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xformers, torchvision, cut_cross_entropy, bitsandbytes, trl, unsloth_zoo, unsloth\n", " Attempting uninstall: triton\n", " Found existing installation: triton 3.1.0\n", " Uninstalling triton-3.1.0:\n", " Successfully uninstalled triton-3.1.0\n", " Attempting uninstall: protobuf\n", " Found existing installation: protobuf 4.25.6\n", " Uninstalling protobuf-4.25.6:\n", " Successfully uninstalled protobuf-4.25.6\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", " Attempting uninstall: torch\n", " Found existing installation: torch 2.5.1+cu124\n", " Uninstalling torch-2.5.1+cu124:\n", " Successfully uninstalled torch-2.5.1+cu124\n", " Attempting uninstall: torchvision\n", " Found existing installation: torchvision 0.20.1+cu124\n", " Uninstalling torchvision-0.20.1+cu124:\n", " Successfully uninstalled torchvision-0.20.1+cu124\n", "\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", "tensorflow-metadata 1.16.1 requires protobuf<6.0.0dev,>=4.25.2; python_version >= \"3.11\", but you have protobuf 3.20.3 which is incompatible.\n", "grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.20.3 which is incompatible.\n", "torchaudio 2.5.1+cu124 requires torch==2.5.1, but you have torch 2.6.0 which is incompatible.\n", "fastai 2.7.18 requires torch<2.6,>=1.10, but you have torch 2.6.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed bitsandbytes-0.45.2 cut_cross_entropy-25.1.1 datasets-3.3.1 dill-0.3.8 hf_transfer-0.1.9 multiprocess-0.70.16 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-cusparselt-cu12-0.6.2 nvidia-nvjitlink-cu12-12.4.127 protobuf-3.20.3 shtab-1.7.1 torch-2.6.0 torchvision-0.21.0 triton-3.2.0 trl-0.15.1 tyro-0.9.16 unsloth-2025.2.14 unsloth_zoo-2025.2.6 xformers-0.0.29.post3 xxhash-3.5.0\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "google" ] }, "id": "fca47623494a4ca4a3d560ee8eb6d844" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "from unsloth import FastLanguageModel\n", "import torch\n", "from transformers import AutoModelForSequenceClassification, AutoTokenizer, TrainingArguments, Trainer\n", "from datasets import Dataset, DatasetDict\n", "import pandas as pd\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score, precision_recall_fscore_support" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QveA5IgvGyjb", "outputId": "f42e2d24-1802-4950-b9be-6bd9c5038884" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "🦥 Unsloth Zoo will now patch everything to make training faster!\n" ] } ] }, { "cell_type": "code", "source": [ "df = pd.read_csv(\"Telugu.csv\") # Replace \"your_dataset.csv\" with your filename\n", "df = df.dropna() # remove null values\n", "df = df.rename(columns={\"text_column\": \"text\", \"label_column\": \"label\"}) # rename colums\n", "print(df.head()) # Inspect the first few rows" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "CoSe53I0HHd4", "outputId": "e4239d43-0edf-42f6-a5d0-7b4e5bb4818e" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " en te\n", "0 Wonderful!. అద్భుతం!.\n", "1 Definitely!. తప్పకుండా.\n", "2 He hung up. అతను పెట్టేసాడు\n", "3 I ran home. నేను ఇంటికి పరిగెత్తాను\n", "4 Who are we?. మేము ఎవరము ?.\n" ] } ] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "\n", "try:\n", " drive.flush_and_unmount()\n", " print('Drive unmounted')\n", "except ValueError:\n", " pass\n", "\n", "# Remount the drive\n", "drive.mount('/content/drive')\n", "df = pd.read_csv(\"/content/Telugu.csv\") # replace with your path in Google Drive\n", "df = df.dropna() # remove null values\n", "df = df.rename(columns={\"text_column\": \"text\", \"label_column\": \"label\"}) # rename colums\n", "print(df.head())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gitAjMl9PvIu", "outputId": "808aabdf-853b-4bb6-b6eb-bd604f4b1dfc" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Drive unmounted\n", "Mounted at /content/drive\n", " en te\n", "0 Wonderful!. అద్భుతం!.