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nvidia-cusolver-cu12==11.4.5.107 (from torch)\n", " Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n", "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch)\n", " Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n", "Collecting nvidia-nccl-cu12==2.20.5 (from torch)\n", " Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n", "Collecting nvidia-nvtx-cu12==12.1.105 (from torch)\n", " Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n", "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.3.0)\n", "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch)\n", " Downloading nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m70.3 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docker_pycreds-0.4.0-py2.py3-none-any.whl (9.0 kB)\n", "Collecting gitpython!=3.1.29,>=1.0.0 (from wandb)\n", " Downloading GitPython-3.1.43-py3-none-any.whl (207 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.3/207.3 kB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: platformdirs in /usr/local/lib/python3.10/dist-packages (from wandb) (4.2.2)\n", "Requirement already satisfied: protobuf!=4.21.0,<6,>=3.19.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (3.20.3)\n", "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (5.9.5)\n", "Collecting sentry-sdk>=1.0.0 (from wandb)\n", " Downloading sentry_sdk-2.7.1-py2.py3-none-any.whl (300 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m300.2/300.2 kB\u001b[0m \u001b[31m33.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hCollecting setproctitle (from wandb)\n", " Downloading setproctitle-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30 kB)\n", "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from wandb) (67.7.2)\n", "Requirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from docker-pycreds>=0.4.0->wandb) (1.16.0)\n", "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n", "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n", "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n", "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n", "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n", "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n", "Collecting gitdb<5,>=4.0.1 (from gitpython!=3.1.29,>=1.0.0->wandb)\n", " Downloading gitdb-4.0.11-py3-none-any.whl (62 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in 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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", "\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 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 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 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 docker-pycreds-0.4.0 gitdb-4.0.11 gitpython-3.1.43 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 pyarrow-16.1.0 requests-2.32.3 sentry-sdk-2.7.1 setproctitle-1.3.3 smmap-5.0.1 wandb-0.17.3 xxhash-3.4.1\n", 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nvidia-cusolver-cu12==11.4.5.107->torch->transformers[torch]) (12.5.82)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.3.2)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.7)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2.0.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2024.6.2)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->transformers[torch]) (2.1.5)\n", "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->transformers[torch]) (1.3.0)\n", "Installing collected packages: transformers, accelerate\n", " Attempting uninstall: transformers\n", " Found existing installation: transformers 4.41.2\n", " Uninstalling transformers-4.41.2:\n", " Successfully uninstalled transformers-4.41.2\n", "Successfully installed accelerate-0.31.0 transformers-4.42.3\n" ] } ], "source": [ "# Install required packages\n", "!pip install transformers datasets torch pandas scikit-learn wandb\n", "!pip install transformers[torch] accelerate -U" ] }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "from transformers import RobertaTokenizer, RobertaForSequenceClassification, Trainer, TrainingArguments\n", "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n", "import torch\n", "import numpy as np\n", "import wandb\n", "from huggingface_hub import HfApi, Repository\n", "import os\n", "import shutil" ], "metadata": { "id": "oYRCDu5h3RPQ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Initialize wandb\n", "wandb.init(project=\"tos-roberta-classifier\", name=\"roberta-large-run\")\n", "\n", "# Load the dataset\n", "dataset = load_dataset('CodeHima/TOS_Dataset')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 449, "referenced_widgets": [ "bbcb15c065e94d76b45d39b19d770889", "a02008bd7cf84badacfe52f545015fde", "2ed8be9ab4d34c179d2bdf342aa5ef18", "12d33e441c7f4f7ea6d7618e32e54b0c", "bf8e2c24b2d744ef8962e42483aba59d", "d4f6ca0cf523477e8c6623c1900b0556", "aaaad66534fa4493b5dc593dd421e6c8", "b2103b7d6319401c842abc9e1cf35183", "2e9acf0e7f10464b9c399b9e12473df8", "ea0a0a46b0534c9590d528665293f278", "07f2794f8e1b4108afac1618341d7df0", "ea6755fbb8c24f2d9ef5a4966fc1d915", "148f7bd3640843379719156075b47061", "ad43b0553848487391296e1f290618d4", "0cf5030c8ed14f30b32c79f6d6f70fba", "eb02c8264c7141d6a19ad140b3f160e6", "c7106312effe49eba0a184d63df35ba7", "22ecdf2f571f4cb29b0744fae40c4dc4", "70fd5ee03f184b6e86f0efe3a79d7361", "9e0a2a5211054aa19ed4913ffb74ab9d", 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"2defc31da0fb4c639944313cebd3456c", "89cdb3369c7547008745fa724414336a", "350108da12864a30be6b62d136cfe1ef", "5d74e150065d42d4856b0511098bd114", "ffc2982552f8413fb5f392061b0eb6b3", "ff044407b5e64af790889e13102c4b14", "b9abd308d3674d52b28eb64a9d73f298", "b53d51e420bc4c06928a391f1077a3be", "7de99082e9434b33a9d9658a44fd82a1", "38723a3a203d4bc3a73e7dc7c36544d4", "4d55b3d0f84d4b248d1a98c45c492f92" ] }, "id": "Isp_YTu23mjN", "outputId": "a9c0af4b-b2b4-444b-8da8-6fd5d2a873b0" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Finishing last run (ID:sb2i231m) before initializing another..." ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "bbcb15c065e94d76b45d39b19d770889" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ " View run roberta-large-run at: https://wandb.ai/starcoder2/tos-roberta-classifier/runs/sb2i231m
View project at: https://wandb.ai/starcoder2/tos-roberta-classifier
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EpochTraining LossValidation LossAccuracyF1PrecisionRecall
10.4435000.3989500.8746990.8588380.8625160.874699
20.4164000.4384090.8530120.8473170.8499160.853012
30.2277000.5058790.8963860.8933250.8915210.896386
40.0526000.6675320.8915660.8931670.8951150.891566
50.1242000.7470900.8843370.8874120.8918070.884337

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=3365, training_loss=0.3287806587534532, metrics={'train_runtime': 3310.0901, 'train_samples_per_second': 8.124, 'train_steps_per_second': 1.017, 'total_flos': 2.505971903112192e+16, 'train_loss': 0.3287806587534532, 'epoch': 5.0})" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "code", "source": [ "# Evaluate the model on validation data\n", "validation_results = trainer.evaluate(eval_dataset=validation_data)\n", "print(f\"Validation Accuracy: {validation_results['eval_accuracy']}\")\n", "\n", "# Evaluate the model on test data\n", "test_results = trainer.evaluate(eval_dataset=test_data)\n", "print(f\"Test Accuracy: {test_results['eval_accuracy']}\")\n", "\n", "# Save the model\n", "model.save_pretrained('./tos-roberta-classifier')\n", "tokenizer.save_pretrained('./tos-roberta-classifier')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 159 }, "id": "a4p9TPYe46w-", "outputId": "224061bf-0e26-4739-a4cd-416c43763bcd" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [52/52 00:48]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Validation Accuracy: 0.8963855421686747\n", "Test Accuracy: 0.8583815028901735\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "('./tos-roberta-classifier/tokenizer_config.json',\n", " './tos-roberta-classifier/special_tokens_map.json',\n", " './tos-roberta-classifier/vocab.json',\n", " './tos-roberta-classifier/merges.txt',\n", " './tos-roberta-classifier/added_tokens.json')" ] }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "code", "source": [ "# Function to predict unfairness level\n", "def predict_unfairness(text, model, tokenizer):\n", " inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True, max_length=512)\n", "\n", " model.eval()\n", " with torch.no_grad():\n", " outputs = model(**inputs)\n", "\n", " probabilities = torch.softmax(outputs.logits, dim=-1).squeeze()\n", " predicted_class = torch.argmax(probabilities).item()\n", "\n", " inverse_label_mapping = {0: 'clearly_fair', 1: 'potentially_unfair', 2: 'clearly_unfair'}\n", " predicted_label = inverse_label_mapping[predicted_class]\n", "\n", " return predicted_label, probabilities.tolist()" ], "metadata": { "id": "DohDrhrd4-cy" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Test the model\n", "def test_model():\n", " model_path = './tos-roberta-classifier'\n", " model = RobertaForSequenceClassification.from_pretrained(model_path)\n", " tokenizer = RobertaTokenizer.from_pretrained(model_path)\n", "\n", " while True:\n", " text = input(\"Enter a clause to classify (or 'quit' to exit): \")\n", " if text.lower() == 'quit':\n", " break\n", "\n", " predicted_label, probabilities = predict_unfairness(text, model, tokenizer)\n", "\n", " print(f\"\\nPredicted unfairness level: {predicted_label}\")\n", " print(\"Probabilities:\")\n", " for label, prob in zip(['clearly_fair', 'potentially_unfair', 'clearly_unfair'], probabilities):\n", " print(f\"{label}: {prob:.4f}\")\n", " print()" ], "metadata": { "id": "oo3ftCcb5BsQ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "if __name__ == \"__main__\":\n", " test_model()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QY_GyqJG5aWg", "outputId": "81ef4698-e1a2-4413-ebe4-c6cee9ac9a01" }, "execution_count": null, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a clause to classify (or 'quit' to exit): We reserve the right to suspend, terminate, or restrict your access to the platform at any time and for any reason, without prior notice or explanation. This includes but is not limited to violations of our community guidelines or terms of service, as determined solely by ConnectWorld.\n", "\n", "Predicted unfairness level: potentially_unfair\n", "Probabilities:\n", "clearly_fair: 0.0034\n", "potentially_unfair: 0.9077\n", "clearly_unfair: 0.0889\n", "\n", "Enter a clause to classify (or 'quit' to exit): We are committed to protecting your privacy and personal information. We will not sell or share your data with third parties without your consent, except as required by law or as necessary to provide our services. Our privacy policy outlines how we collect, use, and protect your data.\n", "\n", "Predicted unfairness level: clearly_fair\n", "Probabilities:\n", "clearly_fair: 0.9953\n", "potentially_unfair: 0.0044\n", "clearly_unfair: 0.0002\n", "\n", "Enter a clause to classify (or 'quit' to exit): We reserve the right to change, modify, or update these terms at any time without prior notice. Your continued use of the platform constitutes acceptance of any changes made.\n", "\n", "Predicted unfairness level: potentially_unfair\n", "Probabilities:\n", "clearly_fair: 0.0007\n", "potentially_unfair: 0.9974\n", "clearly_unfair: 0.0019\n", "\n", "Enter a clause to classify (or 'quit' to exit): By using ConnectWorld, you grant us an irrevocable, perpetual, worldwide, royalty-free license to use, reproduce, modify, adapt, publish, translate, create derivative works from, distribute, and display any content posted on the platform, including personal information and user-generated content.\n", "\n", "Predicted unfairness level: clearly_fair\n", "Probabilities:\n", "clearly_fair: 0.9994\n", "potentially_unfair: 0.0003\n", "clearly_unfair: 0.0003\n", "\n", "Enter a clause to classify (or 'quit' to exit): Our community guidelines are designed to maintain a safe and inclusive environment for all users. We will enforce these guidelines consistently and fairly, taking into account the context of each situation. Users will be provided with clear explanations if their content is removed or if their account is suspended or terminated.\n", "\n", "Predicted unfairness level: clearly_fair\n", "Probabilities:\n", "clearly_fair: 0.9794\n", "potentially_unfair: 0.0200\n", "clearly_unfair: 0.0006\n", "\n", "Enter a clause to classify (or 'quit' to exit): quit\n" ] } ] }, { "cell_type": "code", "source": [ "# Push the model to Hugging Face Hub\n", "def push_to_hub():\n", " # Set your Hugging Face token and repo name\n", " hf_token = \"Your_HuggingFace_APi\"\n", " repo_name = \"Tos-Roberta\"\n", "\n", " # Set the path to your local model\n", " local_model_path = \"./tos-roberta-classifier\"\n", "\n", " # Initialize Hugging Face API\n", " api = HfApi()\n", "\n", " # Create a new repository\n", " repo_url = api.create_repo(\n", " repo_id=repo_name,\n", " token=hf_token,\n", " private=False,\n", " exist_ok=True\n", " )\n", "\n", " # Clone the empty repository\n", " repo = Repository(local_dir=repo_name, clone_from=repo_url, use_auth_token=hf_token)\n", "\n", " # Load your trained model and tokenizer\n", " model = RobertaForSequenceClassification.from_pretrained(local_model_path)\n", " tokenizer = RobertaTokenizer.from_pretrained(local_model_path)\n", "\n", " # Save the model and tokenizer to the cloned repository\n", " model.save_pretrained(repo_name)\n", " tokenizer.save_pretrained(repo_name)\n", "\n", " # Add a README file\n", " with open(os.path.join(repo_name, \"README.md\"), \"w\") as f:\n", " f.write(\"# Tos-Roberta\\n\\nThis model is trained to classify clauses in Terms of Service (ToS) documents using RoBERTa-large.