{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "v3A6MpyQ2GGU", "outputId": "9179ab55-7a6f-4d65-80dc-6562e7f4048a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: jax in /usr/local/lib/python3.10/dist-packages (0.4.26)\n", "Requirement already satisfied: ml-dtypes>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from jax) (0.2.0)\n", "Requirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.10/dist-packages (from jax) (1.25.2)\n", "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.10/dist-packages (from jax) (3.3.0)\n", "Requirement already satisfied: scipy>=1.9 in /usr/local/lib/python3.10/dist-packages (from jax) (1.13.1)\n" ] } ], "source": [ "pip install jax" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "crwOJFYLfy4G", "outputId": "99ebbad6-8895-4fcd-f5cd-2324e2e80078" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: jax in /usr/local/lib/python3.10/dist-packages (0.4.26)\n", "Collecting jax\n", " Downloading jax-0.4.30-py3-none-any.whl.metadata (22 kB)\n", "Collecting jaxlib<=0.4.30,>=0.4.27 (from jax)\n", " Downloading jaxlib-0.4.30-cp310-cp310-manylinux2014_x86_64.whl.metadata (1.0 kB)\n", "Requirement already satisfied: ml-dtypes>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from jax) (0.2.0)\n", "Requirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.10/dist-packages (from jax) (1.25.2)\n", "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.10/dist-packages (from jax) (3.3.0)\n", "Requirement already satisfied: scipy>=1.9 in /usr/local/lib/python3.10/dist-packages (from jax) (1.13.1)\n", "Downloading jax-0.4.30-py3-none-any.whl (2.0 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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"outputId": "045dca43-49e3-431c-ec0a-c8ef5c074b24" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting datasets\n", " Downloading datasets-2.20.0-py3-none-any.whl.metadata (19 kB)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.4)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.25.2)\n", "Collecting pyarrow>=15.0.0 (from datasets)\n", " Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n", "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\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/python3.10/dist-packages (from datasets) (2.0.3)\n", "Collecting requests>=2.32.2 (from datasets)\n", " Downloading requests-2.32.3-py3-none-any.whl.metadata (4.6 kB)\n", "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.4)\n", "Collecting xxhash (from datasets)\n", " Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n", "Collecting multiprocess (from datasets)\n", " Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n", "Collecting fsspec<=2024.5.0,>=2023.1.0 (from fsspec[http]<=2024.5.0,>=2023.1.0->datasets)\n", " Downloading fsspec-2024.5.0-py3-none-any.whl.metadata (11 kB)\n", "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.5)\n", "Requirement already satisfied: huggingface-hub>=0.21.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.23.5)\n", "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n", "Requirement already satisfied: pyyaml>=5.1 in 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in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n", "Downloading datasets-2.20.0-py3-none-any.whl (547 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m17.5 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[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading fsspec-2024.5.0-py3-none-any.whl (316 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m316.1/316.1 kB\u001b[0m \u001b[31m30.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl (39.9 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m 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installation: requests 2.31.0\n", " Uninstalling requests-2.31.0:\n", " Successfully uninstalled requests-2.31.0\n", " Attempting uninstall: pyarrow\n", " Found existing installation: pyarrow 14.0.2\n", " Uninstalling pyarrow-14.0.2:\n", " Successfully uninstalled pyarrow-14.0.2\n", " Attempting uninstall: fsspec\n", " Found existing installation: fsspec 2024.6.1\n", " Uninstalling fsspec-2024.6.1:\n", " Successfully uninstalled fsspec-2024.6.1\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", "torch 2.3.1+cu121 requires nvidia-cublas-cu12==12.1.3.1; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", "torch 2.3.1+cu121 requires nvidia-cuda-cupti-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", "torch 2.3.1+cu121 requires nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", "torch 2.3.1+cu121 requires nvidia-cuda-runtime-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", "torch 2.3.1+cu121 requires nvidia-cudnn-cu12==8.9.2.26; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", "torch 2.3.1+cu121 requires nvidia-cufft-cu12==11.0.2.54; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", "torch 2.3.1+cu121 requires nvidia-curand-cu12==10.3.2.