Delete files pre-tokenize.ipynb with huggingface_hub

pre-tokenize.ipynb

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-{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":487,"status":"ok","timestamp":1731397391515,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"},"user_tz":-345},"id":"hDoDQOpWUu_8","outputId":"2e9b9615-67f7-40fb-af4c-0b9732279281"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/Research/datasets/hf_datasets\n"]}],"source":["%cd /content/drive/MyDrive/Research/datasets/hf_datasets/"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vfKgZNKh0bcw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1731373007922,"user_tz":-345,"elapsed":17273,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"}},"outputId":"4f4e86be-f12a-476c-af3d-f0619689ea14"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/480.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m471.0/480.6 kB\u001b[0m \u001b[31m16.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/116.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0m"]}],"source":["!pip install datasets --quiet"]},{"cell_type":"markdown","metadata":{"id":"kEPBGuHzEkJV"},"source":["## Load dataset"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":177,"referenced_widgets":["06b9765683c642f68eeb26b1e060af9c","c14c6a1dbdbf4159a84634fa547ff525","2aea3d04aff3459ba96c5448d7387d52","e2da47d51bd5424ca70e464ca0b8d99f","aa1993f3e7004c209d99f604ed8b9f6a","5bf2b3e39e8b4c2f8caa70f210296b1d","36424d418dba452db968115f95f0e226","d5a96ee212e445878908eee3d29a9734","a90f621505ed4883ad2154ec8ff4d217","58d912802af84b62938da57ca65f695d","27450725a6764777a6bd1b13126b975e","e132fd5294594e29bc37a07e04520057","6ae25500c53e489b96c4adae0ef9cd6c","d3bd79e723774ebd84c57587e516c97d","3d41c6756d884a1d86554ab80b75d1de","4fbe8017df4f4359a39fcda288462611","61e93c25168b4dfcbee5b10173821e57","2fe0bd31d2bf40e3bfc611728097be26","28936b3277e4423ba78fe9b1289ab37f","f4d4e2a93bfc458f8ec52c5b8f4c4bce","d9b55352064a467ba22d803547b7fce4","e4acacc2f2394a7cbc0f1658f14089a0","ed6a24157b66400dbbef71a244e92647","87215a219a644abcbcd13661d2ef5b89","14f32efecb0446ee9cdb497f24309acb","697b8ff8aed340ee82cb1a28e7362b02","5830e2b499ec42fb9870bb3bc93bf2e8","b0316a2a0a494ade8e9f4191cffa2942","9b3fd8f71e9a43cda24b938327d59fa9","00e61ae2e2854730adebe28d256f949e","7bc90edb7f254345bdadadd11778f294","11993e12cf45444db680b260152c73aa","aedd3a3d9d1644dca3c0f69e5e85b07d","290332c0fde94f8da352429eebbf863b","ef95777adfc14f099eaa4a166df08eed","4d4dc624abf447ebaf895b95345de52c","a6fc17672ef5429d8c41a19c294afd71","76120e07c01840c59f07ef6d7d6a852a","9dd687e990104634a5afec6459b28075","8827e978cf444c1ea5f44e2eaab18d60","444b3254fec14166a846eca4653ee571","bff16f61acba400b92ebd3034aeb5496","9b7dc714600840c68a4dfd2b5de565a2","bc073a19673041458330a1ecc37dbf55","3977e625a173494c8fd5692f999a6008","8bb6dbdd43b94a608d9a3be1b23c7b9b","db129f3d4963426dad8b8316aaa52281","4046c3604add40e7a0fd939c99b88afd","d4c73efd4eef49b981a4642cd1d1624c","30b7db0dc0a744d48c202a70ddb3c9f0","e080f015a1774447a61f65bd0dc0741c","d5f827c83e93400b997417cfad281195","7d76e99418c04aa38c83826aeddbabcb","b0271bb5a9aa46e1ab08d9e425da9d1e","d2f97b4dad0e4679a638531804ee9fa8"]},"executionInfo":{"elapsed":8871,"status":"ok","timestamp":1731381633063,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"},"user_tz":-345},"id":"X7vSShNe00DI","outputId":"a5298688-93c8-4dbf-ce15-b0fa5ae98fbc"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Resolving data files:   0%|          | 0/184 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"06b9765683c642f68eeb26b1e060af9c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Resolving data files:   0%|          | 0/46 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e132fd5294594e29bc37a07e04520057"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Resolving data files:   0%|          | 0/184 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ed6a24157b66400dbbef71a244e92647"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Resolving data files:   0%|     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Tokenize"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_qvWzkow3MuH","outputId":"8e6b9932-9b71-4ef0-c890-eb86657b10b2","executionInfo":{"status":"ok","timestamp":1731394491039,"user_tz":-345,"elapsed":2070680,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["185... tokenized... df created:...saved!