{"cells":[{"cell_type":"markdown","metadata":{"id":"4t3eRIwJjLUd"},"source":["# Config Setup"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":127492,"status":"ok","timestamp":1745243278667,"user":{"displayName":"Judd Marc","userId":"09923286896387104894"},"user_tz":-180},"id":"LOVNadmhuVjz","outputId":"34c8a608-cbde-4fcc-9378-ad0c83072056"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.51.3)\n","Requirement already satisfied: sentencepiece in /usr/local/lib/python3.11/dist-packages (0.2.0)\n","Collecting datasets\n"," Downloading datasets-3.5.0-py3-none-any.whl.metadata (19 kB)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.18.0)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.30.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.30.2)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (2.0.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (24.2)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from transformers) (6.0.2)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (2024.11.6)\n","Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from transformers) (2.32.3)\n","Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.21.1)\n","Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.5.3)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.11/dist-packages (from transformers) (4.67.1)\n","Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (18.1.0)\n","Collecting dill<0.3.9,>=0.3.0 (from datasets)\n"," Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n","Collecting xxhash (from datasets)\n"," Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n","Collecting multiprocess<0.70.17 (from datasets)\n"," Downloading multiprocess-0.70.16-py311-none-any.whl.metadata (7.2 kB)\n","Collecting fsspec<=2024.12.0,>=2023.1.0 (from fsspec[http]<=2024.12.0,>=2023.1.0->datasets)\n"," Downloading fsspec-2024.12.0-py3-none-any.whl.metadata (11 kB)\n","Requirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from datasets) (3.11.15)\n","Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (2.6.1)\n","Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.3.2)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (25.3.0)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.5.0)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (6.4.3)\n","Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (0.3.1)\n","Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.19.0)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.30.0->transformers) (4.13.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.4.1)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2.3.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2025.1.31)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n","Downloading datasets-3.5.0-py3-none-any.whl (491 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m491.2/491.2 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading fsspec-2024.12.0-py3-none-any.whl (183 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m183.9/183.9 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading multiprocess-0.70.16-py311-none-any.whl (143 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.5/143.5 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta 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This behaviour is the source of the following dependency conflicts.\n","gcsfs 2025.3.2 requires fsspec==2025.3.2, but you have fsspec 2024.12.0 which is incompatible.\n","torch 2.6.0+cu124 requires nvidia-cublas-cu12==12.4.5.8; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cublas-cu12 12.5.3.2 which is incompatible.\n","torch 2.6.0+cu124 requires nvidia-cuda-cupti-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-cupti-cu12 12.5.82 which is incompatible.\n","torch 2.6.0+cu124 requires nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-nvrtc-cu12 12.5.82 which is incompatible.\n","torch 2.6.0+cu124 requires nvidia-cuda-runtime-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cuda-runtime-cu12 12.5.82 which is incompatible.\n","torch 2.6.0+cu124 requires nvidia-cudnn-cu12==9.1.0.70; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cudnn-cu12 9.3.0.75 which is incompatible.\n","torch 2.6.0+cu124 requires nvidia-cufft-cu12==11.2.1.3; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cufft-cu12 11.2.3.61 which is incompatible.\n","torch 2.6.0+cu124 requires nvidia-curand-cu12==10.3.5.147; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-curand-cu12 10.3.6.82 which is incompatible.\n","torch 2.6.0+cu124 requires nvidia-cusolver-cu12==11.6.1.9; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusolver-cu12 11.6.3.83 which is incompatible.\n","torch 2.6.0+cu124 requires nvidia-cusparse-cu12==12.3.1.170; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-cusparse-cu12 12.5.1.3 which is incompatible.\n","torch 2.6.0+cu124 requires nvidia-nvjitlink-cu12==12.4.127; platform_system == \"Linux\" and platform_machine == \"x86_64\", but you have nvidia-nvjitlink-cu12 12.5.82 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed datasets-3.5.0 dill-0.3.8 fsspec-2024.12.0 multiprocess-0.70.16 xxhash-3.5.0\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n","Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.6.0+cu124)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch) (3.18.0)\n","Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from torch) (4.13.2)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch) (3.4.2)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.6)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch) (2024.12.0)\n","Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch)\n"," Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch)\n"," Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch)\n"," Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch)\n"," Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cublas-cu12==12.4.5.8 (from torch)\n"," Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cufft-cu12==11.2.1.3 (from torch)\n"," Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-curand-cu12==10.3.5.147 (from torch)\n"," Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch)\n"," Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch)\n"," Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n","Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch) (0.6.2)\n","Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch) (2.21.5)\n","Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n","Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch)\n"," Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch) (3.2.0)\n","Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch) (1.13.1)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch) (3.0.2)\n","Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m53.3 MB/s\u001b[0m eta 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nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m70.