distilbert-finetuning-ai-text-detec.ipynb

{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":10042497,"sourceType":"datasetVersion","datasetId":6186454}],"dockerImageVersionId":30787,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":5,"nbformat":4,"cells":[{"id":"d033c126","cell_type":"code","source":"#basic imports \nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport datasets\nimport transformers\nfrom tqdm import tqdm\nimport warnings \nwarnings.filterwarnings('ignore')\ndevice = torch.device(\"cuda\")\nfrom sklearn.metrics import f1_score,accuracy_score","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:30:56.573821Z","iopub.execute_input":"2024-11-29T12:30:56.574633Z","iopub.status.idle":"2024-11-29T12:30:56.580014Z","shell.execute_reply.started":"2024-11-29T12:30:56.574597Z","shell.execute_reply":"2024-11-29T12:30:56.579059Z"}},"outputs":[],"execution_count":25},{"id":"f99b8aca","cell_type":"markdown","source":"### Load dataset","metadata":{}},{"id":"0b4aa955","cell_type":"code","source":"from datasets import load_dataset\ntext = load_dataset(\"csv\",data_files=\"/kaggle/input/ai-vs-human/AI_vs_Human_essays100k.csv\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:30:56.581931Z","iopub.execute_input":"2024-11-29T12:30:56.582674Z","iopub.status.idle":"2024-11-29T12:30:56.881043Z","shell.execute_reply.started":"2024-11-29T12:30:56.582631Z","shell.execute_reply":"2024-11-29T12:30:56.880349Z"}},"outputs":[],"execution_count":26},{"id":"72f07af7","cell_type":"code","source":"text = text.remove_columns('Unnamed: 0')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:30:56.882012Z","iopub.execute_input":"2024-11-29T12:30:56.882292Z","iopub.status.idle":"2024-11-29T12:30:56.888500Z","shell.execute_reply.started":"2024-11-29T12:30:56.882266Z","shell.execute_reply":"2024-11-29T12:30:56.887737Z"}},"outputs":[],"execution_count":27},{"id":"5b88f726","cell_type":"code","source":"text = text['train'].train_test_split(train_size=0.8) ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:30:56.889550Z","iopub.execute_input":"2024-11-29T12:30:56.889827Z","iopub.status.idle":"2024-11-29T12:30:56.923300Z","shell.execute_reply.started":"2024-11-29T12:30:56.889796Z","shell.execute_reply":"2024-11-29T12:30:56.922744Z"}},"outputs":[],"execution_count":28},{"id":"4bcb4346","cell_type":"code","source":"text","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:30:56.925113Z","iopub.execute_input":"2024-11-29T12:30:56.925354Z","iopub.status.idle":"2024-11-29T12:30:56.930478Z","shell.execute_reply.started":"2024-11-29T12:30:56.925331Z","shell.execute_reply":"2024-11-29T12:30:56.929695Z"}},"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"DatasetDict({\n    train: Dataset({\n        features: ['text', 'label'],\n        num_rows: 78712\n    })\n    test: Dataset({\n        features: ['text', 'label'],\n        num_rows: 19678\n    })\n})"},"metadata":{}}],"execution_count":29},{"id":"88c3989e","cell_type":"code","source":"#explore dataset\ntext.set_format(\"pd\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:30:56.931698Z","iopub.execute_input":"2024-11-29T12:30:56.932358Z","iopub.status.idle":"2024-11-29T12:30:56.942245Z","shell.execute_reply.started":"2024-11-29T12:30:56.932306Z","shell.execute_reply":"2024-11-29T12:30:56.941409Z"}},"outputs":[],"execution_count":30},{"id":"5c2c3422","cell_type":"code","source":"df = text['train'][:]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:30:56.943378Z","iopub.execute_input":"2024-11-29T12:30:56.943913Z","iopub.status.idle":"2024-11-29T12:30:57.515986Z","shell.execute_reply.started":"2024-11-29T12:30:56.943875Z","shell.execute_reply":"2024-11-29T12:30:57.515307Z"}},"outputs":[],"execution_count":31},{"id":"6372e654","cell_type":"code","source":"df.