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Skwarczynski","userId":"01069351128001430158"}},"outputId":"5baebdd1-ecd0-4354-98d7-9ca6689cff7d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.38.2)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.4)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.20.3)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.0)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) 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(2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2024.2.2)\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>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->transformers[torch]) (1.3.0)\n","Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, accelerate\n","Successfully installed accelerate-0.29.3 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-cu12-2.19.3 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, EarlyStoppingCallback\n","from datasets import load_dataset, load_metric, Dataset, concatenate_datasets\n","import numpy as np\n","import pandas as pd\n","import torch\n","import random"],"metadata":{"id":"pGewnnydJxYC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def tokenize(batch):\n"," encoding = tokenizer(batch['text'], padding=\"max_length\", truncation=True, max_length=256, return_tensors='pt')\n"," return encoding\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","def random_swap(sentence, n=5):\n"," length = len(sentence)\n"," if length <= 1:\n"," return sentence\n"," n = min(n, length - 1)\n"," for _ in range(n):\n"," idx1, idx2 = random.sample(range(length), 2)\n"," sentence[idx1], sentence[idx2] = sentence[idx2], sentence[idx1]\n"," return sentence\n","\n","metric = load_metric(\"accuracy\")\n","\n","# Load the dataset\n","dataset = load_dataset(\"vedantgaur/GPTOutputs-MWP\", trust_remote_code=True)\n","\n","# Combine the 'Prompts' and 'Outputs' columns into one\n","prompts = Dataset.from_dict({'text': dataset['train']['Prompts'], 'labels': [0]*len(dataset['train']['Prompts'])})\n","outputs = Dataset.from_dict({'text': dataset['train']['Outputs'], 'labels': [1]*len(dataset['train']['Outputs'])})\n","combined_dataset = concatenate_datasets([prompts, outputs])\n","\n","# Apply data augmentation\n","combined_dataset = combined_dataset.map(lambda x: {'text': ' '.join(random_swap(' '.join(x['text']).split())), 'labels': x['labels']})\n","\n","# Shuffle the data\n","shuffled_dataset = combined_dataset.shuffle()\n","\n","# Tokenize the 'text' column\n","tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n","tokenizedDataset = shuffled_dataset.map(tokenize, batched=True)\n","\n","# Split the tokenized dataset into a training set and a validation set\n","tokenizedDataset = tokenizedDataset.train_test_split(test_size=0.2)\n","\n","# Model: bert-base-uncased\n","model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=2)\n","\n","# Adjust the dropout rate\n","model.dropout.p = 0.3\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"," load_best_model_at_end=True,\n"," evaluation_strategy=\"steps\",\n"," save_strategy=\"steps\",\n"," weight_decay=0.01,\n",")\n","\n","# Creation of Bert Trainer Object\n","trainer = Trainer(\n"," model=model,\n"," args=BertTraining_args,\n"," train_dataset=tokenizedDataset['train'],\n"," eval_dataset=tokenizedDataset['test'],\n"," compute_metrics=compute_metrics,\n"," callbacks=[EarlyStoppingCallback(early_stopping_threshold=0.01, early_stopping_patience=1)],\n",")\n","\n","# Fine-tune the Model\n","trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448,"referenced_widgets":["f9c004cf3b1b4d7a95ed1aaa4db517ed","fe2afd3d46ff40e7ace96c85e20f1429","7a5aa7b0a9a246c38188132a2d660b12","a0a27bee44e648a0820e1264995863ea","7a488d54a526462e9dd97fabb237d117","22291a023c5c492aa7863b4a9cc52e12","a00360c874004d34b69189c115878f91","57ec5e8ec96d4fcdb84234cbf389a369","93c95990f6a140f9812f981e6ea58e76","21aa88709c484f50b3b6c12c4d626329","9b481fbafe7c401c85a58eeff067d8a4","007c4c49b1e844dc9db742a8a55f0621","754a08e4bdc34f6baacc1a778de32dcf","bebd2a954c17441a928a478e850a8749","fa183154a222496eb332e03a65b64804","e8f34311421a4974afdc0254903d6332","53d532dafb9247e98d1