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nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105\n"]}],"source":["!pip install transformers\n","!pip install datasets\n","!pip install numpy\n","!pip install transformers[torch]"]},{"cell_type":"code","source":["from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments\n","from datasets import load_dataset, load_metric\n","from sklearn.preprocessing import LabelEncoder\n","import numpy as np"],"metadata":{"id":"Dv28KDHvq_1h"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def tokenize(text):\n"," outputs = text['Outputs']\n"," tokenized_outputs = tokenizer(outputs, padding=\"max_length\", truncation=True, max_length=256)\n"," labels = np.ones(len(outputs)) # Create an array of ones with the same length as 'Outputs'\n"," return {'input_ids': np.array(tokenized_outputs['input_ids']), 'labels': labels}\n","\n","\n","def compute_metrics(eval_pred):\n"," logits, labels = eval_pred\n"," predictions = np.argmax(logits, axis=-1)\n"," return metric.compute(predictions=predictions, references=labels)\n","\n","\n","# Load the dataset\n","dataset = load_dataset(\"vedantgaur/GPTOutputs-MWP\", trust_remote_code=True)\n","\n","# Add a new field 'labels' that always contains the value 1 (representing 'AI generated')\n","dataset = dataset.map(lambda example: {'labels': 1})\n","\n","# Tokenize the dataset\n","tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n","tokenizedDataset = dataset.map(tokenize, batched=True)\n","\n","# Split into train and test sets\n","trainDataset = tokenizedDataset['train']\n","testDataset = tokenizedDataset['validation']\n","\n","# Model: bert-base-uncased\n","model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=2)\n","\n","BertTraining_args = TrainingArguments(\n"," output_dir=\"test_trainer\",\n"," per_device_train_batch_size=16,\n"," per_device_eval_batch_size=16,\n"," learning_rate=5e-5,\n"," num_train_epochs=3,\n",")\n","\n","# Creation of Bert Trainer Object\n","trainer = Trainer(\n"," model=model,\n"," args=BertTraining_args,\n"," train_dataset=trainDataset,\n"," eval_dataset=testDataset,\n"," compute_metrics=compute_metrics,\n",")\n","\n","# Fine-tuning the Model\n","trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":904,"referenced_widgets":["81c1ac39a7a94ae18f729785bc421bd7","19976920dd484456b025ec9a58aa8e69","b603d74522b9426ca42eeb28d0b2b8a7","5fa1d21406c641ec984ca9b496e7c2e2","95eeed252961468aadbc63667d9a9583","6e5ab438182747789d190f1b0bbd4c04","336d34fcce26478fa53be9cbe84de059","3165addee3f44498979ab1dd34fcb12d","f517a7a9a21a4171b98a81fdcd563aca","04fc895894c14ece925b735a77152391","02d55b5403c44fa4bc7c1d476809ac03","576fb25e0c184310be676975c76f4d0f","a637af71251141768b5b3ffbd627a0c5","dc9c0179b5ff4690b3b263bcf94a5cee","9876909179d74135b2c85cf7a11217ee","7264cda1af5445d8ae2f4a9732a000a7","0a4cd1ae03e54b888f09b574a633dfe2","cdb1de1c0ddc47dda32e1ea794a0c600","52d0f378dae54695a4049058aad3ed1f","d4d1ec8c9727415784dc756de3b830d7","6ccf7d63dfa347ee92a1a70a4f8eebd4","59b460caf864495396b77b8206e11498","9e3f71ff5c3b49409809a76f010e682b","0943e84c5e6e42fbb8afb2b5a1eee09f","58b3439386824b5bb4adcabf5f7c0185","a0ff934fa2e54e3781dc73a476d6b979","da4e3f9fc79f42c89b27c8e7aa493958","96b898ff02b04b9fa82de6d7faba23b4","8f212f0d640c4ec4a0d42b226bab2a10