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n"," Attempting uninstall: nvidia-nvjitlink-cu12\n"," Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n"," Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n"," Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n"," Attempting uninstall: nvidia-curand-cu12\n"," Found existing installation: nvidia-curand-cu12 10.3.6.82\n"," Uninstalling nvidia-curand-cu12-10.3.6.82:\n"," Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n"," Attempting uninstall: nvidia-cufft-cu12\n"," Found existing installation: nvidia-cufft-cu12 11.2.3.61\n"," Uninstalling nvidia-cufft-cu12-11.2.3.61:\n"," Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n"," Attempting uninstall: nvidia-cuda-runtime-cu12\n"," Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n"," Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n"," Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n"," Attempting uninstall: nvidia-cuda-nvrtc-cu12\n"," Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n"," Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n"," Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n"," Attempting uninstall: nvidia-cuda-cupti-cu12\n"," Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n"," Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n"," Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n"," Attempting uninstall: nvidia-cublas-cu12\n"," Found existing installation: nvidia-cublas-cu12 12.5.3.2\n"," Uninstalling nvidia-cublas-cu12-12.5.3.2:\n"," Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n"," Attempting uninstall: nvidia-cusparse-cu12\n"," Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n"," Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n"," Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n"," Attempting uninstall: nvidia-cudnn-cu12\n"," Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n"," Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n"," Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n"," Attempting uninstall: nvidia-cusolver-cu12\n"," Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n"," Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n"," Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n","Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n","Collecting sacrebleu\n"," Downloading sacrebleu-2.5.1-py3-none-any.whl.metadata (51 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.8/51.8 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting portalocker (from sacrebleu)\n"," Downloading portalocker-3.1.1-py3-none-any.whl.metadata (8.6 kB)\n","Requirement already satisfied: regex in /usr/local/lib/python3.11/dist-packages (from sacrebleu) (2024.11.6)\n","Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.11/dist-packages (from sacrebleu) (0.9.0)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from sacrebleu) (2.0.2)\n","Collecting colorama (from sacrebleu)\n"," Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)\n","Requirement already satisfied: lxml in /usr/local/lib/python3.11/dist-packages (from sacrebleu) (5.3.2)\n","Downloading sacrebleu-2.5.1-py3-none-any.whl (104 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.1/104.1 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n","Downloading portalocker-3.1.1-py3-none-any.whl (19 kB)\n","Installing collected packages: portalocker, colorama, sacrebleu\n","Successfully installed colorama-0.4.6 portalocker-3.1.1 sacrebleu-2.5.1\n"]}],"source":["! pip install transformers sentencepiece datasets\n","! pip install tqdm\n","! pip install torch\n","!pip install sacrebleu"]},{"cell_type":"markdown","metadata":{"id":"zJC5YliTjCyC"},"source":["# Huggingface Configuration"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17,"referenced_widgets":["0873702b270f44ac8d403d0f99071b85","0b6b39f74eaf4f88b7d3f61cabacb0e2","49e30da2bece411e8b9694f82c45d4c3","df6ed50562094937a11564a84af307b0","9e7c6b0af2ca41989e7c8d91e841c2a2","955464dbf9b947b98f1b624ee9b3e91e","c739646664eb4ebb9fa75ec379f90742","58a9c1e2ed5a4d8d898151cd316d9b38","ea26eccbc6b54b40bc6d5acd0df31ffd","2c30a0e71754492ea5f83c9474ded504","4cc6ba92ee2e431ca58b1d149cec2bba","13fa0cd4033b4ede881a6c5363040d07","bfc46a69fc3b4de0990cda3ca422c1da","79d871b05ff441acbc6966320816bd70","b507639502064738b7095f409cce89cc","01f97991c9e1461294b6cf8bef9dca24","fd121d1c5c244a6d9db2cce5d2ccb173","67e52e14229e44898003134e8e3bddde","efec379b17154562a54c1bf4a445b1e8","b31d8f6b308640e5922ce8c24300bbef"]},"executionInfo":{"elapsed":678,"status":"ok","timestamp":1745243279400,"user":{"displayName":"Judd Marc","userId":"09923286896387104894"},"user_tz":-180},"id":"JPXDbcI8kz3j","outputId":"648d6cf8-5ab3-47d7-9937-b175a443621b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value='
',\n"," 'en': ''\n","\n","}\n","\n","api = HfApi() # Initialize the API client once\n","repo_id=\"JMwagunda/Trial\"\n","tokenizer = AutoTokenizer.from_pretrained(model_name)\n","model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n","model.to(device)\n","\n","repo_name=\"JMwagunda/Trial\"\n","checkpoint_file = \"checkpoint.pth\"\n","\n","# Add special tokens\n","special_tokens = {'additional_special_tokens': list(lang_token.values())}\n","tokenizer.add_special_tokens(special_tokens)\n","model.resize_token_embeddings(len(tokenizer))\n","\n","# Load the dataset and check available splits\n","ds = load_dataset('Rogendo/English-Swahili-Sentence-Pairs')\n","print(\"Available splits:\", ds.keys())\n","\n","\n","# Calculate the sizes based on percentages\n","total_rows = len(ds['train'])\n","train_size = int(0.8 * total_rows) # 80% for training\n","validation_size = int(0.1 * total_rows) # 10% for validation\n","test_size = total_rows - train_size - validation_size # Remaining 10% for test\n","\n","# Shuffle the dataset to ensure randomness in splitting\n","ds_shuffled = ds['train'].shuffle(seed=42)\n","\n","# Create the splits based on the specified sizes\n","train_dataset = ds_shuffled.select(range(train_size)) # train_dataset should be a dataset, not an int\n","validation_dataset = ds_shuffled.select(range(train_size, train_size + validation_size))\n","test_dataset = ds_shuffled.select(range(train_size + validation_size, train_size + validation_size + test_size))\n","\n","new_dataset = DatasetDict({\n"," 'train': train_dataset,\n"," 'validation': validation_dataset,\n"," 'test': test_dataset\n","})\n","\n","# Print the new dataset structure\n","print(new_dataset)"]},{"cell_type":"markdown","metadata":{"id":"d2ge1G8ei3-t"},"source":["# Preprocessor"]},{"cell_type":"code","source":["import torch\n","from typing import Dict, List, Tuple, Optional, Union, Iterator\n","from dataclasses import dataclass\n","from transformers import AutoTokenizer\n","from torch.utils.data import Dataset, DataLoader\n","from enum import Enum\n","\n","class TranslationDirection(Enum):\n"," \"\"\"Enum for translation directions\"\"\"\n"," SW2EN = \"sw2en\"\n"," EN2SW = \"en2sw\"\n","\n","\n","@dataclass\n","class TranslationPair:\n"," \"\"\"Data class for holding translation pairs.