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:30:57.517245Z","iopub.execute_input":"2024-11-29T12:30:57.517594Z","iopub.status.idle":"2024-11-29T12:30:57.525809Z","shell.execute_reply.started":"2024-11-29T12:30:57.517548Z","shell.execute_reply":"2024-11-29T12:30:57.524965Z"}},"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"                                                text  label\n0  Dear principal,\\n\\nI think it's not fair to st...      0\n1  Driverless cars are quite a new invention. The...      0\n2  The new technology called Facial Action Coding...      0\n3  Gun control is a really important issue that a...      1\n4  Venus IA indeed a very powerful planet that ca...      0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>text</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Dear principal,\\n\\nI think it's not fair to st...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Driverless cars are quite a new invention. The...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>The new technology called Facial Action Coding...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Gun control is a really important issue that a...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Venus IA indeed a very powerful planet that ca...</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":32},{"id":"b904cefb","cell_type":"code","source":"plt.figure(figsize=(5,5))\nsns.set_style('white')\nsns.countplot(x='label',data=df,palette='viridis')\nplt.title('Lable Count')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:30:57.526749Z","iopub.execute_input":"2024-11-29T12:30:57.526985Z","iopub.status.idle":"2024-11-29T12:30:57.760118Z","shell.execute_reply.started":"2024-11-29T12:30:57.526960Z","shell.execute_reply":"2024-11-29T12:30:57.759318Z"}},"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Lable Count')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x500 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":33},{"id":"7dc86918","cell_type":"code","source":"df['wrd_len'] = df['text'].str.split().apply(len)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:30:57.761173Z","iopub.execute_input":"2024-11-29T12:30:57.761425Z","iopub.status.idle":"2024-11-29T12:31:01.204836Z","shell.execute_reply.started":"2024-11-29T12:30:57.761401Z","shell.execute_reply":"2024-11-29T12:31:01.203771Z"}},"outputs":[],"execution_count":34},{"id":"ee1f9185","cell_type":"code","source":"df.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:31:01.206072Z","iopub.execute_input":"2024-11-29T12:31:01.206462Z","iopub.status.idle":"2024-11-29T12:31:01.227879Z","shell.execute_reply.started":"2024-11-29T12:31:01.206413Z","shell.execute_reply":"2024-11-29T12:31:01.227021Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 78712 entries, 0 to 78711\nData columns (total 3 columns):\n #   Column   Non-Null Count  Dtype \n---  ------   --------------  ----- \n 0   text     78712 non-null  object\n 1   label    78712 non-null  int64 \n 2   wrd_len  78712 non-null  int64 \ndtypes: int64(2), object(1)\nmemory usage: 1.8+ MB\n","output_type":"stream"}],"execution_count":35},{"id":"dadcf2b2","cell_type":"code","source":"#load tokenizer and model\nfrom transformers import AutoTokenizer\nmodel_path = 'distilbert-base-uncased'\ntokenizer = AutoTokenizer.from_pretrained(model_path)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:31:01.229103Z","iopub.execute_input":"2024-11-29T12:31:01.229515Z","iopub.status.idle":"2024-11-29T12:31:01.383726Z","shell.execute_reply.started":"2024-11-29T12:31:01.229487Z","shell.execute_reply":"2024-11-29T12:31:01.383101Z"}},"outputs":[],"execution_count":36},{"id":"48bc330a","cell_type":"code","source":"out = tokenizer('this is sample text')\nprint(tokenizer.convert_ids_to_tokens(out.input_ids))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:31:01.387745Z","iopub.execute_input":"2024-11-29T12:31:01.388000Z","iopub.status.idle":"2024-11-29T12:31:01.392832Z","shell.execute_reply.started":"2024-11-29T12:31:01.387974Z","shell.execute_reply":"2024-11-29T12:31:01.392000Z"}},"outputs":[{"name":"stdout","text":"['[CLS]', 'this', 'is', 'sample', 'text', '[SEP]']\n","output_type":"stream"}],"execution_count":37},{"id":"1c72d842","cell_type":"markdown","source":"### tokenize dataset","metadata":{}},{"id":"927569b9","cell_type":"markdown","source":"### Reset format","metadata":{}},{"id":"7e4df06d","cell_type":"code","source":"text.reset_format()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:31:01.393798Z","iopub.execute_input":"2024-11-29T12:31:01.394036Z","iopub.status.idle":"2024-11-29T12:31:01.403311Z","shell.execute_reply.started":"2024-11-29T12:31:01.394011Z","shell.execute_reply":"2024-11-29T12:31:01.402592Z"}},"outputs":[],"execution_count":38},{"id":"bcdce76f","cell_type":"code","source":"def apply_tokenizer(batch):\n    return tokenizer(batch['text'],padding=True,truncation=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:31:01.404353Z","iopub.execute_input":"2024-11-29T12:31:01.404796Z","iopub.status.idle":"2024-11-29T12:31:01.414559Z","shell.execute_reply.started":"2024-11-29T12:31:01.404770Z","shell.execute_reply":"2024-11-29T12:31:01.413841Z"}},"outputs":[],"execution_count":39},{"id":"9354f65d","cell_type":"code","source":"text = text.map(apply_tokenizer,batch_size=None,batched=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:31:01.415486Z","iopub.execute_input":"2024-11-29T12:31:01.415730Z","iopub.status.idle":"2024-11-29T12:32:10.954015Z","shell.execute_reply.started":"2024-11-29T12:31:01.415707Z","shell.execute_reply":"2024-11-29T12:32:10.953132Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Map:   0%|          | 0/78712 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"389f1832423c453ba449239bd2dee40b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Map:   0%|          | 0/19678 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6ccfda0a909246159e3a014f0178a3b6"}},"metadata":{}}],"execution_count":40},{"id":"1b218d28","cell_type":"markdown","source":"### set to pytorch format","metadata":{}},{"id":"6789cc15","cell_type":"code","source":"text.set_format(\"pt\",columns=['input_ids','attention_mask','label'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:32:10.955256Z","iopub.execute_input":"2024-11-29T12:32:10.955531Z","iopub.status.idle":"2024-11-29T12:32:10.961238Z","shell.execute_reply.started":"2024-11-29T12:32:10.955505Z","shell.execute_reply":"2024-11-29T12:32:10.960267Z"}},"outputs":[],"execution_count":41},{"id":"fdbbf85a","cell_type":"code","source":"text['train'][0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:32:10.962254Z","iopub.execute_input":"2024-11-29T12:32:10.962506Z","iopub.status.idle":"2024-11-29T12:32:11.245936Z","shell.execute_reply.started":"2024-11-29T12:32:10.962482Z","shell.execute_reply":"2024-11-29T12:32:11.245137Z"}},"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"{'label': tensor(0),\n 'input_ids': tensor([  101,  6203,  4054,  1010,  1045,  2228,  2009,  1005,  1055,  2025,\n          4189,  2000,  2493,  2008,  2123,  1005,  1056,  2215,  2000,  2079,\n          2451,  