\n", "1 Definitely!. తప్పకుండా.\n", "2 He hung up. అతను పెట్టేసాడు\n", "3 I ran home. నేను ఇంటికి పరిగెత్తాను\n", "4 Who are we?. మేము ఎవరము ?.\n" ] } ] }, { "cell_type": "code", "source": [ "dataset = Dataset.from_pandas(df)" ], "metadata": { "id": "w2GR-QAOKT4K" }, "execution_count": 41, "outputs": [] }, { "cell_type": "code", "source": [ "dataset = dataset.train_test_split(test_size=0.2, seed=42) # 80% train, 20% validation. seed for reproducibility" ], "metadata": { "id": "jPTEPFfVKZeh" }, "execution_count": 42, "outputs": [] }, { "cell_type": "code", "source": [ "model_name = \"bert-base-multilingual-cased\" # Or try \"xlm-roberta-base\" if that's faster\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)" ], "metadata": { "id": "8iRRkH0OKp_G" }, "execution_count": 45, "outputs": [] }, { "cell_type": "code", "source": [ "def tokenized_function(examples):\n", " tokenized_datasets = dataset.map(preprocess_function, batched=True)\n", " return tokenized_datasets\n", "\n", "def tokenize_fn(examples):\n", " inputs = [ex for ex in examples['te']]\n", " targets = [ex for ex in examples['en']]\n", " model_inputs = tokenizer(inputs, max_length=128, truncation=True, padding=\"max_length\")\n", " labels = tokenizer(targets, max_length=128, truncation=True, padding=\"max_length\").input_ids\n", " model_inputs[\"labels\"] = labels\n", " return model_inputs\n", "\n", "tokenized_dataset = dataset.map(tokenize_fn, batched=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "9f683a1137ca416c8029401d5ae849d7", "d630fe3ecfad408dafa41d789815037e", "40e6c0fe4cb7497fa1838e67548d6005", "0f0c56e6454045989f59191d40dbd3fd", "15009d21d1fe4ff98b315ea818fba5a7", "e4b8252ccaa64792bb8776e78dc88f90", "b667072ecb8c4351b3da6550186677ae", "11837c3f7efc4a9bbf8a9be11f83022f", "4ce912605c7840f78cfd7dea5b45f17e", "ed9ef10a871f4405b035d65180fb028f", "0df0592954a44d339b6398414a78c105", "d6147102590c4f9fb6e141c39d2eced5", "5f12706d0fcc472a81190f56ccc45506", "4388ed0a33694d0bb109f76ebaefb875", "1ec0d86bfd7b4e8d8533109c05429cff", "cc9b55029bd04b52bee8c06341fd2e05", "529ad705e06a4772bfcc35452ab2a937", "a8443544e2244ebdb3d2d851cac43483", "d5a0c82f0dda4e73ab95f071e523982d", "eb280ce21f9b4da2bb4f30a999a95bba", "89471acf295c4c6ca19d212746c734f6", "142451eac18749eb8e727052eaa69de9" ] }, "id": "6CdDktluQlGE", "outputId": "d14e1f17-fe7b-4896-c3a8-b7cd38ec838c" }, "execution_count": 15, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/336536 [00:00=1.17 in /usr/local/lib/python3.11/dist-packages (from peft) (1.26.4)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from peft) (24.2)\n", "Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from peft) (5.9.5)\n", "Requirement already satisfied: pyyaml in /usr/local/lib/python3.11/dist-packages (from peft) (6.0.2)\n", "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.11/dist-packages (from peft) (2.6.0)\n", "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (from peft) 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/usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n", "Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n", "Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (3.2.0)\n", "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (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>=1.13.0->peft) (1.3.0)\n", "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers->peft) (2024.11.6)\n", "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers->peft) (0.21.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=1.13.0->peft) (3.0.2)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (3.4.1)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (3.10)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (2.3.