\n","186... tokenized... df created:...saved!\n","187... tokenized... df created:...saved!\n","188... tokenized... df created:...saved!\n","189... tokenized... df created:...saved!\n","190... tokenized... df created:...saved!\n","191... tokenized... df created:...saved!\n","192... tokenized... df created:...saved!\n","193... tokenized... df created:...saved!\n","194... tokenized... df created:...saved!\n","195... tokenized... df created:...saved!\n","196... tokenized... df created:...saved!\n","197... tokenized... df created:...saved!\n","198... 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pyarrow.parquet as pq\n","import pandas as pd\n","import pyarrow as pa\n","\n","def prepare_dataset(txt, tokenizer, max_length, stride, index):\n","    input_ids = []\n","    target_ids = []\n","    save_file = f\"pre_tokenized/nepberta_data_{index}.parquet\"\n","    token_ids = tokenizer.encode(txt)\n","\n","    for i in range(0, len(token_ids) - max_length, stride):\n","        input_chunk = token_ids[i:i + max_length]\n","        target_chunk = token_ids[i + 1: i + max_length + 1]\n","\n","        input_ids.append(input_chunk)\n","        target_ids.append(target_chunk)\n","\n","    print('... tokenized', end='')\n","    df = pd.DataFrame({\n","      \"input_ids\": input_ids,\n","      \"target_ids\": target_ids\n","    })\n","    print('... df created', end='')\n","\n","    # Convert DataFrame to pyarrow Table\n","    table = pa.Table.from_pandas(df)\n","\n","    # # Try to read existing Parquet file\n","    # try:\n","    #     existing_table = pq.read_table(save_file)\n","    #     # Concatenate existing table with new table\n","    #     table = pa.concat_tables([existing_table, table])\n","    # except FileNotFoundError:\n","    #     print('file not found')  # File does not exist, so just write the new table\n","\n","    # Write table to Parquet file\n","    pq.write_table(table, save_file, compression='snappy')\n","    print(':...saved!')\n","\n","# Writing each batch to the Parquet file\n","# i=0\n","# for data in train_data:\n","#     # combine two chunks\n","#     i+=1;print(i, end='')\n","#     prepare_dataset(data['text'], tokenizer, max_length=1024, stride=1024, index=i)\n","i=len(train_data)\n","for data in test_data:\n","    # combine two chunks\n","    i+=1;print(i, end='')\n","    prepare_dataset(data['text'], tokenizer, max_length=1024, stride=1024, index=i)"]},{"cell_type":"code","source":["len(train_data) + len(test_data)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xJVZWDjXB2sF","executionInfo":{"status":"ok","timestamp":1731395569536,"user_tz":-345,"elapsed":396,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"}},"outputId":"d9dbd062-b168-45ff-c969-9b0c120ab562"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["230"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["# Merge the .parquet files generated\n","import pyarrow.parquet as pq\n","import glob\n","import pyarrow as pa\n","\n","# Specify the input folder and output file\n","input_folder = 'pre_tokenized'\n","output_file = 'nepberta.parquet'\n","\n","# Get a list of all Parquet files in the input folder\n","parquet_files = glob.glob(f'{input_folder}/*.parquet')\n","print(len(parquet_files))\n","\n","# Write each file to the output file one at a time\n","writer = None\n","i=0\n","for file in parquet_files:\n","    i+=1;print(i)\n","    table = pq.read_table(file)\n","    if writer is None:\n","        # Initialize the writer with the schema of the first