\"\"\"\n"," source_text: str\n"," target_text: str\n"," source_lang: str\n"," target_lang: str\n","\n"," def __post_init__(self):\n"," \"\"\"Validate inputs upon initialization.\"\"\"\n"," # Convert None to empty string to avoid attribute errors\n"," self.source_text = str(self.source_text or \"\")\n"," self.target_text = str(self.target_text or \"\")\n"," self.source_lang = str(self.source_lang or \"\")\n"," self.target_lang = str(self.target_lang or \"\")\n","\n"," def is_valid(self) -> bool:\n"," \"\"\"Check if the translation pair is valid for training.\"\"\"\n"," return (bool(self.source_text.strip()) and\n"," bool(self.target_text.strip()) and\n"," bool(self.source_lang.strip()) and\n"," bool(self.target_lang.strip()))\n","\n","class TranslationPreprocessor:\n"," def __init__(self, tokenizer: AutoTokenizer, lang_tokens: Dict[str, str], max_length: int):\n"," \"\"\"\n"," Initialize the preprocessor.\n","\n"," Args:\n"," tokenizer: HuggingFace tokenizer\n"," lang_tokens: Dictionary mapping language codes to tokens\n"," max_length: Maximum sequence length\n"," \"\"\"\n"," self.tokenizer = tokenizer\n"," self.lang_tokens = lang_tokens\n"," self.max_length = max_length\n"," self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n"," self.key_mapping = {\n"," TranslationDirection.SW2EN.value: (\"Swahili Translation\", \"English sentence\"),\n"," TranslationDirection.EN2SW.value: (\"English sentence\", \"Swahili Translation\")\n"," }\n","\n"," def add_language_token(self, text: str, lang_code: str) -> str:\n"," \"\"\"Prepend language-specific token to text.\"\"\"\n"," return f\"{self.lang_tokens[lang_code]} {text}\"\n","\n"," def encode_input_str(self, text: str, target_lang: str, tokenizer: AutoTokenizer, seq_len: int, lang_token: Dict[str, str]) -> torch.Tensor:\n"," \"\"\"Encode input string with source language token\"\"\"\n"," # Use source language token (English)\n"," source_lang_token = lang_token['sw']\n","\n"," # Tokenize with English token\n"," input_ids = tokenizer.encode(\n"," text=f\"{source_lang_token} {text}\",\n"," return_tensors='pt',\n"," padding='max_length',\n"," truncation=True,\n"," max_length=seq_len\n"," )\n","\n"," return input_ids[0]\n","\n"," def encode_target_str(self, text: str, tokenizer: AutoTokenizer, seq_len: int, lang_token: Dict[str, str]) -> torch.Tensor:\n"," \"\"\"Encode target string with target language token\"\"\"\n"," # For target, we'll keep the Giriama token as it's the target language\n"," target_lang_token = lang_token['en']\n","\n"," token_ids = tokenizer.encode(\n"," text=f\"{target_lang_token} {text}\",\n"," return_tensors='pt',\n"," padding='max_length',\n"," truncation=True,\n"," max_length=seq_len\n"," )\n","\n"," return token_ids[0]\n","\n"," def format_translation_data(self, translations: Dict[str, str], lang_token: Dict[str, str], tokenizer: AutoTokenizer, seq_len: int = 20) -> Optional[Tuple[torch.Tensor, torch.Tensor]]:\n"," \"\"\"Format translation data for model input\"\"\"\n"," # Define possible input-output language pairs and randomly select one\n"," input_lang, target_lang = 'sw', 'en'\n","\n"," # Map language codes to column names\n"," column_map = {\n"," 'sw': 'Swahili Translation',\n"," 'en': 'English sentence'\n"," }\n","\n"," # Extract the translations based on the chosen direction\n"," input_text = translations.get(column_map[input_lang])\n"," target_text = translations.get(column_map[target_lang])\n","\n"," # Check for any missing translations\n"," if input_text is None or target_text is None:\n"," return None\n","\n"," # Tokenize input with English as source language\n"," input_token_ids = self.encode_input_str(\n"," text=input_text,\n"," target_lang='en',\n"," tokenizer=tokenizer,\n"," seq_len=seq_len,\n"," lang_token=lang_token\n"," )\n","\n"," # Tokenize target text\n"," target_token_ids = self.encode_target_str(\n"," text=target_text,\n"," tokenizer=tokenizer,\n"," seq_len=seq_len,\n"," lang_token=lang_token\n"," )\n","\n"," return input_token_ids, target_token_ids\n","\n"," def transform_batch(self, batch: Dict[str, List[str]], lang_token: Dict[str, str], tokenizer: AutoTokenizer, max_length: int) -> Optional[Tuple[torch.Tensor, torch.Tensor]]:\n"," \"\"\"Transform a batch of translations\"\"\"\n"," inputs = []\n"," targets = []\n","\n"," # Get all English Sentences and Giriama translations from the batch\n"," swahili_translations = batch['Swahili Translation']\n"," english_sentences = batch['English sentence']\n","\n","\n"," # Process each pair in the batch\n"," for eng, sw in zip(english_sentences, swahili_translations):\n"," translation_set = {\n"," 'Swahili Translation': sw,\n"," 'English sentence': eng\n","\n"," }\n","\n"," # Process the translation pair\n"," formatted_data = self.format_translation_data(\n"," translation_set,\n"," lang_token,\n"," tokenizer,\n"," max_length\n"," )\n","\n"," if formatted_data is None:\n"," continue\n","\n"," input_ids, target_ids = formatted_data\n"," inputs.append(input_ids.unsqueeze(0))\n"," targets.append(target_ids.unsqueeze(0))\n","\n"," if not inputs or not targets: # Check if we have any valid translations\n"," return None\n","\n"," # Concatenate tensors for batch\n"," device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n"," batch_input_ids = torch.cat(inputs).to(device)\n"," batch_target_ids = torch.cat(targets).to(device)\n","\n"," return batch_input_ids, batch_target_ids\n","\n"," def get_data_generator(self, dataset, lang_token: Dict[str, str], tokenizer: AutoTokenizer, batch_size: int = 8, direction: TranslationDirection = TranslationDirection.SW2EN) -> Iterator[Tuple[torch.Tensor, torch.Tensor]]:\n"," \"\"\"Generate batches of processed translation data\"\"\"\n"," # dataset = dataset.shuffle()\n"," for i in range(0, len(dataset), batch_size):\n"," end_idx = min(i + batch_size, len(dataset))\n"," batch = dataset[i:end_idx]\n"," batch_dict = {\n"," 'Swahili Translation': batch['Swahili Translation'],\n"," 'English sentence': batch['English sentence']\n"," }\n"," processed_batch = self.transform_batch(batch, lang_token, tokenizer, self.max_length)\n"," if processed_batch is not None:\n"," yield processed_batch\n","\n"," def process_translation_pair(self, pair: TranslationPair) -> Tuple[torch.Tensor, torch.Tensor]:\n"," \"\"\"Process a single translation pair\"\"\"\n"," # Add language tokens to source and target texts\n"," source_text_with_token = self.add_language_token(pair.source_text, pair.source_lang)\n"," target_text_with_token = self.add_language_token(pair.target_text, pair.target_lang)\n","\n"," # Tokenize source and target texts\n"," source_ids = self.tokenizer.encode(\n"," source_text_with_token,\n"," return_tensors='pt',\n"," padding='max_length',\n"," truncation=True,\n"," max_length=self.max_length\n"," )\n","\n"," target_ids = self.tokenizer.encode(\n"," target_text_with_token,\n"," return_tensors='pt',\n"," padding='max_length',\n"," truncation=True,\n"," max_length=self.max_length\n"," )\n","\n"," return source_ids[0], target_ids[0]"],"metadata":{"id":"1DTZQgbS8Phl","executionInfo":{"status":"ok","timestamp":1745243400122,"user_tz":-180,"elapsed":19,"user":{"displayName":"Judd