","dc33fdfda99c40b589dd68b51dbaf59b","f9186bf2d9e146b38ae70d781d272ad9","12f005d15e9c4744b2b8b70cf2ca0028","de3fa7c12b294bed898be121f3128f46","d432b70233934c7a9695c406240fae51","8c45381148c74e61a5decd4aef50a202","fa15d0feef774314a44f9c1c1a3fbc33","f3d8301858224a58ac5441cd0d934f7c","6febe5cc961249f89536808c7b7a81af","40930195470f418599870833e4f760bc","8cd4e5e1addb4d1aa2eed89a43281ad9","3ec6e39df73e42568c6f14115da29dd8","bc7bd519be1c4a3a97734d2d7faf08c5","448c75ec56fb4c1fb4b493f1d07c8aa1","be5eaaf5220c4d57a19124acfec9c04e","7d0fffa5f0ea439d9f63bcc09d5acfd4","086358571ed54304b83dc560bda16b55","9836e35c81b7479daccba05e259ce189","c108444f1a5f47e09af23c0b34cd4046","b9125327d77c48ad9fb923712f44696a","e73dbdc802a24c64aef86b9862c35ef2","ddc60c60902c49b5a6dd6b9cfd7049c0","f7a12d8e0ab54e93b56552cc95365e82","4bcb149e55474a36922c1d6608f0b46f","f3c65536c28047b8bddc2b54631a2739","5a3e07021a964cc280916901c79308e1","cba01437822c41afb70174d8a04526be","e4288f547a13479093fd88bc7c9d0a56","68c9ddc8feac4fd9bbdfd9822c847b30","07997a7f93cd408bb65063347d83e53a","cd1a31593c744627b852b04232429c6c","d33ebb28a9d24b4db9bc9d3cd1d76e23","d476c231bd3f45b0b552f20324ee8e91","c4a136cb5e4e4dd8b6e0113c94f209a4","d6b9c374fd36422c91ebdd8c0f3e6d83","abfeff2afe7f475eaadd046e480db07e","3a27e95a424942aa9a50471eb95e5a76","b54487ea8602416491a96cff9f1f8913","0da0efe9fef5440b8a673bbce4ee4285","71e646e21c5a4d12be1436bf5e08243c","cb2c882c5580493c89e2e1da8e07ec23","bed50f86f53c4a758d3202e8ad653646","550c566ebc0642f7a0d32c58d1a06374","19e10f8eadac46b185b24bcd81bb1ad6","add4344087364a65a999dd137f9825e7","d4e6d9a6d6fb43ba88bb3988c2df0e55","e77ad3c9c0f84d34b44da6b5022c90e9","320b102cac094cc9be1a3c614ef076f8","81d18c6a8f484c80a7678488e96fda2c","c6a00cbe6d60474b903e2561df289a7a","51920a127cf9459b83544c999d8fba6b","466df3ea133a4b91856337e51b1331a4","68acad55764a4e32b54aee8a52226f6e","47ebb07a69284d28b58c0f864748ab90","400cb2f785174dec9669816b02a32623","b59b43a1c826411a860e05ac752c83c3","c1f5676834b84250ba357d9c33a3a2a7","d09c73ea82a04a58a1631725c508da5e","29b3cb00f41d48b4a8c8a094c9532a6d","e68050eb614a4b6a82a3eaed6aa29b53","6dc54505156944faabad4f5c340e5257","15e58e0a329d469ebf91240940b8d05f","357a706897d9461197722de3f8741c53","74003efa791b4e719141eb8880b5b483","af7f74fc9d414b799d4e037738598b5f","e01fe8be2f6342f89773bf7139c61138","5f87e50d4c154713b8daf9131f256a3f","a7abf35455664cd693ceb0a076da055d","dc8bd59e3eee412594275bbbf7fd1e47","48b713ce3d3341ea926403392b2eebc9","0927d35909bf4ff8a590debda3f2c989","335a89e9451a4317bcb4c60c79df3a06","204af3d8c774479f8bf9707a4095aefc","0eb0cb54b2144819ab46fb102ac3d7d8","2b4ebe6d6afe453e8da14a243162f12a","b646965e06e648b0a00fa14a96450979","817c20c54fec4da3b4856b8f4609ddf4","d72a0d02975f420ba97ca645381d8358","ca2bad03286f4a1eacb97c9dfa32d912","bc5d9d6b3a8040ff8a3de1ae6eae7a5d","8f0e345ad35f4e9da0723424fb5f8056","782412d9001e45d39a96d068900b0454","338f6fe5bae14cee821c520ba4aac51f","b4455912e2df4143b0f9bb283af951a0","8889464567bf48599c2c824f0d73c4fa","5172f7312d1d416189d69ce36db8f2e3","b1352ce5ebd14c57a49ba29b7fd866fb","57a51f4f705d47fb896d8e5d4d8212a9","4a0a981309b64cad88fc71d12506510f","da2daeea6e19495d955f72bee172baa9","7f9c81b41cfc487cb0cb7eaad1165299","e9c648d7b1f7422e89583ed6514930b9","60dcc013aee844ec8e4360a162013101","f1884b2477024fdeb748abf0ca01571f","65494