file\n","        writer = pq.ParquetWriter(output_file, table.schema)\n","    writer.write_table(table)\n","\n","# Close the writer\n","if writer:\n","    writer.close()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WMsVPT7uQECp","executionInfo":{"status":"ok","timestamp":1731396442727,"user_tz":-345,"elapsed":149532,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"}},"outputId":"6308599c-f0b1-408b-8083-31ecfec6e531"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["230\n","1\n","2\n","3\n","4\n","5\n","6\n","7\n","8\n","9\n","10\n","11\n","12\n","13\n","14\n","15\n","16\n","17\n","18\n","19\n","20\n","21\n","22\n","23\n","24\n","25\n","26\n","27\n","28\n","29\n","30\n","31\n","32\n","33\n","34\n","35\n","36\n","37\n","38\n","39\n","40\n","41\n","42\n","43\n","44\n","45\n","46\n","47\n","48\n","49\n","50\n","51\n","52\n","53\n","54\n","55\n","56\n","57\n","58\n","59\n","60\n","61\n","62\n","63\n","64\n","65\n","66\n","67\n","68\n","69\n","70\n","71\n","72\n","73\n","74\n","75\n","76\n","77\n","78\n","79\n","80\n","81\n","82\n","83\n","84\n","85\n","86\n","87\n","88\n","89\n","90\n","91\n","92\n","93\n","94\n","95\n","96\n","97\n","98\n","99\n","100\n","101\n","102\n","103\n","104\n","105\n","106\n","107\n","108\n","109\n","110\n","111\n","112\n","113\n","114\n","115\n","116\n","117\n","118\n","119\n","120\n","121\n","122\n","123\n","124\n","125\n","126\n","127\n","128\n","129\n","130\n","131\n","132\n","133\n","134\n","135\n","136\n","137\n","138\n","139\n","140\n","141\n","142\n","143\n","144\n","145\n","146\n","147\n","148\n","149\n","150\n","151\n","152\n","153\n","154\n","155\n","156\n","157\n","158\n","159\n","160\n","161\n","162\n","163\n","164\n","165\n","166\n","167\n","168\n","169\n","170\n","171\n","172\n","173\n","174\n","175\n","176\n","177\n","178\n","179\n","180\n","181\n","182\n","183\n","184\n","185\n","186\n","187\n","188\n","189\n","190\n","191\n","192\n","193\n","194\n","195\n","196\n","197\n","198\n","199\n","200\n","201\n","202\n","203\n","204\n","205\n","206\n","207\n","208\n","209\n","210\n","211\n","212\n","213\n","214\n","215\n","216\n","217\n","218\n","219\n","220\n","221\n","222\n","223\n","224\n","225\n","226\n","227\n","228\n","229\n","230\n"]}]},{"cell_type":"code","source":["# Delete the original files (after merging)\n","import os\n","for file in parquet_files:\n","    try:\n","        os.remove(file)\n","        print(f\"Deleted file: {file}\")\n","    except OSError as e:\n","        print(f\"Error deleting file: {file} - {e.strerror}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cL6mGBtuXrY4","executionInfo":{"status":"ok","timestamp":1731396568736,"user_tz":-345,"elapsed":1765,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"}},"outputId":"18d682fd-78bf-4b5e-ca9a-7850b81cd84f"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Deleted file: pre_tokenized/nepberta_data_1.parquet\n","Deleted file: pre_tokenized/nepberta_data_2.parquet\n","Deleted file: pre_tokenized/nepberta_data_3.parquet\n","Deleted file: pre_tokenized/nepberta_data_4.parquet\n","Deleted file: pre_tokenized/nepberta_data_5.parquet\n","Deleted file: pre_tokenized/nepberta_data_6.parquet\n","Deleted file: 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_|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n","    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n","    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n","    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n","\n","    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n","Enter your token (input will not be visible): \n","Add token as git credential? (Y/n) y\n","Token is valid (permission: write).\n","Your token has been saved in your configured git credential helpers (store).