Marc","userId":"09923286896387104894"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ExKaNdSRiz9f"},"source":["# Optimizer\n"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"FpWqrHk7eXWw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1745243400142,"user_tz":-180,"elapsed":15,"user":{"displayName":"Judd Marc","userId":"09923286896387104894"}},"outputId":"5cff2477-d7c2-4588-c14d-f52064f5c37d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Step 0:\n"," Base LR: 0.00e+00\n"," Scheduler LR: 0.00e+00\n","Step 1:\n"," Base LR: 4.75e-09\n"," Scheduler LR: 4.75e-09\n","Step 2:\n"," Base LR: 9.50e-09\n"," Scheduler LR: 9.50e-09\n","Step 3:\n"," Base LR: 1.43e-08\n"," Scheduler LR: 1.43e-08\n","Step 4:\n"," Base LR: 1.90e-08\n"," Scheduler LR: 1.90e-08\n"]}],"source":["from transformers import get_cosine_schedule_with_warmup\n","from torch.optim import AdamW\n","import math\n","import numpy as np\n","\n","# Configurable parameters\n","learning_rate = 5e-5\n","epsilon_value = 1e-8\n","batch_size = 8\n","num_epochs = 10\n","\n","# Optimizer setup\n","optimizer = AdamW(\n"," model.parameters(),\n"," lr=learning_rate,\n"," eps=epsilon_value,\n",")\n","\n","# Calculate steps\n","n_batches = int(np.ceil(len(train_dataset) / batch_size))\n","total_steps = num_epochs * n_batches\n","num_warmup_steps = int(0.05 * total_steps) # 5% warmup\n","\n","# Use standard cosine scheduler\n","scheduler = get_cosine_schedule_with_warmup(\n"," optimizer,\n"," num_warmup_steps=num_warmup_steps,\n"," num_training_steps=total_steps\n",")\n","\n","# Debugging function\n","def print_lr_details(optimizer, scheduler, step):\n"," print(f\"Step {step}:\")\n"," print(f\" Base LR: {optimizer.param_groups[0]['lr']:.2e}\")\n"," print(f\" Scheduler LR: {scheduler.get_last_lr()[0]:.2e}\")\n","\n","# Example usage (replace with your actual training loop)\n","for step in range(5):\n"," print_lr_details(optimizer, scheduler, step)\n"," optimizer.step()\n"," scheduler.step()"]},{"cell_type":"markdown","source":["# Checkpoint Manager\n"],"metadata":{"id":"oBC0yjRDFRGT"}},{"cell_type":"code","execution_count":6,"metadata":{"id":"NU8DRmxNyzZ-","executionInfo":{"status":"ok","timestamp":1745243400171,"user_tz":-180,"elapsed":24,"user":{"displayName":"Judd Marc","userId":"09923286896387104894"}}},"outputs":[],"source":["import torch\n","from huggingface_hub import HfApi, hf_hub_download\n","import os\n","import gc\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","class CheckpointManager:\n"," def __init__(self, model, optimizer, scheduler, tokenizer, repo_name, device, branch_name=\"main\"):\n"," self.model = model\n"," self.optimizer = optimizer\n"," self.scheduler = scheduler\n"," self.tokenizer = tokenizer\n"," self.repo_name = repo_name\n"," self.device = device\n"," self.branch_name = branch_name\n"," self.api = HfApi()\n"," self.batches_per_epoch = None\n","\n"," def set_dataset_info(self, num_samples, batch_size):\n"," \"\"\"Store dataset info for accurate step calculations\"\"\"\n"," self.batches_per_epoch = int(np.ceil(num_samples / batch_size))\n"," self.batch_size = batch_size\n"," self.num_samples = num_samples\n"," print(f\"CheckpointManager: Batches per epoch set to {self.batches_per_epoch}\")\n","\n","\n"," def save_checkpoint(self, epoch, batch_idx, losses, total_steps=None, steps_taken=None, repo_name=None, branch_name=None, is_best=False):\n"," repo_name = repo_name or self.repo_name\n"," branch_name = branch_name or self.branch_name\n","\n"," try:\n"," # Calculate steps_taken if not provided, using the stored batches_per_epoch\n"," if steps_taken is None and self.batches_per_epoch is not None:\n"," steps_taken = epoch * self.batches_per_epoch + batch_idx\n"," print(f\"Calculated steps_taken for saving: {steps_taken}\")\n","\n"," # Get current learning rates\n"," current_lr = self.optimizer.param_groups[0]['lr']\n"," scheduler_lr = self.scheduler.get_last_lr()[0]\n","\n"," # Prepare the checkpoint dictionary with enhanced information\n"," checkpoint = {\n"," 'model_state_dict': self.model.state_dict(),\n"," 'optimizer_state_dict': self.optimizer.state_dict(),\n"," 'scheduler_state_dict': self.scheduler.state_dict(),\n"," 'epoch': epoch,\n"," 'batch': batch_idx,\n"," 'losses': losses,\n"," 'current_lr': current_lr,\n"," 'scheduler_lr': scheduler_lr,\n"," 'total_steps': total_steps,\n"," 'batches_per_epoch': self.batches_per_epoch,\n"," 'steps_taken': steps_taken if steps_taken is not None else (epoch * batch_idx),\n"," 'is_best': is_best\n"," }\n","\n"," # Save the checkpoint locally\n"," checkpoint_filename = 'best_checkpoint.pth' if is_best else 'checkpoint.pth'\n"," torch.save(checkpoint, checkpoint_filename)\n"," print(f\"{'Best m' if is_best else 'M'}odel checkpoint saved locally with learning rate: {current_lr:.2e}, scheduler lr: {scheduler_lr:.2e}\")\n"," print(f\"Saved at epoch {epoch + 1}, batch {batch_idx}, steps_taken: {steps_taken}\")\n","\n"," # Ensure all tensors in the model are contiguous\n"," for param in self.model.parameters():\n"," param.data = param.data.contiguous()\n","\n"," # Upload model to Hugging Face Hub\n"," commit_message = f\"{'Best m' if is_best else 'M'}odel checkpoint after epoch {epoch + 1}, batch {batch_idx}\"\n"," self.model.push_to_hub(\n"," repo_id=repo_name,\n"," commit_message=commit_message,\n"," use_temp_dir=True,\n"," revision=branch_name\n"," )\n","\n"," # Upload tokenizer to Hugging Face Hub\n"," self.tokenizer.push_to_hub(\n"," repo_id=repo_name,\n"," revision=branch_name\n"," )\n","\n"," # Upload checkpoint file to Hugging Face Hub\n"," self.api.upload_file(\n"," path_or_fileobj=checkpoint_filename,\n"," path_in_repo=checkpoint_filename,\n"," repo_id=repo_name,\n"," revision=branch_name\n"," )\n","\n"," # Upload notebook if available\n"," notebook_path = \"/content/drive/MyDrive/Colab Notebooks/progress.ipynb\"\n"," if os.path.exists(notebook_path):\n"," self.api.upload_file(\n"," path_or_fileobj=notebook_path,\n"," path_in_repo=\"current_notebook.ipynb\",\n"," repo_id=repo_name,\n"," revision=branch_name\n"," )\n","\n"," print(f\"Checkpoint file uploaded to Hugging Face Hub.