{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "nydD1iOK5UT8", "outputId": "bf036347-0c4e-4276-d46a-38d7a7a7078b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n", "Collecting datasets\n", " Downloading datasets-4.4.0-py3-none-any.whl.metadata (19 kB)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from datasets) (3.20.0)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from datasets) (2.0.2)\n", "Collecting pyarrow>=21.0.0 (from datasets)\n", " Downloading pyarrow-22.0.0-cp312-cp312-manylinux_2_28_x86_64.whl.metadata (3.2 kB)\n", "Requirement already satisfied: dill<0.4.1,>=0.3.0 in /usr/local/lib/python3.12/dist-packages (from datasets) (0.3.8)\n", "Requirement already satisfied: pandas 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/usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (0.4.1)\n", "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (1.22.0)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n", "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.12/dist-packages (from anyio->httpx<1.0.0->datasets) (1.3.1)\n", "Downloading datasets-4.4.0-py3-none-any.whl (511 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m511.5/511.5 kB\u001b[0m \u001b[31m38.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading pyarrow-22.0.0-cp312-cp312-manylinux_2_28_x86_64.whl (47.7 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.7/47.7 MB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: pyarrow, datasets\n", " Attempting uninstall: pyarrow\n", " Found existing installation: pyarrow 18.1.0\n", " Uninstalling pyarrow-18.1.0:\n", " Successfully uninstalled pyarrow-18.1.0\n", " Attempting uninstall: datasets\n", " Found existing installation: datasets 4.0.0\n", " Uninstalling datasets-4.0.0:\n", " Successfully uninstalled datasets-4.0.0\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", "cudf-cu12 25.6.0 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 22.0.0 which is incompatible.\n", "pylibcudf-cu12 25.6.0 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 22.0.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed datasets-4.4.0 pyarrow-22.0.0\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "pyarrow" ] }, "id": "7b3eff8a80604dae9544d95d47c89039" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (4.57.1)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers) (3.20.0)\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.34.0 in 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(4.12.0)\n" ] } ], "source": [ "!pip install datasets --upgrade\n", "!pip install transformers\n", "!pip install sentencepiece\n", "!pip install transformers[torch]\n", "!pip install sacrebleu\n", "!pip install evaluate\n", "!pip install accelerate -U\n", "!pip install gradio\n", "!pip install kaleido cohere openai tiktoken\n", "!pip install typing-extensions==4.12.0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7V9Nq_Yd5V2x", "colab": { "base_uri": "https://localhost:8080/", "height": 316 }, "outputId": "da4c67d4-9e99-498a-fa7f-5e59753c8b9a" }, "outputs": [ { "output_type": "error", "ename": "AttributeError", "evalue": "partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import)", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipython-input-2682221660.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cfilt/iitb-english-hindi\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/datasets/__init__.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfingerprint\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdisable_caching\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menable_caching\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_caching_enabled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDatasetInfo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m from .inspect import (\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0mget_dataset_config_info\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mget_dataset_config_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/datasets/inspect.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstreaming_download_manager\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStreamingDownloadManager\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDatasetInfo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m from .load import (\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0mdataset_module_factory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mget_dataset_builder_class\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/datasets/load.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0miterable_dataset\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mIterableDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mnaming\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcamelcase_to_snakecase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnakecase_to_camelcase\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m from .packaged_modules import (\n\u001b[0m\u001b[1;32m 68\u001b[0m \u001b[0m_EXTENSION_TO_MODULE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0m_MODULE_TO_EXTENSIONS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/datasets/packaged_modules/__init__.