\n","Your token has been saved to /root/.cache/huggingface/token\n","Login successful\n"]}],"source":["!huggingface-cli login"]},{"cell_type":"code","source":["!git add pre_tokenized"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k1X5B-54TgzH","executionInfo":{"status":"ok","timestamp":1731397041531,"user_tz":-345,"elapsed":446,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"}},"outputId":"86778243-efea-43d3-ea7b-744b55fde257"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["shell-init: error retrieving current directory: getcwd: cannot access parent directories: Transport endpoint is not connected\n","fatal: Unable to read current working directory: Transport endpoint is not connected\n"]}]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":71},"executionInfo":{"elapsed":486,"status":"ok","timestamp":1731397095505,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"},"user_tz":-345},"id":"X2bY6_zqUszv","outputId":"10c27d28-dab5-4112-c7f0-b13e95c32c4c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["RepoUrl('https://huggingface.co/datasets/Aananda-giri/nepali_llm_datasets', endpoint='https://huggingface.co', repo_type='dataset', repo_id='Aananda-giri/nepali_llm_datasets')"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":14}],"source":["# Upload to Hugging Face\n","from huggingface_hub import HfApi, Repository\n","import os\n","import shutil\n","\n","# Set your dataset repo name and Hugging Face username\n","username = \"Aananda-giri\"  # Replace with your Hugging Face username\n","repo_name = \"nepali_llm_datasets\"  # Name for the new dataset repository\n","repo_id = f\"{username}/{repo_name}\"  # Full repository ID\n","\n","# Authenticate\n","api = HfApi()\n","# token = \"token\"  # Replace with your Hugging Face token\n","\n","# Local dataset directory\n","local_dir = \"./\"  # Adjust to the path of the dataset folder\n","\n","# Create the repository if it does not exist\n","api.create_repo(repo_id, token=token, repo_type=\"dataset\", exist_ok=True)\n","\n","# # Clone the repo from Hugging Face Hub if it does not already exist locally\n","repo = Repository(\n","    local_dir=local_dir,\n","    clone_from=repo_id,  # Use `clone_from` to set up repository cloning\n","    token=token,\n","    repo_type=\"dataset\"\n",")\n","\n","# Add all files and folders to the Git repository\n","repo.git_add(\".\")  # Stage all files\n","repo.git_commit(\"Add all dataset files and folders\")  # Commit the changes\n","\n","# # Push the current structure to the Hugging Face Hub\n","# repo.push_to_hub(commit_message=\"Initial upload of structured Nepali LLM dataset folders\")\n","\n","# print(f\"Dataset uploaded to https://huggingface.co/datasets/{repo_id}\")\n"]},{"cell_type":"markdown","source":["## update README.md"],"metadata":{"id":"0zV7WbWKSwk9"}},{"cell_type":"code","source":["!ls  pre_tokenized"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"M4Yz3W_hUwJV","executionInfo":{"status":"ok","timestamp":1731397407717,"user_tz":-345,"elapsed":420,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"}},"outputId":"0926767d-c231-4d72-bc33-fdfb8ca5fa90"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["nepberta.parquet  pre-tokenize.ipynb\n"]}]},{"cell_type":"code","source":["from huggingface_hub import HfApi\n","api = HfApi()\n","\n","from google.colab import userdata\n","\n","\n","# Upload all the content from the local folder to your remote Space.\n","# By default, files are uploaded at the root of the repo\n","api.upload_folder(\n","    folder_path=\"./pre_tokenized\",\n","    repo_id=\"Aananda-giri/nepali_llm_datasets\",\n","    repo_type=\"dataset\",\n","    token=userdata.get('HF_TOKEN')\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["8e91fe272a8b431e9148c220d00845c9","608cdce93e2c4418ac12b38344906258","4baec1a84bc74ae6abfa8fdb85c2f2e7","f4170f4604f04c0f98b7819ac29e1e08","bc7c4e61167e4c908e073670a65c5eca","d43ccf4170434ae385ca6c70e28036c5","d0a13a0032a644928ab03ea9b541c6c9","d0590236d7f9415aa9fbe2532eeb2b80","37586cef5e8c4c9382919c183fc1efa9","ec8d2cdc02ba4d179b521a10d3ba95de","0185e7e9f75545ec977e0be4a1c4b720"]},"id":"3UzfiGemUohk","outputId":"c8c75621-2fe1-4abf-b664-8857c4f23c16"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["nepberta.parquet:   