\")\n"," print(f\"{'Best m' if is_best else 'M'}odel checkpoint saved to Hugging Face Hub after epoch {epoch + 1}, batch {batch_idx}\")\n","\n"," except Exception as e:\n"," print(f\"Error saving checkpoint: {e}\")\n","\n"," def load_checkpoint(self, repo_name=None, branch_name=None):\n"," repo_name = repo_name or self.repo_name\n"," branch_name = branch_name or self.branch_name\n","\n"," try:\n"," # Clear memory before loading checkpoint\n"," torch.cuda.empty_cache()\n"," gc.collect()\n","\n"," # Check if the checkpoint file exists in the repo\n"," if self.checkpoint_exists(repo_name, \"checkpoint.pth\", branch_name):\n"," # Download the checkpoint file\n"," checkpoint_path = hf_hub_download(\n"," repo_id=repo_name,\n"," filename=\"checkpoint.pth\",\n"," revision=branch_name\n"," )\n","\n"," # Load the checkpoint\n"," checkpoint = torch.load(checkpoint_path, map_location=self.device)\n","\n"," # Store learning rate before loading for comparison\n"," prev_lr = self.optimizer.param_groups[0]['lr'] if self.optimizer else None\n"," prev_scheduler_lr = self.scheduler.get_last_lr()[0] if self.scheduler else None\n","\n"," # Load model, optimizer, and scheduler states\n"," self.model.load_state_dict(checkpoint['model_state_dict'])\n"," self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n"," self.scheduler.load_state_dict(checkpoint['scheduler_state_dict'])\n","\n"," # Load training state\n"," epoch_idx = checkpoint['epoch']\n"," batch_idx = checkpoint['batch']\n"," losses = checkpoint['losses']\n","\n"," # Extract additional information (with backward compatibility)\n"," current_lr = checkpoint.get('current_lr', self.optimizer.param_groups[0]['lr'])\n"," scheduler_lr = checkpoint.get('scheduler_lr', self.scheduler.get_last_lr()[0])\n"," steps_taken = checkpoint.get('steps_taken', None)\n"," total_steps = checkpoint.get('total_steps', None)\n"," is_best = checkpoint.get('is_best', False)\n","\n"," if steps_taken is None and self.batches_per_epoch is not None:\n"," # Calculate steps_taken from epoch and batch_idx\n"," steps_taken = epoch_idx * self.batches_per_epoch + batch_idx\n"," print(f\"Calculated steps_taken from epoch/batch: {steps_taken}\")\n","\n"," # Verify learning rates\n"," print(f\"LR before checkpoint loading: {prev_lr:.2e}, After: {self.optimizer.param_groups[0]['lr']:.2e}\")\n"," print(f\"Scheduler LR before: {prev_scheduler_lr:.2e}, After: {self.scheduler.get_last_lr()[0]:.2e}\")\n"," print(f\"Checkpoint saved with LR: {current_lr:.2e}, Scheduler LR: {scheduler_lr:.2e}\")\n","\n"," # Verify if the learning rates match up\n"," if abs(self.optimizer.param_groups[0]['lr'] - current_lr) > 1e-8:\n"," print(\"WARNING: Current learning rate doesn't match the checkpoint's saved learning rate!\")\n","\n"," print(f\"Loaded checkpoint from epoch {epoch_idx + 1}, batch {batch_idx}\")\n"," if is_best:\n"," print(\"This is marked as the best checkpoint with lowest validation loss.\")\n","\n"," if steps_taken is not None:\n"," print(f\"Steps taken according to checkpoint: {steps_taken}\")\n"," if self.batches_per_epoch is not None:\n"," expected_steps = epoch_idx * self.batches_per_epoch + batch_idx\n"," if expected_steps != steps_taken:\n"," print(f\"WARNING: Expected steps ({expected_steps}) doesn't match steps_taken in checkpoint ({steps_taken})!\")\n","\n"," return epoch_idx, batch_idx, losses, steps_taken, total_steps\n","\n"," # Try loading the best checkpoint if regular checkpoint not found\n"," elif self.checkpoint_exists(repo_name, \"best_checkpoint.pth\", branch_name):\n"," print(\"Regular checkpoint not found. Attempting to load best checkpoint.\")\n"," checkpoint_path = hf_hub_download(\n"," repo_id=repo_name,\n"," filename=\"best_checkpoint.pth\",\n"," revision=branch_name\n"," )\n","\n"," # Process the best checkpoint (similar logic as above)\n"," checkpoint = torch.load(checkpoint_path, map_location=self.device)\n","\n"," # Store learning rate before loading for comparison\n"," prev_lr = self.optimizer.param_groups[0]['lr'] if self.optimizer else None\n"," prev_scheduler_lr = self.scheduler.get_last_lr()[0] if self.scheduler else None\n","\n"," # Load model, optimizer, and scheduler states\n"," self.model.load_state_dict(checkpoint['model_state_dict'])\n"," self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n"," self.scheduler.load_state_dict(checkpoint['scheduler_state_dict'])\n","\n"," # Load training state\n"," epoch_idx = checkpoint['epoch']\n"," batch_idx = checkpoint['batch']\n"," losses = checkpoint['losses']\n","\n"," # Extract additional information\n"," current_lr = checkpoint.get('current_lr', self.optimizer.param_groups[0]['lr'])\n"," scheduler_lr = checkpoint.get('scheduler_lr', self.scheduler.get_last_lr()[0])\n"," steps_taken = checkpoint.get('steps_taken', None)\n"," total_steps = checkpoint.get('total_steps', None)\n","\n"," print(f\"Loaded best checkpoint from epoch {epoch_idx + 1}, batch {batch_idx}\")\n","\n"," return epoch_idx, batch_idx, losses, steps_taken, total_steps\n"," else:\n"," print(\"No checkpoint file found in the repository.\")\n","\n"," except Exception as e:\n"," print(f\"Error loading checkpoint: {e}\")\n"," torch.cuda.empty_cache()\n"," gc.collect()\n","\n"," print(\"No checkpoint found or error occurred. Starting training from scratch.\")\n"," return 0, 0, [], 0, None\n","\n"," def checkpoint_exists(self, repo_name, filename, branch_name):\n"," try:\n"," # List all files in the specified branch\n"," files = self.api.list_repo_files(repo_id=repo_name, revision=branch_name)\n"," return filename in files\n"," except Exception as e:\n"," print(f\"Error checking for checkpoint: {e}\")\n"," return False"]},{"cell_type":"markdown","metadata":{"id":"1kRcC8c-p_b8"},"source":["# Evaluate"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"URWKI37kp9nt","executionInfo":{"status":"ok","timestamp":1745243400189,"user_tz":-180,"elapsed":15,"user":{"displayName":"Judd Marc","userId":"09923286896387104894"}}},"outputs":[],"source":["def eval_model(model, val_dataset, tokenizer, LANG_TOKEN_MAPPING):\n"," model.eval()\n"," total_val_loss = 0\n"," val_data_generator = preprocessor.get_data_generator(\n"," val_dataset, LANG_TOKEN_MAPPING, tokenizer, batch_size=batch_size\n"," )\n"," total_val_batches = len(val_dataset) // batch_size\n"," if len(val_dataset) % batch_size != 0:\n"," total_val_batches += 1\n","\n"," with torch.no_grad():\n"," for batch_idx, (input_batch, label_batch) in enumerate(val_data_generator):\n"," input_batch = input_batch.to(device)\n"," label_batch = label_batch.to(device)\n"," outputs = model(input_ids=input_batch, labels=label_batch)\n"," total_val_loss += outputs.loss.item()\n","\n"," avg_val_loss = total_val_loss / total_val_batches\n"," return avg_val_loss"]},{"cell_type":"markdown","metadata":{"id":"2fiODOvRjnYV"},"source":["# Bleue Score"]},{"cell_type":"code","source":["import sacrebleu\n","\n","def calculate_bleu(predictions, references):\n"," \"\"\"\n"," Calculate BLEU score using sacrebleu.\n","\n"," Args:\n"," predictions: List of predicted translations\n"," references: List of reference translations\n","\n"," Returns:\n"," BLEU score\n"," \"\"\"\n"," # Convert single references to the format expected by sacrebleu\n"," refs = [[ref] for ref in references]\n","\n"," # Calculate BLEU score\n"," bleu = sacrebleu.corpus_bleu(predictions, refs)\n","\n"," return bleu.score"],"metadata":{"id":"M7Gbg99IaQet","executionInfo":{"status":"ok","timestamp":1745243400271,"user_tz":-180,"elapsed":84,"user":{"displayName":"Judd Marc","userId":"09923286896387104894"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["%matplotlib inline\n","from transformers import get_cosine_schedule_with_warmup\n","import numpy as np\n","from tqdm import tqdm\n","import matplotlib.pyplot as plt\n","import random\n","\n","def plot_training_history(history):\n"," \"\"\"\n"," Plot training loss, validation loss, and BLEU scores.