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", "torch 2.3.1+cu121 requires nvidia-cusolver-cu12==11.4.5.107; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", "torch 2.3.1+cu121 requires nvidia-cusparse-cu12==12.1.0.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", "torch 2.3.1+cu121 requires nvidia-nccl-cu12==2.20.5; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", "torch 2.3.1+cu121 requires nvidia-nvtx-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n", "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n", "gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.5.0 which is incompatible.\n", "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n", "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 fsspec-2024.5.0 multiprocess-0.70.16 pyarrow-17.0.0 requests-2.32.3 xxhash-3.4.1\n" ] } ], "source": [ "!pip3 install datasets" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 469, "referenced_widgets": [ "b73ba84c160b487a88642e37d40b12d3", "84f7bb881e6140b0b24b5713d1e1d480", "4f2269b3b1354d8584fe656f98d609fc", "2e468483a4a84c279b169ea2e47a95f1", "d37f5831e7d34e818b55bc77535b1864", "f84dde5581ba47f2858fb3df098d7e63", "e84cbdf874d8422990dc8b9da2762bd7", "3ca73ffe77e2429bb8e4f82d49cfc39d", "b5e75af11483420a97cb7358e10e0a8a", "82d7036129a149f2aa3fb083012ffd28", "2c05bacfd939427aa6ffc475b000ec3c", "f7ebf09d9d134fe98b5b6cc72f3c4383", "fcd10f203f7b4444a542f4bc46819bb0", "fdee9c4abbb44f8eb6bd2c086cdc35f0", "4537e006398a40bba662bd107738260b", "687f71247fe547f58c15e88dfc877539", "7301f73ac17d48cd89ad34b73a90f8a5", "4f6c2315a70b4233904da1e298d1afc0", "0314d66ef91b4444bb6f6c88518a25c5", "b2971fda27d042d9aa724d6525bf4c9b", "68da8c3879a847b09b0e651884b5c1a0", "da9637b20f514e169df8a0f1f36ddff2", "703cce4bdd344a648b464a46628c6d4c", "f2a510c32bc74b33b198afb768c007aa", "c49769104e7540b0bddc4e032acf5bff", "bcee0902bd1344058c3ef2c084193e93", "913511092fbb4b77af8b62a8a8842286", "f854664e703d4ffcb8e727fe3303a6a5", "9a86368c7193427593523d6eec2d39dd", "89502b3f46d244d6aef1ca88059d9749", "498bb9451fbb4e0999bce7bf46660c6b", "8db61fd6f451465893332522f90f993c", "c3d357db23e04bf6841850f86c869f32", "52706477c5d24007a70b13554485ce73", "c8b60addbf194beaa2baaa13ca55b53b", "a5b876316d954cec90de7d2db63e63ee", "1401ce1f323e4970883f465425537dde", "56adc36bfc2c45f39a4aefef41683fe0", "f584d1be92824cd887cdc792ae25c848", "8613bce8c0b54a24895c14cc3e129e08", "c146f2236cf843c0b6500cdaf96fcc8f", "76a772e7c0ce4618b09249b1a14d7f46", "4f52a9acae7149c3ae34b6c79ba25d1d", "6af6a25f5b7b4cb5be9c57a528161786", "7635754453f2469ea30b70434a05ef0b", "6738fd4585df48858e4a0873107264ad", "1235dd6e99a14b368ce56c0cde2f7d3d", "b82681e38e06438491f97464eb1b8201", "d0a7f1707bbf488c85c164606fe708c4", "8269aa9b06454c0a8687fd484b20da6c", "4d7531b07bf24ac2b526088d1ff690c9", "c31c403605644fb0ba09038f02409f3a", "9e7c6f31148d41cf922b9f791ec43501", "106bf8a7a1164d048413e2eb7308e734", "1c1231954fbb49028fe866f4f2b97367", "4ab946402d904653a3d5813a3b17bd9c", "a14cd233548746239a752addba8b1741", "5934c90c17f4467ebac636d47a1fe7fc", "c3a1eeab7db4445581f9c4f3bb3c5419", "82047addfdba4d38a530cc81e96bfe75", "93106cbd133e4e4687bee2253e6fe442", "8fc84be040d04ce497cad6d2e3737dcf", "be76c1a8c9ba446d81c71e853fa191ea", "0391adf58391424499d97d42c766a413", "5e217155ba8a4146ab19072b92ee2228", "6228a5337eee4bbe9a7cbccf7d1fb74e", "3f311ef8babc47deb2153fd38c17f5f0", "9253936450d743479dcc97a50656c4f2", "1bfb04415c7b459c988c6d2325c0db8d", "b5b12d9f26624254a502e1e22ea147f6", "f94cb648fbc543f6b1ee413ed8abe450", "47104cb129e14d97830e66d319a8660f", "960ba49bf0d54a908132b9864f1ebdb2", "0cdbeaf1a131424687ff578b92d42ee4", "88c1f3616e304a5f9aa58e935973974f", "bf863cdb2ae74f62b277760a03bbf10d", "9cdc4c76755043c18f8854e7907b99b0" ] }, "id": "3sCOD-9sWjZ_", "outputId": "c7da3805-a7d4-4d88-8c3a-5bcea781a2e8" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b73ba84c160b487a88642e37d40b12d3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading readme: 0%| | 0.00/7.81k [00:00\n", " \n", " \n", " [2502/2502 34:13, Epoch 3/3]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation Loss