2326,  2323,  2022,  3140,  2205,  1012,  2009,  2323,  2022,\n          1037,  3601,  2025,  2242,  2057,  2031,  2000,  2079,  1012,  2070,\n          2493,  2066,  5094,  2041,  1998,  2108,  2785,  2500,  2079,  2025,\n          2215,  2000,  5949,  2037,  2051,  5094,  2030,  2022,  3625,  2008,\n          2003,  2006,  2068,  2025,  2006,  2017,  1012,  2009,  2052,  2022,\n          2367,  2065,  1996,  2493,  2020,  2437,  1037,  6752,  1998, 11669,\n          2075,  1996,  2082,  2006,  3800,  2059,  2027,  2323,  2079,  2451,\n          2326,  1010,  2021,  2070,  4268,  2024,  5697,  2030,  2079,  2025,\n          2031,  1996,  2051,  2000,  2393,  2041,  1012,  2070,  4268,  2079,\n          2009,  2138,  2027,  2031,  2498,  2488,  2000,  2079,  1012,  1045,\n          1005,  1049,  3492,  2469,  2008,  2017,  2876,  1005,  1056,  2066,\n          2000,  2022,  3140,  2000,  2079,  2242,  2017,  2428,  2123,  1005,\n          1056,  2215,  2000,  2079,  1012,  3531,  2074,  2228,  2055,  2009,\n          1012,  2005,  1996,  4268,  2008,  2079,  2451,  2326,  2323,  2131,\n          1037,  2210,  2242,  2013,  2017,  2030,  1996,  2082,  2009,  2987,\n          1005,  1056,  2031,  2000,  2022,  2505,  6450,  2074,  1037,  4283,\n          2030,  1037, 14745,  2030,  2242,  2000,  2265,  1996,  9120,  2037,\n         16056,  1012,  2017,  2064,  3198,  2493,  2000,  2393,  2041,  2007,\n          2477,  2030,  2265,  2500,  2129,  2000,  2079,  2242,  1012,  2017,\n          2097,  2031,  2062,  2493,  2393,  2041,  2065,  2027,  2024,  2356,\n          2021,  2065,  2027,  2024,  3140,  2087,  2876,  1005,  1056,  8572,\n          2725,  2009,  1012,  3198,  2151,  3076,  2005,  2393,  2053,  3043,\n          2065,  2027,  2360,  2748,  2030,  2053,  2009,  2987,  1005,  1056,\n          3480,  2000,  3198,  2619,  2005,  2037,  2393,  1012,  2030,  2017,\n          2064,  2404,  4981,  2006,  1996,  2816,  7923,  2055,  2477,  2008,\n          2024,  2734,  2000,  2022,  3271,  2007,  1012,  2404,  2009,  3182,\n          2073,  2493,  2064,  2156,  2009,  1012,  2009,  1005,  1055,  6082,\n          2008,  2126,  2065,  2017,  2024,  2200,  5697,  2000,  3198,  2493,\n          2105,  1996,  2082,  2074,  2404,  1996,  4981,  2039,  1998,  3524,\n          2005,  2493,  2000,  2265,  2039,  2000,  2115,  2436,  2593,  4851,\n          2073,  2000,  2175,  1010,  2030,  2027,  2074,  2453,  2424,  2041,\n          2011,  3209,  1998,  2079,  2054,  2027,  2020,  2356,  2000,  2079,\n          1012,  2040,  4282,  2065,  1996,  4390, 12088,  2453,  2215,  2000,\n          2393,  2041,  1998,  2079,  2204,  1012,  1045,  2123,  1005,  1056,\n         16755,  2017,  2000,  2191,  2023,  2880,  2664,  3198,  2060,  2493,\n          2054,  2079,  2027,  2228,  2055,  2023,  1998,  3198,  2065,  2009,\n          2052,  2022,  2488,  2000,  2031,  2009,  2011,  3601,  1012,  2031,\n          2070,  5089,  3198,  2493,  1999,  1996,  2707,  1997,  2465,  2030,\n          1996,  2203,  1012,  2017,  2097,  2156,  2043,  1037,  3836,  5176,\n          1037,  3076,  2005,  2393,  1996,  3076,  7126,  2068,  2021,  2065,\n          1996,  3076,  2003,  2108,  7581,  2012,  2030,  3140,  2000,  1996,\n          3076,  2097,  2954,  2067,  1010,  2007,  2616,  1010,  1998,  7475,\n          2007,  1996,  3095,  1012,   102,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0]),\n 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n         0, 0, 0, 0, 0, 0, 0, 0])}"},"metadata":{}}],"execution_count":42},{"id":"3463d0d3","cell_type":"markdown","source":"### Load