\n","\n"," Args:\n"," history: Dictionary containing training and validation metrics.\n"," \"\"\"\n"," plt.figure(figsize=(12, 6))\n","\n"," # Plot training and validation loss\n"," plt.subplot(1, 2, 1)\n"," plt.plot(history['train_loss'], label='Training Loss')\n"," plt.plot(history['val_loss'], label='Validation Loss')\n"," plt.xlabel('Epoch')\n"," plt.ylabel('Loss')\n"," plt.title('Training and Validation Loss')\n"," plt.legend()\n","\n"," # Plot BLEU score\n"," plt.subplot(1, 2, 2)\n"," plt.plot(history['val_bleu'], label='Validation BLEU', color='green')\n"," plt.xlabel('Epoch')\n"," plt.ylabel('BLEU Score')\n"," plt.title('Validation BLEU Score')\n"," plt.legend()\n","\n"," plt.tight_layout()\n"," plt.show()"],"metadata":{"id":"xRqAgFY8TmIM","executionInfo":{"status":"ok","timestamp":1745243400289,"user_tz":-180,"elapsed":21,"user":{"displayName":"Judd Marc","userId":"09923286896387104894"}}},"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"j1o67awKjrVs"},"source":["# Training loop"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"sih3aURBjs4O","executionInfo":{"status":"ok","timestamp":1745243400343,"user_tz":-180,"elapsed":69,"user":{"displayName":"Judd Marc","userId":"09923286896387104894"}}},"outputs":[],"source":["def train_model(\n"," model, train_dataset, val_dataset, optimizer, scheduler, num_epochs, device, tokenizer,\n"," preprocessor, LANG_TOKEN_MAPPING, batch_size=8, max_length=128,\n"," learning_rate=5e-5, repo_name=\"JMwagunda/Trial\" # Reduced default learning rate\n","):\n"," \"\"\"\n"," Train the model with checkpointing, validation, and BLEU score calculation.\n"," \"\"\"\n"," # Memory management and initialization\n"," torch.cuda.empty_cache()\n"," gc.collect()\n","\n"," # Move model to device\n"," model.to(device)\n","\n"," # Store training history\n"," history = {\n"," 'train_loss': [],\n"," 'val_loss': [],\n"," 'val_bleu': [],\n"," }\n","\n"," # Calculate total batches and steps\n"," n_batches = int(np.ceil(len(train_dataset) / batch_size))\n"," total_steps = num_epochs * n_batches\n","\n"," # Reduced warmup ratio to 3%\n"," num_warmup_steps = int(0.03 * total_steps) # 3% warmup\n"," print(f\"Number of warmup steps: {num_warmup_steps}\")\n"," print(f\"Total training steps: {total_steps}\")\n"," print(f\"Batches per epoch: {n_batches}\")\n","\n"," # Initialize CheckpointManager\n"," checkpoint_manager = CheckpointManager(\n"," model=model,\n"," optimizer=optimizer,\n"," scheduler=scheduler,\n"," tokenizer=tokenizer,\n"," repo_name=repo_name,\n"," device=device\n"," )\n","\n"," # Set dataset info for accurate calculations\n"," checkpoint_manager.set_dataset_info(len(train_dataset), batch_size)\n","\n"," # Load checkpoint if it exists - pass dataset info here\n"," start_epoch, start_batch, checkpoint_losses, steps_taken, checkpoint_total_steps = checkpoint_manager.load_checkpoint()\n","\n"," # Check if total_steps has changed and print warning\n"," if checkpoint_total_steps is not None and checkpoint_total_steps != total_steps:\n"," print(f\"WARNING: Total steps in checkpoint ({checkpoint_total_steps}) differs from current setting ({total_steps})\")\n","\n"," # Manually adjust the scheduler state if resuming from a checkpoint\n"," if start_epoch > 0 or start_batch > 0:\n"," current_steps = steps_taken if steps_taken is not None else (start_epoch * n_batches + start_batch)\n"," expected_steps = start_epoch * n_batches + start_batch\n","\n"," print(f\"Checkpoint indicates {current_steps} steps taken, calculated steps: {expected_steps}\")\n","\n"," # If steps_taken is not available, manually step the scheduler\n"," if steps_taken is None:\n"," print(f\"Stepping scheduler {expected_steps} times to match checkpoint position\")\n"," # Reset scheduler to initial state\n"," scheduler = get_cosine_schedule_with_warmup(\n"," optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=total_steps\n"," )\n"," # Step to match checkpoint position\n"," for _ in range(expected_steps):\n"," scheduler.step()\n","\n"," print(f\"Learning rate after scheduler adjustment: {optimizer.param_groups[0]['lr']:.2e}\")\n"," print(f\"Scheduler learning rate after adjustment: {scheduler.get_last_lr()[0]:.2e}\")\n","\n"," # Check if training has already completed\n"," if start_epoch >= num_epochs:\n"," print(f\"Training already completed. Checkpoint found at epoch {start_epoch}, batch {start_batch}.\")\n"," return history, start_batch\n","\n"," # Early stopping variables\n"," best_val_loss = float('inf')\n"," patience = 5 # Number of epochs to wait for improvement\n"," patience_counter = 0\n"," early_stop = False\n","\n"," # Training loop\n"," for epoch in range(start_epoch, num_epochs):\n"," print(f\"\\nEpoch {epoch + 1}/{num_epochs}\")\n"," print(f\"Starting from batch: {start_batch if epoch == start_epoch else 0}\")\n"," print(f\"Current learning rate: {optimizer.param_groups[0]['lr']:.2e}\")\n"," print(f\"Current scheduler learning rate: {scheduler.get_last_lr()[0]:.2e}\")\n","\n"," model.train()\n","\n"," # Reset epoch-specific tracking variables\n"," epoch_losses = [] # Track losses only for this epoch\n"," processed_batches = 0 # Count of actually processed batches\n","\n"," # Initialize total_train_loss only with losses from the current epoch\n"," total_train_loss = 0.0\n","\n"," # Create data generator for this epoch\n"," train_data_generator = preprocessor.get_data_generator(\n"," train_dataset, LANG_TOKEN_MAPPING, tokenizer, batch_size=batch_size\n"," )\n","\n"," # Calculate total number of batches\n"," total_batches = len(train_dataset) // batch_size\n"," if len(train_dataset) % batch_size != 0:\n"," total_batches += 1\n","\n"," # Training progress bar\n"," pbar = tqdm(train_data_generator, total=total_batches, desc=f\"Epoch {epoch + 1}/{num_epochs}\", ncols=100)\n","\n"," # Keep track of steps for this session\n"," session_steps = 0\n","\n"," # Add gradient accumulation\n"," gradient_accumulation_steps = 4 # Accumulate gradients over 4 batches\n"," optimizer.zero_grad()\n","\n"," for batch_idx, (input_batch, label_batch) in enumerate(pbar):\n"," try:\n"," # Skip batches already processed in the checkpoint\n"," if epoch == start_epoch and batch_idx < start_batch:\n"," continue\n","\n"," # Memory management\n"," torch.cuda.empty_cache()\n","\n"," # Move batches to device\n"," input_batch = input_batch.to(device)\n"," label_batch = label_batch.to(device)\n","\n"," # Forward pass with memory management\n"," try:\n"," outputs = model(input_ids=input_batch, labels=label_batch)\n"," loss = outputs.loss / gradient_accumulation_steps # Scale loss\n"," except RuntimeError as e:\n"," if 'CUDA out of memory' in str(e):\n"," print(\"Out of memory error. Clearing memory and retrying.