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mhuggingface_hub\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0minsecure_hashlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0marrow\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0marrow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0maudiofolder\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maudiofolder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/datasets/packaged_modules/arrow/arrow.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mlogger\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_logger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import)" ] } ], "source": [ "from datasets import load_dataset\n", "dataset = load_dataset(\"cfilt/iitb-english-hindi\")" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 402, "referenced_widgets": [ "5967abf8d13646869e7cfc6f93a75ce2", "a1c19669dba04843a8c03ae046d10335", "6c934c122e624f3dbbb791d6fcaa4b1f", "4514763ec8344fd780b5fd74075f2ed5", "57c57e5737e9459bb72a4d8e9e389602", "7d3a1d8f064f49d0bd85e5abc176fd06", "8a3eecedbeb046189594328fcc2e923a", "56f3786206be4e5c8e0cccc1ac7b7854", "02eee28aa58a448ebc6591547fa6186d", "2e53c90df17346fca210556d9767bf30", "a4677c3eada746d1a74720fd36b670e5", "5c950320e91e42d69c6f6220664a0104", "c524c2d1df62430ca3ba97ff96eaab44", "dd4f2aa8db6d42c888fd278b9b5f1145", "8c050d3a87c44cc3a4bda9c16d0f6d13", "7ef4194788304cacb562b0858d2ce19e", "c2c16583b2c24261811ca700f6449c0a", "f0c0e2af7d964096aa20cef90b44d94b", "985e4d0481bd40be809538050450e01a", "56af4c9dc85b433b906a71580e90310c", "f8fa3c8e4a80442ba1274e76f5d1d8f1", "3bd6ccab56ed40788fe50c52f996b1d4", "07875bfc465644d988d31f79c0a84f30", "4acd9c2ac94d4c59b370a4f76d4887ff", "84be661e91d845b59f869e18fd019660", "e66f92fc829f4f01808a2ed8667ce60b", "17390a30c67a4713b86f7c2afe97897e", "d8d4583e746444ac90ee5502c2058e8e", "f810d6004c8f46f68524c9d45a2bc61f", "8e8331258aac4cb895fe8daa9631401a", "4b80f54f54c24f29839891beaced62be", "d1e0ba119cbb4809a24104150e12836a", "34953dbe17874f83a859bc150c0e9ef8", "021d35d9d0c542f4b48654c9f42fdd10", "4540e3e2b17a4581b6af5c659e6a09db", "eed5244ae2d446f0b2b7a64733f53c0f", "4e64d71b2ddd4419bae7b819247ba6e8", "73a048a7fd4b4a25936811d4cd425319", "b8cab1977aaa441ab8abf8cdc9fd4ffb", "721f2c624003468194e5a0eb9e1f3e4d", "055b4de2fa184a0da149ff602f840bbf", "feff66ced9944175ab700b077879e68f", "f44201ed83934cb0ac7722e891508380", "0128bfa7ccb0489e8e647997bc1fc4ab", "ac2c489973b143a2b7216802a439c9d8", "b2e621968a7b480db125d718e6b2bc36", "c36e16adcba94bf590d3c98e5b11f66b", "393f2ebfb60549a98a8aea6e51a8db31", "492e85d80d2d4f7aa12c0158c44b8cf8", "da468cd0852e4078843e477ab6e564ba", "f3db7c5259f3487caa7edf0e58da2b49", "26d2226540144734b1523551716dc58d", "1e915126f755469aa42237e75660c4ca", "f3d3e1604c5a4f079265cfc235128d5a", "a1bd9307dcac4538a4424af984860fd3", "6ce1290e5a204dcd9486824eb41986de", "9159c9ce010e4587b962e1e4b7e8c151", "0481d3c70c1140f09c43b5bce76211b8", "bba3f27b76e34818b8fa226b25449d86", "aea250cc9b6e4af9af9191683842fa19", "38422b786c674b7d8911dde8bb4a1508", "db4ee36b230a47bdbb5a481dfd7d1a02", "0a4e1570bb424889a51a84d76bfc6732", "73fe182b213f451185b3757dc9129fcd", "e5c17bfff2f744c59e2da82733ba1ed1", "e119dedce16a4fb1827bb8f5cc514a1a", "798a33a076ed4ad79aee2dea78f2f5df", "5c4fc833d6ab4f8c9eebae7433bf4311", "12ed8f870f764612a2b1a186d80787e8", "fdd8e71813024227ba9dd4d9cfc1e663", "957234a316994a71a5ab7496692ab454", "0e88d96f438a4296a60c3e8fe227aa8f", "132b5fa9c198433e93363573e7e7632b", "31aa6d7c7b824e6e989d6d68c0707650", "a642d46f9af147eabcbcceed760e202d", "1801ab0a47ac493bb68ce4ef7cb87a83", "551ce33819cd49d4bf256fd489f9cbb3", "716133d3eb6e45ed919c54ac30d00ac0", "066392ea57214d2a8248712380ee39bd", "6a78c075d32d4249b6d1e9732d588757", "fc4b30f874e84f00895523664263c6cc", "4378c9416423495dbe3b10bc06ff7984", "a0f5b7c845ab426da424313e35dc1cda", "9467c49b9de747d2a24620d9db6d0142", "33fb96d40cd642809fdd3d6113bccb38", "8dbe61b01a684dda864ddc959446f066", "0289c3de209f490ea4b631d425405aaa", "4fdcf42a7edf42ab8d2c3b23f10fbbee" ] }, "id": "0522c757", "outputId": "9b8d58bb-9b1f-48b3-ed48-8e56c7dd0f55" }, "source": [ "from datasets import load_dataset\n", "dataset = load_dataset(\"cfilt/iitb-english-hindi\")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "5967abf8d13646869e7cfc6f93a75ce2" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "dataset_infos.json: 0%| | 0.00/953 [00:00" ], "text/html": [ "\n", "