0%|          | 0.00/2.93G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8e91fe272a8b431e9148c220d00845c9"}},"metadata":{}}]},{"cell_type":"markdown","source":["## load from huggingface"],"metadata":{"id":"Gy-Cp8-D5YyE"}},{"cell_type":"code","source":[],"metadata":{"id":"hNGEFTUG5eZd"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WUtyTCCIOw1y"},"source":["# Example: append to parquet"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"x_QmHyzZOhlr"},"outputs":[],"source":["import pyarrow as pa\n","import pyarrow.parquet as pq\n","\n","# Sample data loader simulation\n","# Let's create a list of dictionaries to simulate batches of data with text to be tokenized\n","# In practice, each `data` entry could represent a batch, for simplicity here, we'll use one item per batch\n","\n","data_loader = [\n","    {'text': \"The quick brown fox\"},\n","    {'text': \"jumps over the lazy dog\"},\n","    {'text': \"and runs away quickly\"},\n","    {'text': \"The sun sets behind the mountains\"},\n","    {'text': \"A quick brown dog jumps over the lazy fox\"},\n","]\n","\n","# Define a simple tokenization function for demonstration\n","def get_tokenized(text):\n","    # Simulating tokenization to input_ids and target_ids (just for example)\n","    input_ids = [ord(char) for char in text]  # Convert each character to ASCII value\n","    target_ids = [ord(char) + 1 for char in text]  # Shift ASCII values by 1\n","    return input_ids, target_ids\n","\n","# Define the schema based on `input_ids` and `target_ids` being lists of integers\n","schema = pa.schema([\n","    ('input_ids', pa.list_(pa.int64())),\n","    ('target_ids', pa.list_(pa.int64()))\n","])\n","\n","# Path for the Parquet file\n","parquet_file_path = \"nepberta_data.parquet\"\n","\n","# Writing each batch to the Parquet file\n","with pq.ParquetWriter(parquet_file_path, schema) as writer:\n","    for data in data_loader:\n","        # Tokenize the text to get input_ids and target_ids\n","        input_ids, target_ids = get_tokenized(data['text'])\n","\n","        # because dataloader seeks same sized data in a batch\n","        input_ids = input_ids[:18]\n","        target_ids = target_ids[:18]\n","\n","        # Create a record batch for the current data\n","        batch = pa.RecordBatch.from_arrays([pa.array([input_ids]), pa.array([target_ids])], schema=schema)\n","\n","        # Write batch to Parquet file\n","        writer.write_table(pa.Table.from_batches([batch]))\n","\n","parquet_file_path\n"]},{"cell_type":"markdown","metadata":{"id":"SZMpCwMiEefH"},"source":["## upload to huggingface"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"k16WfpRRDy8B"},"outputs":[],"source":["%cd /content/drive/MyDrive/Research/datasets/hf_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ruCVcH8WDknh"},"outputs":[],"source":["from huggingface_hub import HfApi, Repository\n","import os\n","import shutil\n","\n","# Set your dataset repo name and Hugging Face username\n","username = \"Aananda-giri\"  # Replace with your Hugging Face username\n","repo_name = \"nepali_llm_datasets\"  # Name for the new dataset repository\n","repo_id = f\"{username}/{repo_name}\"  # Full repository ID\n","\n","# Authenticate\n","api = HfApi()\n","# token = \"token\"  # Replace with your Hugging Face token\n","\n","# Local dataset directory\n","local_dir = \"./\"  # Adjust to the path of the dataset folder\n","\n","# Create the repository if it does not exist\n","api.create_repo(repo_id, token=token, repo_type=\"dataset\", exist_ok=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FDsSoN5dEdlK"},"outputs":[],"source":["# Local dataset directory\n","local_dir = \"./\"  # Path to your dataset folder\n","\n","# Upload all files and folders recursively\n","for root, dirs, files in os.walk(local_dir):\n","    for file in files:\n","        file_path = os.path.join(root, file)\n","        # Upload file to the specified repo with the same directory structure\n","        repo_file_path = os.path.relpath(file_path, local_dir)\n","        api.upload_file(\n","            path_or_fileobj=file_path,\n","            path_in_repo=repo_file_path,\n","            repo_id=repo_id,\n","            repo_type=\"dataset\",\n","            token=token\n","        )\n","\n","print(f\"Dataset uploaded to https://huggingface.co/datasets/{repo_id}\")"]},{"cell_type":"markdown","metadata":{"id":"53UCcTChEFLK"},"source":["# Example: upload to huggingface"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":292},"executionInfo":{"elapsed":5873,"status":"ok","timestamp":1731311746054,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"},"user_tz":-345},"id":"u36PC3MdDT5D","outputId":"5c704375-d58b-4350-c76d-1162471acf01"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"26432447ae53469895afc04ffd009346","version_major":2,"version_minor":0},"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"36b9df2f161c41a3926d2389a8a17ae8","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":"45f99d0efc6f49549d0b7c69278265c5","version_major":2,"version_minor":0},"text/plain":["Creating parquet from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"db4d1925e07640ffaa551f0a59046d49","version_major":2,"version_minor":0},"text/plain":["README.md:   0%|          | 0.00/310 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["No files have been modified since last commit. Skipping to prevent empty commit.\n","WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n"]},{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["CommitInfo(commit_url='https://huggingface.co/datasets/Aananda-giri/tokenized_dataset_parquet/commit/677a7569e167fdce921e3a2150342fb407025f3e', commit_message='Upload dataset', commit_description='', oid='677a7569e167fdce921e3a2150342fb407025f3e', pr_url=None, pr_revision=None, pr_num=None)"]},"execution_count":56,"metadata":{},"output_type":"execute_result"}],"source":["from datasets import load_dataset\n","from google.colab import userdata\n","# Load Parquet file as a Dataset\n","dataset = load_dataset(\"parquet\", data_files=\"*.parquet\")\n","\n","# Push to Hugging Face Hub\n","dataset.push_to_hub(\"Aananda-giri/tokenized_dataset_parquet\", token=userdata.get('HUGGING_FACE_BEARER'))\n"]},{"cell_type":"markdown","metadata":{"id":"C56FQLi_EvS5"},"source":[" # Example: load from huggingface"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":54},"executionInfo":{"elapsed":2699,"status":"ok","timestamp":1731311851804,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"},"user_tz":-345},"id":"V4wlCSvJEyT6","outputId":"bd1d8b44-5e22-4574-a5cb-b30640f74d65"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["\"\\nDatasetDict({\\n    train: Dataset({\\n        features: ['input_ids', 'target_ids'],\\n        num_rows: 3\\n    })\\n})\\n\""]},"execution_count":60,"metadata":{},"output_type":"execute_result"}],"source":["from datasets import load_dataset\n","\n","ds = load_dataset(\"Aananda-giri/tokenized_dataset_parquet\")\n","ds\n","'''\n","DatasetDict({\n","    train: Dataset({\n","        features: ['input_ids', 'target_ids'],\n","        num_rows: 3\n","    })\n","})\n","'''"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":118,"status":"ok","timestamp":1731311851806,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"},"user_tz":-345},"id":"OO3YP4PhFVWv","outputId":"30fd32c3-fb0b-4b02-b701-16ca388689b4"},"outputs":[{"name":"stdout","output_type":"stream","text":["{'input_ids': [84, 104, 101, 32, 113, 117, 105, 99, 107, 32, 98, 114, 111, 119, 110, 32, 102, 111], 'target_ids': [85, 105, 102, 33, 114, 118, 106, 100, 108, 33, 99, 115, 112, 120, 111, 33, 103, 112]}\n","{'input_ids': [106, 117, 109, 112, 115, 32, 111, 118, 101, 114, 32, 116, 104, 101, 32, 108, 97, 122], 'target_ids': [107, 118, 110, 113, 116, 33, 112, 119, 102, 115, 