\")\n"," torch.cuda.empty_cache()\n"," gc.collect()\n"," continue\n"," else:\n"," raise\n","\n"," # Backward pass\n"," loss.backward()\n","\n"," # Step the optimizer every gradient_accumulation_steps batches\n"," if (batch_idx + 1) % gradient_accumulation_steps == 0 or (batch_idx + 1) == total_batches:\n"," torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n"," optimizer.step()\n"," scheduler.step()\n"," optimizer.zero_grad()\n"," session_steps += 1\n","\n"," # Update metrics - only track losses for current epoch\n"," # Multiply by gradient_accumulation_steps to get the actual loss value\n"," current_loss = loss.item() * gradient_accumulation_steps\n"," epoch_losses.append(current_loss)\n"," total_train_loss += current_loss\n"," processed_batches += 1\n","\n"," # Calculate steps taken more accurately\n"," steps_from_previous_epochs = start_epoch * n_batches\n"," steps_from_current_epoch = session_steps\n"," if epoch == start_epoch:\n"," steps_from_current_epoch += start_batch\n"," total_steps_taken = steps_from_previous_epochs + steps_from_current_epoch\n","\n"," # Update progress bar with current epoch's metrics only\n"," current_avg_loss = total_train_loss / processed_batches if processed_batches > 0 else 0\n"," pbar.set_postfix({\n"," \"loss\": current_loss,\n"," \"avg_loss\": current_avg_loss,\n"," \"lr\": scheduler.get_last_lr()[0],\n"," \"steps\": total_steps_taken\n"," })\n","\n"," except Exception as e:\n"," print(f\"\\nError processing training batch {batch_idx}: {str(e)}\")\n","\n"," # Save checkpoint before exiting in case of error\n"," steps_from_previous_epochs = start_epoch * n_batches\n"," steps_from_current_epoch = session_steps\n"," if epoch == start_epoch:\n"," steps_from_current_epoch += start_batch\n"," total_steps_taken = steps_from_previous_epochs + steps_from_current_epoch\n","\n"," # Combine previous checkpoint losses with current epoch losses for saving\n"," all_losses = checkpoint_losses + epoch_losses if epoch == start_epoch else epoch_losses\n","\n"," checkpoint_manager.save_checkpoint(\n"," epoch=epoch,\n"," batch_idx=batch_idx,\n"," losses=all_losses,\n"," total_steps=total_steps,\n"," steps_taken=total_steps_taken\n"," )\n"," print(f\"Checkpoint saved after error at epoch {epoch + 1}, batch {batch_idx}\")\n"," continue\n","\n"," pbar.close()\n","\n"," # Calculate average training loss for this epoch only\n"," avg_train_loss = total_train_loss / processed_batches if processed_batches > 0 else 0\n"," history['train_loss'].append(avg_train_loss)\n"," print(f\"Average Training Loss: {avg_train_loss:.4f}\")\n","\n"," # Validation loop\n"," model.eval()\n"," total_val_loss = 0\n"," processed_val_batches = 0 # Count actual processed validation batches\n"," predictions = []\n"," references = []\n","\n"," # Create validation data generator\n"," val_data_generator = preprocessor.get_data_generator(\n"," val_dataset, LANG_TOKEN_MAPPING, tokenizer, batch_size=batch_size\n"," )\n","\n"," # Calculate total number of validation batches\n"," total_val_batches = len(val_dataset) // batch_size\n"," if len(val_dataset) % batch_size != 0:\n"," total_val_batches += 1\n","\n"," # Validation progress bar\n"," pbar = tqdm(val_data_generator, total=total_val_batches, desc=\"Validating\", ncols=100)\n","\n"," with torch.no_grad():\n"," for batch_idx, (input_batch, label_batch) in enumerate(pbar):\n"," try:\n"," # Move batches to device\n"," input_batch = input_batch.to(device)\n"," label_batch = label_batch.to(device)\n","\n"," # Calculate validation loss\n"," model_outputs = model(input_ids=input_batch, labels=label_batch)\n"," batch_val_loss = model_outputs.loss.item()\n"," total_val_loss += batch_val_loss\n"," processed_val_batches += 1 # Count only successfully processed batches\n","\n"," # Generate translations\n"," outputs = model.generate(input_batch, max_length=max_length)\n","\n"," # Decode predictions and references\n"," pred_texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n"," ref_texts = tokenizer.batch_decode(label_batch, skip_special_tokens=True)\n","\n"," # Print some sample translations for debugging (first 2 in the batch)\n"," if batch_idx == 0:\n"," print(\"\\nSample translations:\")\n"," for i in range(min(2, len(pred_texts))):\n"," print(f\"Reference: {ref_texts[i]}\")\n"," print(f\"Prediction: {pred_texts[i]}\")\n"," print(\"---\")\n","\n"," # Add to predictions and references\n"," predictions.extend(pred_texts)\n"," references.extend(ref_texts)\n","\n"," # Update progress bar with current batch loss and running average\n"," current_avg_val_loss = total_val_loss / processed_val_batches\n"," pbar.set_postfix({\n"," \"batch_loss\": batch_val_loss,\n"," \"avg_val_loss\": current_avg_val_loss\n"," })\n","\n"," except Exception as e:\n"," print(f\"Error processing validation batch {batch_idx}: {str(e)}\")\n"," continue\n","\n"," pbar.close()\n","\n"," # Calculate average validation loss based on actually processed batches\n"," avg_val_loss = total_val_loss / processed_val_batches if processed_val_batches > 0 else float('inf')\n"," history['val_loss'].append(avg_val_loss)\n","\n"," # Calculate BLEU score with the fixed implementation\n"," bleu_score = calculate_bleu(predictions, references)\n"," history['val_bleu'].append(bleu_score)\n"," print(f\"Validation Loss: {avg_val_loss:.4f}\")\n"," print(f\"Validation BLEU: {bleu_score:.4f}\")\n","\n"," # Update the combined list of losses for checkpoint saving\n"," if epoch == start_epoch:\n"," all_losses = checkpoint_losses + epoch_losses\n"," else:\n"," all_losses = epoch_losses if epoch == 0 else history.get('checkpoint_losses', []) + epoch_losses\n","\n"," # Store losses for future epochs\n"," history['checkpoint_losses'] = all_losses\n","\n"," # Save checkpoint at the end of the epoch - only every 10 epochs or final epoch\n"," if (epoch + 1) % 10 == 0 or epoch == num_epochs - 1: # Save every 10 epochs or at the final epoch\n"," total_steps_taken = (epoch + 1) * n_batches # End of epoch\n"," checkpoint_manager.save_checkpoint(\n"," epoch=epoch + 1, # Save as the start of the next epoch\n"," batch_idx=0, # Start from batch 0 of next epoch\n"," losses=all_losses,\n"," total_steps=total_steps,\n"," steps_taken=total_steps_taken\n"," )\n"," print(f\"Checkpoint saved at epoch {epoch + 1}\")\n"," else:\n"," print(f\"Skipping checkpoint save at epoch {epoch + 1} (saving every 10 epochs)\")\n","\n"," # Print epoch summary\n"," print(f\"\\nEpoch {epoch + 1} Summary:\")\n"," print(f\"Training Loss: {avg_train_loss:.4f}\")\n"," print(f\"Validation Loss: {avg_val_loss:.4f}\")\n"," print(f\"BLEU Score: {bleu_score:.4f}\")\n"," print(f\"Final Learning Rate: {optimizer.param_groups[0]['lr']:.2e}\")\n","\n"," # Early stopping check - always save the best model regardless of epoch\n"," if avg_val_loss < best_val_loss:\n"," best_val_loss = avg_val_loss\n"," patience_counter = 0\n"," # Save best model\n"," checkpoint_manager.save_checkpoint(\n"," epoch=epoch + 1,\n"," batch_idx=0,\n"," losses=all_losses,\n"," total_steps=total_steps,\n"," steps_taken=total_steps_taken,\n"," is_best=True # Important to mark this as the best checkpoint\n"," )\n"," print(\"New best model saved!