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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=5000, training_loss=0.3303934112548828, metrics={'train_runtime': 2100.9016, 'train_samples_per_second': 76.158, 'train_steps_per_second': 2.38, 'total_flos': 2771972849074176.0, 'train_loss': 0.3303934112548828, 'epoch': 63.29113924050633})" ] }, "metadata": {}, "execution_count": 49 } ], "source": [ "from transformers import Seq2SeqTrainer\n", "\n", "trainer = Seq2SeqTrainer(\n", " model,\n", " training_args,\n", " train_dataset=tokenized_datasets_test,\n", " eval_dataset=tokenized_datasets_validation,\n", " data_collator=data_collator,\n", " tokenizer=tokenizer,\n", " compute_metrics=compute_metrics,\n", ")\n", "\n", "trainer.train()\n" ] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "91ci0857cBk5", "outputId": "64b38e51-d515-4b45-bc15-e8f6fb9d6144" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "from transformers import MarianMTModel, MarianTokenizer\n", "\n", "# Save model and tokenizer\n", "model.save_pretrained(\"saved_model_nlp\")\n", "tokenizer.save_pretrained(\"saved_model_nlp\")\n", "\n", "print(\"✅ Model and tokenizer saved successfully.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cawWO9z-BD3i", "outputId": "5a605679-f381-4fd2-9b2b-c51b74613955" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Model and tokenizer saved successfully.\n" ] } ] }, { "cell_type": "code", "source": [ "from transformers import MarianMTModel, MarianTokenizer\n", "\n", "# Load model and tokenizer\n", "model = MarianMTModel.from_pretrained(\"saved_model\")\n", "tokenizer = MarianTokenizer.from_pretrained(\"saved_model\")\n", "\n", "print(\"✅ Model and tokenizer loaded successfully.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YpebpTOjD_WU", "outputId": "4f4d7589-c2a9-4b7f-ad19-1cd9861d2a03" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Model and tokenizer loaded successfully.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n", " warnings.warn(\"Recommended: pip install sacremoses.\")\n" ] } ] }, { "cell_type": "code", "source": [ "import torch\n", "import gradio as gr\n", "from transformers import MarianMTModel, MarianTokenizer\n", "\n", "# Load model and tokenizer\n", "model = MarianMTModel.from_pretrained(\"saved_model_nlp\")\n", "tokenizer = MarianTokenizer.from_pretrained(\"saved_model_nlp\")\n", "\n", "def translate(text):\n", " inputs = tokenizer(text, return_tensors=\"pt\", padding=True, truncation=True)\n", " with torch.no_grad():\n", " translated = model.generate(**inputs)\n", " return tokenizer.decode(translated[0], skip_special_tokens=True)\n", "\n", "iface = gr.Interface(\n", " fn=translate,\n", " inputs=gr.Textbox(label=\"Enter source text\"),\n", " outputs=gr.Textbox(label=\"Translated text\"),\n", " title=\"Machine Translation (MarianMT)\",\n", " description=\"Translate text using a fine-tuned MarianMT model.\"\n", ")\n", "\n", "iface.launch()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 687 }, "id": "dp3arn8vD_Ql", "outputId": "f01d11c4-c85c-47c2-b8ad-5c3115ad4ede" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n", " warnings.warn(\"Recommended: pip install sacremoses.\")\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "It looks like you are running Gradio on a hosted Jupyter notebook, which requires `share=True`. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n", "\n", "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n", "* Running on public URL: https://19548d0f3eb82b95f4.gradio.live\n", "\n", "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [] }, "metadata": {}, "execution_count": 51 } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "daBmjoRDD_Lk" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "hJcgMbyVD_Dc" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Recreate the same model architecture\n", "model = Transformer(\n", " num_layers=num_layers,\n", " d_model=d_model,\n", " num_heads=num_heads,\n", " dff=dff,\n", " input_vocab_size=tokenizer_pt.vocab_size + 2,\n", " target_vocab_size=tokenizer_en.vocab_size + 2,\n", " pe_input=1000,\n", " pe_target=1000,\n", ")\n", "\n", "# Load saved weights\n", "model.load_weights(\"transformer_weights.h5\")\n", "print(\"✅ Weights loaded successfully.\")\n" ], "metadata": { "id": "XSXoZeseBEWC" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import gradio as gr\n", "\n", "def translate_sentence(sentence):\n", " translated = evaluate(sentence) # use your existing inference function\n", " return translated\n", "\n", "iface = gr.Interface(\n", " fn=translate_sentence,\n", " inputs=gr.Textbox(label=\"Enter Portuguese Sentence\"),\n", " outputs=gr.Textbox(label=\"English Translation\"),\n", " title=\"Transformer Machine Translation\",\n", " description=\"Translate Portuguese → English using a trained Transformer model\"\n", ")\n", "\n", "iface.launch()\n" ], "metadata": { "id": "aXmxgZznBORI" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import gradio as gr\n", "\n", "\n", "def translate(text):\n", " inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n", " translated_tokens = model.generate(**inputs, max_length=256)\n", " results = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]\n", " return results\n", "\n", "interface = gr.Interface(fn=translate,inputs=gr.Textbox(lines=2, 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