33, 117, 105, 102, 33, 109, 98, 123]}\n","{'input_ids': [97, 110, 100, 32, 114, 117, 110, 115, 32, 97, 119, 97, 121, 32, 113, 117, 105, 99], 'target_ids': [98, 111, 101, 33, 115, 118, 111, 116, 33, 98, 120, 98, 122, 33, 114, 118, 106, 100]}\n","{'input_ids': [84, 104, 101, 32, 115, 117, 110, 32, 115, 101, 116, 115, 32, 98, 101, 104, 105, 110], 'target_ids': [85, 105, 102, 33, 116, 118, 111, 33, 116, 102, 117, 116, 33, 99, 102, 105, 106, 111]}\n","{'input_ids': [65, 32, 113, 117, 105, 99, 107, 32, 98, 114, 111, 119, 110, 32, 100, 111, 103, 32], 'target_ids': [66, 33, 114, 118, 106, 100, 108, 33, 99, 115, 112, 120, 111, 33, 101, 112, 104, 33]}\n"]}],"source":["for data in ds['train']:\n","    print(data)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":617,"status":"ok","timestamp":1731311870239,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"},"user_tz":-345},"id":"YOgWCteSOmFQ","outputId":"4f0b5ab7-88a0-4b12-f799-b71593ac7dec"},"outputs":[{"name":"stdout","output_type":"stream","text":["18 18\n","18 18\n","18 18\n","18 18\n","18 18\n"]}],"source":["for data in ds['train']:\n","  print(len(data['input_ids']), len(data['target_ids']))\n","  # print(data)"]},{"cell_type":"markdown","metadata":{"id":"mepgfJGyH8rx"},"source":["# Example: Our dataloader with huggingface datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2584,"status":"ok","timestamp":1731311778025,"user":{"displayName":"Aananda Giri","userId":"10248715758347187876"},"user_tz":-345},"id":"D5ZIHHIqIBlL","outputId":"e9eeae4c-e184-42a2-e31d-c702f5db4206"},"outputs":[{"name":"stdout","output_type":"stream","text":["[tensor([[106, 117, 109, 112, 115,  32, 111, 118, 101, 114,  32, 116, 104, 101,\n","          32, 108,  97, 122],\n","        [ 97, 110, 100,  32, 114, 117, 110, 115,  32,  97, 119,  97, 121,  32,\n","         113, 117, 105,  99],\n","        [ 84, 104, 101,  32, 115, 117, 110,  32, 115, 101, 116, 115,  32,  98,\n","         101, 104, 105, 110],\n","        [ 84, 104, 101,  32, 113, 117, 105,  99, 107,  32,  98, 114, 111, 119,\n","         110,  32, 102, 111]]), tensor([[107, 118, 110, 113, 116,  33, 112, 119, 102, 115,  33, 117, 105, 102,\n","          33, 109,  98, 123],\n","        [ 98, 111, 101,  33, 115, 118, 111, 116,  33,  98, 120,  98, 122,  33,\n","         114, 118, 106, 100],\n","        [ 85, 105, 102,  33, 116, 118, 111,  33, 116, 102, 117, 116,  33,  99,\n","         102, 105, 106, 111],\n","        [ 85, 105, 102,  33, 114, 118, 106, 100, 108,  33,  99, 115, 112, 120,\n","         111,  33, 103, 112]])]\n"]}],"source":["from torch.utils.data import DataLoader\n","from datasets import load_dataset\n","import torch\n","\n","# Load the dataset from Hugging Face\n","ds = load_dataset(\"Aananda-giri/tokenized_dataset_parquet\")\n","\n","# Define the custom collate_fn function\n","def collate_fn(batch):\n","    # Extract the 'input_ids' and 'target_ids' from the batch and return them as a list of tensors\n","    input_ids = [item['input_ids'] for item in batch]\n","    target_ids = [item['target_ids'] for item in batch]\n","\n","    # Convert to tensors (if not already)\n","    input_ids_tensor = torch.stack(input_ids)\n","    target_ids_tensor = torch.stack(target_ids)\n","\n","    return [input_ids_tensor, target_ids_tensor]\n","\n","# Convert Hugging Face Dataset to PyTorch tensors (we can directly use the dataset as it is already in the correct format)\n","ds.set_format(type=\"torch\", columns=[\"input_ids\", \"target_ids\"])  # Directly set columns to torch tensors\n","\n","# Creating the DataLoader for the 'train' split of the dataset with the custom collate_fn\n","dataloader = DataLoader(\n","    ds[\"train\"],\n","    batch_size=4,\n","    shuffle=True,\n","    drop_last=True,\n","    num_workers=0,\n","    collate_fn=collate_fn\n",")\n","\n","# Example of accessing one batch\n","for batch in dataloader:\n","    print(batch)\n","    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