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (2025.1.31)\n" ] } ] }, { "cell_type": "code", "source": [ "from peft import LoraConfig, get_peft_model\n", "\n", "# ... (rest of your code)\n", "\n", "lora_config = LoraConfig(\n", " r=16,\n", " lora_alpha=32,\n", " target_modules=[\"query\", \"key\", \"value\", \"dense\"],\n", " lora_dropout=0.05,\n", " bias=\"none\",\n", " task_type=\"SEQ_CLS\", # Specify task type as sequence classification\n", ")\n", "\n", "model = get_peft_model(model, lora_config) # Use peft.get_peft_model directly" ], "metadata": { "id": "OTHpe1MmU0Cy" }, "execution_count": 25, "outputs": [] }, { "cell_type": "code", "source": [ "training_args = TrainingArguments(\n", " output_dir=\"./results\",\n", " learning_rate=2e-5,\n", " per_device_train_batch_size=32,\n", " per_device_eval_batch_size=32,\n", " num_train_epochs=3,\n", " weight_decay=0.01,\n", " evaluation_strategy=\"epoch\",\n", " save_strategy=\"epoch\",\n", " load_best_model_at_end=True,\n", " metric_for_best_model=\"f1\", # Use F1 score to determine the best model\n", " report_to=\"none\" # Disable WANDB to avoid login issues\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hDc8Wg-eU7ja", "outputId": "93a2c34d-98fd-43d4-c549-2fa756d59066" }, "execution_count": 26, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.11/dist-packages/transformers/training_args.py:1575: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n" ] } ] }, { "cell_type": "code", "source": [ "tokenized_datasets = dataset.map(tokenize_fn, batched=True)\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=tokenized_datasets[\"train\"],\n", " eval_dataset=tokenized_datasets[\"test\"],\n", " tokenizer=tokenizer,\n", " compute_metrics=compute_metrics,\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 136, "referenced_widgets": [ "01dd70a8e7fd4facaac6ad9ffc58da3e", "ec6d21757b634f39972579481748f186", "0ffff7c822734be4bf1ea63c952d1529", "4d0a7af24e094555ac81240fe86880a1", "8c340e0cc3504ea7af682f9d5860a32d", "bd885849068847d8b73dd4088c115352", "bc57a1ac50404369ab5f65223f81fe30", "e214dd4a9e204bbdaa172fec8a3c4006", "5d5f1a6ead7c4e569bf55641a511b7e7", "d585af1c836f4291af178ba09f07d22b", "7275a88bcc704a5090786c0d887aae23", "5ffea52d1c8c4d4795ce0383907bc318", "42bfae1264c5414e8b22bcc2facf955b", "1140dd19db0144b4874a349aedc3b623", "9dc7bb7f1c634e2ea5e447fed948eadc", "ba5322dab9704e989c75aa71155786bd", "b579fa33f23f4a1fa5de52fb419a093d", "fc94936373f74d5e81ca3e4637c6d947", "7dda9808feed46bd9e60cbc0e1468864", "943db725002144aaa8e53b1f74dc3075", "0d802ae9a0fa44bd903381c253394112", "1a64c89ec0724679bc7f7e657c2c1a16" ] }, "id": "Gp5X_dtMVCnZ", "outputId": "9b51cfcd-17cd-4061-f3ce-fe9292df881a" }, "execution_count": 55, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/336536 [00:00:2: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n", " trainer = Trainer(\n" ] } ] }, { "cell_type": "code", "source": [ "trainer.save_model(\"./my_colloquial_telugu_model\")" ], "metadata": { "id": "zQvTbZUqe4Fh" }, "execution_count": 56, "outputs": [] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 66, "referenced_widgets": [ "e24f6e6a5adc425e83ff51b58411afb9", "a9c72f1d56d148bda2077d1cf355e365", "0bcb97eb422647b089f2093bcc89a851", "2f57b7d258524f98b5bc68c9927eac74", "c862d578f0064ff1ad720dfd673d4d91", "4318e63bac7a4e298f3b8159416fbc29", "c12101ddff58427d8c9445fef68549c0", "e648bc7daa5d41bdb9bc34a354cf64e1", "0e65ef70dcf84d20a6fd2299ea1464f4", "4d1610508a8c4ef09bd188a2343927e9", "fa677063d8cd48c681c8d0531b2aa805", "07eccd1ee92e47f6bd3ae6175077410c", "f1cf0f2ffd654b23aa2a610a4782d168", "0c92e1aa5e6b4196ba107ba8a5fffd5d", "09b4e4b15d844eca9d540125c43068e4", "9fec41f1be4342b085055e4c4095fad3", "fb4e2d8316504cae99d094f248b3f150", "f9a528d054a04985bb657f94384e77d1", "64a6775c39f24740adcca9f4310fbdef", "b9d3dce86814485c8f25c9bfb8f4913c", "1fb291379eb844bd8e6efcaedc96d6e1", "9472c0deb60346fb95f81691645ec01c", "dc0e7e619bc244f4be4ce7640d83499b" ] }, "id": "XJlDzA6_e4CK", "outputId": "5724124a-c9f4-4dea-f8ac-b8d33b4582b1" }, "execution_count": 57, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='