101f4044c39a6e6719521c0872b","532e927bb72c4341b1678579983c4f3c","9eef88ceb7ef4c23ae022b5c2c0d7ae7","c3f662d03a7d4d23963a1d9600671478","dfb33fbef78c44e188b02bf53b1144e9","fb34868785f64f9fae21e15329f4d257","6d5eb69998dc4439bfad8c195674c80f","8af28d5a64ad4ed19c895c8d3de9d312","879cbb33953e45d4b4a38a169da8aac9","884b2d4a163843e5bea9776e7b5c6869","349e882dd736421a9410a593b1ced867","2bc42e03f0e74960bbfb54668be87060","6ffdb42bb71141aa92dd2147cd201abf","4c5a867948e2419995a2c83a6bcfab8b","1ef6b25e5dd04d0597f5ea229eff35b2","201b3895b93f4d7cb6b8591454cd2ee1","ff4d6addfc814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Skwarczynski","userId":"08544190469990276444"}},"outputId":"46f0ec6e-4618-4bc2-9880-a3a3a5e99cdb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","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"]},{"output_type":"display_data","data":{"text/plain":["Downloading metadata: 0%| | 0.00/960 [00:00<?, 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[00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7d0fffa5f0ea439d9f63bcc09d5acfd4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/3048 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cba01437822c41afb70174d8a04526be"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/762 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b54487ea8602416491a96cff9f1f8913"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","\n","Model: bert-base-uncased\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 0%| | 0.00/48.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"81d18c6a8f484c80a7678488e96fda2c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e68050eb614a4b6a82a3eaed6aa29b53"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0927d35909bf4ff8a590debda3f2c989"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0%| | 0.00/466k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"782412d9001e45d39a96d068900b0454"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/3048 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"60dcc013aee844ec8e4360a162013101"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/762 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"884b2d4a163843e5bea9776e7b5c6869"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"563f494c30e04d7bba4c468b0693021f"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n","dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n"," warnings.warn(\n","We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='573' max='573' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [573/573 06:49, Epoch 3/3]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>500</td>\n"," <td>0.003900</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=573, training_loss=0.0034153302320820422, metrics={'train_runtime': 411.6063, 'train_samples_per_second': 22.215, 'train_steps_per_second': 1.392, 'total_flos': 1202943745105920.0, 'train_loss': 0.0034153302320820422, 'epoch': 3.0})"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["# Predictions\n","metric = load_metric(\"accuracy\")\n","predictions = trainer.predict(testDataset)\n","classPredictions = np.argmax(predictions.predictions, axis=-1)\n","evalMetrics = compute_metrics((predictions.predictions, predictions.label_ids))\n","print(evalMetrics)\n","\n","apiToken = \"REDACTED\"\n","\n","# Push the fine-tuned model to Huggingface\n","model.push_to_hub(\"SkwarczynskiP/bert-base-uncased-finetuned-vedantgaur-AI-generated\", token=apiToken)\n","\n","# Push the tokenizer to Huggingface\n","tokenizer.push_to_hub(\"SkwarczynskiP/bert-base-uncased-finetuned-vedantgaur-AI-generated\", token=apiToken)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":359,"referenced_widgets":["3c577a959acc4a08ae7fa88ebe777de9","45397808a485456fa67ee223c8db2bcb","7917a0ef6c9c4c3a90ea26ff9d638be1","0708031fa92d4c6094a36f359438abaa","79f3d3dc65bd4a83bf06e0e6187f537a","9346c68b76f5483995d097447895df22","d9ba3d700c9d46c38ad41d1a4aa30ed9","6e48e6089a474f50a1c3eaefc6d0152f","e5dcdb9ce71045d8a4ca4a90c2d8c8f5","546a6642502e4a22a33eee44649101cd","63512e347c8d411c8255ba04dcec5eac","0cbb402f7b324a47b4a8dfeb8672c29f","08a0191ddbd9474f8a14c9322c1837a7","5ad03dad4ae14b33bfa4c811148e1423","1e64c53c27ef46fc8c2c7665662523fc","5370fd799a344d71a00ec47332ced5a2","14c2fcb532174a9a94692642f8a478bc","5c463e6e6181417f99badd4d175dd107","0c41038e3b58414691444a3ee25732d7","f93165e43d1449bfb3f583781334d932","5c2c4e5ae06444d88a4d3ef8a03dab01","d909504f9cf745358bfd955995a9f5be","425ca27eb2f945de96707b3f0454f222","7973db3060394ff3b2a1bb5d97c2ebb7","8cc2e66eb03a4e4ba0621dbf79cb7e93","aa3609c00452435694ea051ac10d5331","fc27bc2b46fa484bb17cbedcc1ce046a","a03c8764993343159d96928087220d19","971ad557718d47d08599d05ef2a9c38c","c39b6b22b5224f01bf8a50a63ebc20e3","6eb7f1520d244cf7baa6ee68c1aacdd0","4be306c97b0c4c7eb574d9cdbfad0ed6","861b1365d7cd4124b2bf00a78cf086f4"]},"id":"gjpfE8VIrFxU","executionInfo":{"status":"ok","timestamp":1713721870336,"user_tz":240,"elapsed":53077,"user":{"displayName":"Peyton Skwarczynski","userId":"08544190469990276444"}},"outputId":"776b3372-6d5c-4e50-d6a1-c4acf338cf02"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-4-637832f5a9d2>:2: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n"," metric = load_metric(\"accuracy\")\n","/usr/local/lib/python3.10/dist-packages/datasets/load.py:759: FutureWarning: The repository for accuracy contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.19.0/metrics/accuracy/accuracy.py\n","You can avoid this message in future by passing the argument `trust_remote_code=True`.\n","Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading builder script: 0%| | 0.00/1.65k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3c577a959acc4a08ae7fa88ebe777de9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["{'accuracy': 1.0}\n"]},{"output_type":"display_data","data":{"text/plain":["model.safetensors: 0%| | 0.00/438M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0cbb402f7b324a47b4a8dfeb8672c29f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["README.md: 0%| | 0.00/5.17k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"425ca27eb2f945de96707b3f0454f222"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["CommitInfo(commit_url='https://huggingface.co/SkwarczynskiP/bert-base-uncased-finetuned-vedantgaur-AI-generated/commit/b68c7f39f8996bc852534aed1394f3afe87def14', commit_message='Upload tokenizer', commit_description='', oid='b68c7f39f8996bc852534aed1394f3afe87def14', pr_url=None, pr_revision=None, pr_num=None)"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":4}]}]} |