10.2804000.235824
20.1619000.239435
30.0396000.303847

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "TrainOutput(global_step=2502, training_loss=0.18205622088709036, metrics={'train_runtime': 2055.9889, 'train_samples_per_second': 38.904, 'train_steps_per_second': 1.217, 'total_flos': 2.104520087402496e+16, 'train_loss': 0.18205622088709036, 'epoch': 3.0})" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from transformers import BertForSequenceClassification, Trainer, TrainingArguments\n", "\n", "# Define the model\n", "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n", "\n", "# Define training arguments with increased batch sizes and mixed precision training\n", "training_args = TrainingArguments(\n", " output_dir='./results',\n", " num_train_epochs=3,\n", " per_device_train_batch_size=32, # Further increased batch size\n", " per_device_eval_batch_size=32, # Further increased batch size\n", " evaluation_strategy='epoch',\n", " save_strategy='epoch',\n", " learning_rate=2e-5,\n", " logging_dir='./logs',\n", " logging_steps=10,\n", " fp16=True, # Enable mixed precision training\n", ")\n", "\n", "# Define the Trainer\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=train_dataset,\n", " eval_dataset=eval_dataset,\n", ")\n", "\n", "# Start training\n", "trainer.train()\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "81BUsUrI5aoP", "outputId": "359840a2-fb70-4525-df65-9e35730acc65" }, "outputs": [ { "data": { "text/plain": [ "('./fine_tuned_bert/tokenizer_config.json',\n", " './fine_tuned_bert/special_tokens_map.json',\n", " './fine_tuned_bert/vocab.txt',\n", " './fine_tuned_bert/added_tokens.json')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.save_pretrained('./fine_tuned_bert')\n", "tokenizer.save_pretrained('./fine_tuned_bert')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 74 }, "id": "VgRgkWHr5dPz", "outputId": "d66f2953-29b2-4f1c-fec5-82dcb117d67d" }, "outputs": [ { "data": { "text/html": [ "\n", "

\n", " \n", " \n", " [209/209 00:51]\n", "
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Evaluation results: {'eval_loss': 0.30384719371795654, 'eval_runtime': 51.8936, 'eval_samples_per_second': 128.455, 'eval_steps_per_second': 4.027, 'epoch': 3.0}\n" ] } ], "source": [ "results = trainer.evaluate()\n", "print(f\"Evaluation results: {results}\")\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XEzKwlQt_7gv", "outputId": "de1e461a-183b-4f7d-cb19-b19e60029aa7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The sentiment of the text is: negative\n" ] } ], "source": [ "from transformers import BertTokenizer, BertForSequenceClassification, AutoConfig\n", "import torch\n", "\n", "# Define the directory containing the model and tokenizer files\n", "model_dir = '.'\n", "\n", "# Load the configuration, model, and tokenizer\n", "config = AutoConfig.from_pretrained(model_dir)\n", "model = BertForSequenceClassification.from_pretrained(model_dir, config=config)\n", "tokenizer = BertTokenizer.from_pretrained(model_dir)\n", "\n", "# Function to make predictions on a given text\n", "def predict_sentiment(text):\n", " # Tokenize the input text\n", " inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=512)\n", "\n", " # Set the model to evaluation mode\n", " model.eval()\n", "\n", " # Disable gradient calculation for faster inference\n", " with torch.no_grad():\n", " # Make the prediction\n", " outputs = model(**inputs)\n", " logits = outputs.logits\n", " predicted_class = torch.argmax(logits, dim=1).item()\n", "\n", " # Convert predicted class to label (0 or 1)\n", " label = 'negative' if predicted_class == 0 else 'positive'\n", "\n", " return label\n", "\n", "# Example usage\n", "text = \"I hate this product! It works bad and is easy to use.\"\n", "prediction = predict_sentiment(text)\n", "print(f\"The sentiment of the text is: {prediction}\")\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.7" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0314d66ef91b4444bb6f6c88518a25c5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": 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