\")\n"," else:\n"," patience_counter += 1\n"," print(f\"Validation loss did not improve. Patience: {patience_counter}/{patience}\")\n","\n"," if patience_counter >= patience:\n"," print(f\"Early stopping triggered after {epoch + 1} epochs\")\n"," early_stop = True\n"," break\n","\n"," if early_stop:\n"," break\n","\n"," # Plot training history\n"," plot_training_history(history)\n","\n"," return history"]},{"cell_type":"markdown","metadata":{"id":"IhGr97x5Jwul"},"source":["# Run training loop"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":877,"referenced_widgets":["f52bf6543a1b4210b0088ae01092e552","fadfa2c246194682a9fa2cda0be5fcbf","70e803c534ca4a598f758f71a6a5cf9e","54d8e95ffac44fab8ee3678c99dd4c9d","e597a3e957764e79a5c2fd3838055f20","0c00a41d78dd49c098f07f431e56e000","daad0d84fca34e7a9c8959d901768f3a","f1ed39710e5945d69e4c8aaab1227cd1","53f190cbb797479580f10e7126cd2d98","a04368b095fc4b1aa7f85609f7ea3ae8","6398a0f7cb7e45cbb53ecdb1f3f8a210","3e2bf1c957f8490d8af594b5393dd9e9","ff5d82b83f094613a97bdce16767dcf3","7b599508229746508252fb8b4fa7d08c","756d7d37885f4be1bfa8432e13f4be68","38b3f637913d42c4a898865d3cc65241","99af1f7bdd3243fc9b6dd72991f9848b","c04936fb51e3463d9ecbf82eaf75bd0b","330a09588a1741a08cd9bb8b91a72cdd","81d6575ab426481688e0a098e33654b6","7fafa218796c4e5ea1346636191e0f34","de4652b0d3824e31b3a1fac8c4439c32","4085c92f282b4e03a6ea563fd5785879","404a4531344c420d9e3df64156604494","497c3aab22294a8287be7681beed4af5","102299141a114074b69741d15d986f5b","fdf9c5d2e354459295cab200cbb57f10","cee0f02f89d44f48a84951610e8186ae","8047b9f0c3664fb4a9949e46d294226e","d537deb9041947249e47d70937a553cd","c6339c4b26a94af79a5c4816009b1b91","9caad337f5d542acad5d2d2b72a81e43","564c5aa95d6241e39aff6739c85c4291","635acd876dab46b2b944c5bae4dd4e09","730d7f35ad584ddfa38e0acb2a1ed25f","20bd0956455a4228a05e8ae882b06805","7c867b80e0ab41c380c495130733af9a","2f2b51acca1149318440342568642cd2","42781d18428d4782ae567cccc0435b87","1107dd5a4f674d7d91db9595be370343","3cd4fdc7c326462cbaa417167a5ccee2","44eb9eaf05144ff58d24101cf2e0ea2f","fb96e0c0252c4b78b9cc56f1f370fd7c","64f1d24c16e04b5d9134fc62b3dda477"]},"collapsed":true,"id":"PYeWtSRtJ0_T","outputId":"a4919a2f-df56-4ce2-bd10-4a9bd28b0ce3"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of warmup steps: 6314\n","Total training steps: 210470\n","Batches per epoch: 21047\n","CheckpointManager: Batches per epoch set to 21047\n","Regular checkpoint not found. Attempting to load best checkpoint.\n"]},{"output_type":"display_data","data":{"text/plain":["best_checkpoint.pth: 0%| | 0.00/895M [00:00',\n"," 'nys': ''\n","}\n","\n","# Function to load model and tokenizer\n","def load_model_and_tokenizer(model_name):\n"," model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n"," tokenizer = AutoTokenizer.from_pretrained(model_name)\n"," model.to(device)\n"," return model, tokenizer\n","\n","# Load English to Giriama model\n","eng_gir_model, eng_gir_tokenizer = load_model_and_tokenizer(\"Lingua-Connect/ENG_GIR\")\n","print(\"English to Giriama model and tokenizer loaded.\")\n","\n","# Load Giriama to English model\n","gir_eng_model, gir_eng_tokenizer = load_model_and_tokenizer(\"Lingua-Connect/GIR_ENG\")\n","print(\"Giriama to English model and tokenizer loaded.\")"]},{"cell_type":"markdown","metadata":{"id":"0YE9Rxr-k-4_"},"source":["# Perform inference"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iJWGL949lASo"},"outputs":[],"source":["def translate(text, model, tokenizer, source_lang, target_lang, max_length=128, num_beams=5, num_return_sequences=1):\n"," # Add language token to the input text\n"," input_text = f\"{lang_token[source_lang]} {text}\"\n","\n"," # Tokenize the input text\n"," input_ids = tokenizer.encode(input_text, return_tensors=\"pt\", max_length=max_length, truncation=True)\n"," input_ids = input_ids.to(device)\n","\n"," # Generate translation\n"," outputs = model.generate(\n"," input_ids,\n"," max_length=max_length,\n"," num_beams=num_beams,\n"," num_return_sequences=num_return_sequences,\n"," early_stopping=True\n"," )\n","\n"," # Decode the generated tokens to text\n"," translated_texts = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]\n","\n"," return translated_texts"]},{"cell_type":"code","source":["# Example usage - English to Giriama\n","eng_text = \"Where are you?\"\n","gir_translations = translate(\n"," eng_text,\n"," eng_gir_model,\n"," eng_gir_tokenizer,\n"," source_lang=\"en\",\n"," target_lang=\"nys\",\n"," num_beams=5,\n"," num_return_sequences=3\n",")\n","\n","print(f\"\\nEnglish to Giriama:\")\n","print(f\"Input: {eng_text}\")\n","for i, translation in enumerate(gir_translations):\n"," print(f\"Translation {i+1}: {translation}\")"],"metadata":{"id":"W2Gwuu9WecDO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Example usage - Giriama to English\n","gir_text = gir_translations[0] # Use the first translation as input\n","eng_back_translations = translate(\n"," gir_text,\n"," gir_eng_model,\n"," gir_eng_tokenizer,\n"," source_lang=\"nys\",\n"," target_lang=\"en\",\n"," num_beams=5,\n"," num_return_sequences=3\n",")\n","\n","print(f\"\\nGiriama to English:\")\n","print(f\"Input: {gir_text}\")\n","for i, translation in enumerate(eng_back_translations):\n"," print(f\"Translation {i+1}: {translation}\")"],"metadata":{"id":"w7SeA-8decx7"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fwspZl7Nl8qG"},"source":["# Validation Inference\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_79JXTMgl-Rh"},"outputs":[],"source":["# If you have a validation dataset\n","if 'validation_dataset' in locals():\n"," print(\"\\n===== VALIDATION DATASET TESTS =====\")\n"," sample = validation_dataset[76] # Change the index to test different samples\n"," input_text = sample['English sentence']\n"," target_text = sample['Swahili Translation'] # This should be 'Giriama Translation'\n","\n"," print(f\"Dataset sample:\")\n"," print(f\"English: {input_text}\")\n"," print(f\"Reference Giriama: {target_text}\")\n","\n"," # Translate English to Giriama\n"," gir_translations = translate(\n"," input_text,\n"," eng_gir_model,\n"," eng_gir_tokenizer,\n"," source_lang=\"en\",\n"," target_lang=\"gir\",\n"," num_beams=5,\n"," num_return_sequences=3\n"," )\n","\n"," print(\"\\nGenerated translations:\")\n"," for i, translation in enumerate(gir_translations):\n"," print(f\"Translation {i+1}: {translation}\")"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["jmiqP3XaJR2Z"],"gpuType":"T4","provenance":[],"authorship_tag":"ABX9TyM4u7fODAs/hBs2W6DIH49r"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"widgets":{"application/vnd.jupyter.widget-state+json":{"0873702b270f44ac8d403d0f99071b85":{"model_module":"@jupyter-widgets/controls","model_name":"VBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"VBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"VBoxView","box_style":"","children":[],"layout":"IPY_MODEL_c739646664eb4ebb9fa75ec379f90742"}},"0b6b39f74eaf4f88b7d3f61cabacb0e2":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_58a9c1e2ed5a4d8d898151cd316d9b38","placeholder":"​","style":"IPY_MODEL_ea26eccbc6b54b40bc6d5acd0df31ffd","value":"

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