296e93ffae0","184ae56875914961808262c13e7c9b4f","005bb23939794eba9487cd40cd20b756","af7b14542a4e493d89efd0b8b4dd9d78","4a100ad818744dd995ef19e1b8070f56","86f2bf324f05455ea68ac927d9938c93","287e80a6fd9c46edb7a907b149bb5801","c43ebef49b644c039962314c014ce544","1b7d86a6b08444d886b039432aa0a47d","56c5d5d3b27a49108e597e9fdeb8d256","f90008de947e42848a529995f99237c8","6ae82312ba37442db99f2dd035df8bae","c946146648314c258608a1b7105f2f5d","45cf59593cf84954b9b9ee4e4eca10ab","854f7ab99e6741cf91723cca3cbb27b1","8281ae9873aa4f14a426a76d13eb4284","38f11a77382f43bd89611f1908b965a1","75ce99605fca4987b367c05ca55ce0ce","e8b0b20d8192471aaf0d33985164436a","4bea25a04efd4b38b5a360155f221a5f","b4999797c06a45f88c3e48c7aaa39919","94dce60a3958453b82d1d586b47ed889","dd2076977ed846309351d746d807889a","b6ac3d525c5c4baf8db72a347cff8f77","aefd43ebd2404d0287cc4758a2c1fda6","c7122661fddc41e19157452ac5529e2d","2b710a8c86a04b5a8668f73b4c004cef","ff578afcf4be41688ca68f27576bbd17","6453c3c5154b416dad5e799bb5150129"]},"id":"p4zkirodqEOp","executionInfo":{"status":"ok","timestamp":1773554035371,"user_tz":-420,"elapsed":80719,"user":{"displayName":"Pp 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oid='7fb7d48b4c46fe7b48a7eb690915d2f7e1e742b5', pr_url=None, repo_url=RepoUrl('https://huggingface.co/datasets/Ik45/data-science-en-id', endpoint='https://huggingface.co', repo_type='dataset', repo_id='Ik45/data-science-en-id'), pr_revision=None, pr_num=None)"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":3}]}]}