424ff79b94091","106cde7d8fa04f7a8feba61251b8ea96","943d8f184f0a4c81914dccc7f761174e","fa49738bdc4344ba94c397895817e313","bc640dd101284060980ae2f8a6239ecf","66a08b59a5f347bfa63d189744da4edd","1f303b0c72f64e868805dee3bbeac417","f29e760067f9419089f54be5fa541f5c","bbc274ef12444a7a86f2e3e8b3d53c2d","cdbff3736a5a4cc49fab75196ecd5445","9ea58b3e73714c68adade58f5a66b1de","a0bea12019274df8a5e6e4feb9d7ae51","2eb3ee6d249949bd811dad034b2d19fe","83bacaf2e11342f29c8c205c5ac296cc","51911fbd44674cd48912a4fa9d821720","7c330764c63440cbac2a5878f3831bce","1d23bab6ad7c46fbb1c635b667e12a53"]},"id":"7zVLQyqEplq6","executionInfo":{"status":"ok","timestamp":1713742878298,"user_tz":240,"elapsed":694012,"user":{"displayName":"Peyton Skwarczynski","userId":"01069351128001430158"}},"outputId":"c6a85c3a-bfe7-45c9-98c1-5c929a13c680"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-6-bd4c63323459>:22: 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":"f9c004cf3b1b4d7a95ed1aaa4db517ed"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/6096 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"007c4c49b1e844dc9db742a8a55f0621"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/6096 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1f303b0c72f64e868805dee3bbeac417"}},"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"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='915' max='915' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [915/915 11:23, 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"," <th>Validation Loss</th>\n"," <th>Accuracy</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>500</td>\n"," <td>0.061000</td>\n"," <td>0.042642</td>\n"," <td>0.993443</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=915, training_loss=0.04021555113662136, metrics={'train_runtime': 684.47, 'train_samples_per_second': 21.371, 'train_steps_per_second': 1.337, 'total_flos': 1924394258903040.0, 'train_loss': 0.04021555113662136, 'epoch': 3.0})"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["# Predictions\n","predictions = trainer.predict(tokenizedDataset['test'])\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-and-human-generated\", token=apiToken)\n","\n","# Push the tokenizer to Huggingface\n","tokenizer.push_to_hub(\"SkwarczynskiP/bert-base-uncased-finetuned-vedantgaur-AI-and-human-generated\", token=apiToken)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":133,"referenced_widgets":["71eb779c65c24773adc245c3804af3ba","da5e793c59fd4d23baa441d2bcf9b20c","130519e888294c199546df95b537d3c7","2d07e9c034ba44bc92997c9e2643d215","a636199e9d15480d84813de1a636b1d2","d87ab0d048144562a770abbb02a81c2e","cbdcb9c099f94414b5276e10886507f3","2eb6e2901ec4485182bb22777f5f2f39","eb67e351347b460ebf1ee49eddf0563b","74ef0e12b1bc49948e790de42dd5ad77","e2cd3256938445bdb1e47687b340d2fb","bef080335498488d9b0b99d32a7e4955","ddd9631d029240be99cc3b09a5422e3e","0834d1bd5c6d43e0af258de69e10d257","80ef0116d39845c1ad0252d6c5ff5e4d","5053d5240bf047e4a9c57686301ea7f0","4483e28a5da04c97ae9dddd349518d57","daffe99117f945ea96e3149408270397","e6c7d4040a6d494883902d3a2440ca8c","8d3256ffcb3d422eabe320105219cede","9c0c771dd9564aa5b00e9e55fbbcb711","1c6cc8f5d8db49888ce622fd3eab64ba"]},"id":"83HxyQ53XS8s","executionInfo":{"status":"ok","timestamp":1713742953624,"user_tz":240,"elapsed":66475,"user":{"displayName":"Peyton Skwarczynski","userId":"01069351128001430158"}},"outputId":"4b1dd6ae-e34a-492e-f54d-fa1f2dac6900"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["{'accuracy': 0.9934426229508196}\n"]},{"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":"71eb779c65c24773adc245c3804af3ba"}},"metadata":{}},{"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":"bef080335498488d9b0b99d32a7e4955"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["CommitInfo(commit_url='https://huggingface.co/SkwarczynskiP/bert-base-uncased-finetuned-vedantgaur-AI-and-human-generated/commit/1f19478c0013107ec916792737d66be56f3f97c3', commit_message='Upload tokenizer', commit_description='', oid='1f19478c0013107ec916792737d66be56f3f97c3', pr_url=None, pr_revision=None, pr_num=None)"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":7}]}]} |