\")\n", "\n", " # Push the files to the Hugging Face Hub\n", " repo.git_add()\n", " repo.git_commit(\"Initial commit\")\n", " repo.git_push()\n", "\n", " print(f\"Model successfully pushed to https://huggingface.co/{repo_name}\")" ], "metadata": { "id": "hZmUOUa85E4z" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "if __name__ == \"__main__\":\n", " test_model()\n", " push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 642, "referenced_widgets": [ "ce6deb3fd7224ee0bc256075a27086b4", "7e1dacbed378436dad5c1c3d270537d9", "d0a0602443744257a8030cdd560abade", "4ce4532d0b9c40139204a23d08a78e7e", "eb0fd951178749109076493e2b033081", "af3eb95ad398476da4c94c355f622f20", "f91135c2f0eb488db2e616e3177861c6", "6fb67fa753cd4c81a813e5cc4cd0a7e2", "c8944fd096504c678d61675ee0a300d4", "6389792f208e40d7848897911f0a4e6e", "2a2e8ac24b68443cb46a738b35c152a3" ] }, "id": "F3Ni9iXk5S8L", "outputId": "e9a2431c-b95b-41fd-8ace-28e0e92b897d" }, "execution_count": null, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a clause to classify (or 'quit' to exit): quit\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:131: FutureWarning: 'Repository' (from 'huggingface_hub.repository') is deprecated and will be removed from version '1.0'. Please prefer the http-based alternatives instead. Given its large adoption in legacy code, the complete removal is only planned on next major release.\n", "For more details, please read https://huggingface.co/docs/huggingface_hub/concepts/git_vs_http.\n", " warnings.warn(warning_message, FutureWarning)\n", "Cloning https://huggingface.co/CodeHima/Tos-Roberta into local empty directory.\n", "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/CodeHima/Tos-Roberta into local empty directory.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Upload file model.safetensors: 0%| | 1.00/1.32G [00:00 main\n", "\n", "WARNING:huggingface_hub.repository:remote: \u001b[33m-------------------------------------------------------------------------\u001b[0m \n", "remote: \u001b[33mYour push was accepted, but with warnings: \u001b[0m \n", "remote: \u001b[33m- Warning: empty or missing yaml metadata in repo card\u001b[0m \n", "remote: \u001b[33mhelp: https://huggingface.co/docs/hub/model-cards#model-card-metadata\u001b[0m \n", "remote: \u001b[33m-------------------------------------------------------------------------\u001b[0m \n", "remote: \u001b[32m-------------------------------------------------------------------------\u001b[0m \n", "remote: \u001b[32mPlease find the documentation at:\u001b[0m \n", "remote: \u001b[32mhttps://huggingface.co/docs/hub/model-cards#model-card-metadata\u001b[0m \n", "remote: \u001b[32m\u001b[0m \n", "remote: \u001b[32m-------------------------------------------------------------------------\u001b[0m \n", "To https://huggingface.co/CodeHima/Tos-Roberta\n", " b7f5bf7..57579c3 main -> main\n", "\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Model successfully pushed to https://huggingface.co/Tos-Roberta\n" ] } ] }, { "cell_type": "code", "source": [ "# Finish wandb run\n", "wandb.finish()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 586, "referenced_widgets": [ "83cd806013344549a6ecf57c9c98fd08", "1d66edc17d804a7c84b3424be17a690f", "7e0cd46e937c48ecb936c82c86ed8c91", "ea05bc814f2049969755247ef731be66", "5782edd875504a41898cef64d6b0b445", "3514655677c94ab2ab288d3612acb584", "37ea1e0f2bf7405799bd17e4583bf7e6", "9862a9fc85454abd8848c4ccc77aceb5" ] }, "id": "fiy4Srmf5Wds", "outputId": "aff01493-fc0a-4cf4-e8a7-661082021642" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(Label(value='0.003 MB of 0.003 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "83cd806013344549a6ecf57c9c98fd08" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

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Run summary:


eval/accuracy0.85838
eval/f10.84973
eval/loss0.65101
eval/precision0.84726
eval/recall0.85838
eval/runtime34.6228
eval/samples_per_second29.98
eval/steps_per_second3.755
total_flos2.505971903112192e+16
train/epoch5.0
train/global_step3365
train/grad_norm0.00794
train/learning_rate0.0
train/loss0.1242
train_loss0.32878
train_runtime3310.0901
train_samples_per_second8.124
train_steps_per_second1.017

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ " View run roberta-large-run at: https://wandb.ai/starcoder2/tos-roberta-classifier/runs/g8aoulir
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