model","metadata":{}},{"id":"0de6e45f","cell_type":"code","source":"from transformers import AutoModelForSequenceClassification\nnum_labels = 2\nmodel = (AutoModelForSequenceClassification.from_pretrained(model_path,num_labels=num_labels).to(device))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:32:11.246886Z","iopub.execute_input":"2024-11-29T12:32:11.247150Z","iopub.status.idle":"2024-11-29T12:32:11.516732Z","shell.execute_reply.started":"2024-11-29T12:32:11.247109Z","shell.execute_reply":"2024-11-29T12:32:11.516102Z"}},"outputs":[],"execution_count":43},{"id":"df2baa70","cell_type":"code","source":"def compute_metrics(pred):\n    labels = pred.label_ids\n    preds = pred.predictions.argmax(-1)\n    f1 = f1_score(labels,preds)\n    acc = accuracy_score(labels,preds)\n    return {'f1':f1,'acc':acc}","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:32:11.517769Z","iopub.execute_input":"2024-11-29T12:32:11.518103Z","iopub.status.idle":"2024-11-29T12:32:11.522521Z","shell.execute_reply.started":"2024-11-29T12:32:11.518048Z","shell.execute_reply":"2024-11-29T12:32:11.521585Z"}},"outputs":[],"execution_count":44},{"id":"ba499261","cell_type":"markdown","source":"### training hypyer params","metadata":{}},{"id":"b345343d","cell_type":"code","source":"from transformers import TrainingArguments\nmodel_name = 'RoBERTa_AI_text_detection'\nbatch_size = 32\nlearning_rate = 2e-6\nloggin_steps = len(text['train'])//batch_size\nweigth_decay = 0.025\ntraining_args = TrainingArguments(output_dir=model_name,\n                                 per_device_eval_batch_size=batch_size,\n                                 per_device_train_batch_size=batch_size,\n                                 logging_steps=loggin_steps,\n                                 log_level=\"error\",\n                                 evaluation_strategy=\"epoch\",\n                                 learning_rate=learning_rate,\n                                 num_train_epochs=2,\n                                 weight_decay=weigth_decay,\n                                 disable_tqdm=False,\n                                 push_to_hub=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:32:11.523573Z","iopub.execute_input":"2024-11-29T12:32:11.523839Z","iopub.status.idle":"2024-11-29T12:32:11.557118Z","shell.execute_reply.started":"2024-11-29T12:32:11.523794Z","shell.execute_reply":"2024-11-29T12:32:11.556323Z"}},"outputs":[],"execution_count":45},{"id":"ef42fa2d","cell_type":"markdown","source":"### trainer","metadata":{}},{"id":"3a773761","cell_type":"code","source":"from transformers import Trainer","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:32:11.558067Z","iopub.execute_input":"2024-11-29T12:32:11.558339Z","iopub.status.idle":"2024-11-29T12:32:11.562250Z","shell.execute_reply.started":"2024-11-29T12:32:11.558298Z","shell.execute_reply":"2024-11-29T12:32:11.561389Z"}},"outputs":[],"execution_count":46},{"id":"786d6110","cell_type":"code","source":"trainer = Trainer(model=model,\n                 args = training_args,\n                 compute_metrics=compute_metrics,\n                 train_dataset=text['train'],\n                 eval_dataset=text['test'],\n                 tokenizer = tokenizer)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:32:11.563194Z","iopub.execute_input":"2024-11-29T12:32:11.563485Z","iopub.status.idle":"2024-11-29T12:32:11.684637Z","shell.execute_reply.started":"2024-11-29T12:32:11.563461Z","shell.execute_reply":"2024-11-29T12:32:11.684028Z"}},"outputs":[],"execution_count":47},{"id":"338be658","cell_type":"code","source":"trainer.train()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T12:32:11.685560Z","iopub.execute_input":"2024-11-29T12:32:11.685810Z","iopub.status.idle":"2024-11-29T13:52:05.543803Z","shell.execute_reply.started":"2024-11-29T12:32:11.685786Z","shell.execute_reply":"2024-11-29T13:52:05.543204Z"}},"outputs":[{"name":"stderr","text":"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit:","output_type":"stream"},{"output_type":"stream","name":"stdin","text":"  ········\n"},{"name":"stderr","text":"\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.011113186133333229, max=1.0…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6fd0bdce73664e3bb48e35216f6152be"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Tracking run with wandb version 0.18.3"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Run data is saved locally in <code>/kaggle/working/wandb/run-20241129_123649-zpner0gu</code>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Syncing run <strong><a href='https://wandb.ai/adi-joshi2018-vit/huggingface/runs/zpner0gu' target=\"_blank\">RoBERTa_AI_text_detection</a></strong> to <a href='https://wandb.ai/adi-joshi2018-vit/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View project at <a href='https://wandb.ai/adi-joshi2018-vit/huggingface' target=\"_blank\">https://wandb.ai/adi-joshi2018-vit/huggingface</a>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run at <a href='https://wandb.ai/adi-joshi2018-vit/huggingface/runs/zpner0gu' target=\"_blank\">https://wandb.ai/adi-joshi2018-vit/huggingface/runs/zpner0gu</a>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n    <div>\n      \n      <progress value='4920' max='4920' style='width:300px; height:20px; vertical-align: middle;'></progress>\n      [4920/4920 1:15:11, Epoch 2/2]\n    </div>\n    <table border=\"1\" class=\"dataframe\">\n  <thead>\n <tr style=\"text-align: left;\">\n      <th>Epoch</th>\n      <th>Training Loss</th>\n      <th>Validation Loss</th>\n      <th>F1</th>\n      <th>Acc</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>1</td>\n      <td>0.092200</td>\n      <td>0.053058</td>\n      <td>0.982742</td>\n      <td>0.985059</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.029700</td>\n      <td>0.052640</td>\n      <td>0.984375</td>\n      <td>0.986482</td>\n    </tr>\n  </tbody>\n</table><p>"},"metadata":{}},{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"TrainOutput(global_step=4920, training_loss=0.060937496603038765, metrics={'train_runtime': 4792.9788, 'train_samples_per_second': 32.845, 'train_steps_per_second': 1.027, 'total_flos': 2.0853547766022144e+16, 'train_loss': 0.060937496603038765, 'epoch': 2.0})"},"metadata":{}}],"execution_count":48},{"id":"cba6e27f","cell_type":"code","source":"trainer.push_to_hub(commit_message='Done!')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T13:52:05.544796Z","iopub.execute_input":"2024-11-29T13:52:05.545060Z","iopub.status.idle":"2024-11-29T13:52:09.198052Z","shell.execute_reply.started":"2024-11-29T13:52:05.545034Z","shell.execute_reply":"2024-11-29T13:52:09.197322Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"events.out.tfevents.1732883532.d6dccfbb0153.30.0:   0%|          | 0.00/6.36k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1d492d114c044e09aa068312147a2d93"}},"metadata":{}},{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"CommitInfo(commit_url='https://huggingface.co/Sharpaxis/RoBERTa_AI_text_detection/commit/03c45c10c1f339f3935691931c3523fcb181fa24', commit_message='Done!', commit_description='', oid='03c45c10c1f339f3935691931c3523fcb181fa24', pr_url=None, repo_url=RepoUrl('https://huggingface.co/Sharpaxis/RoBERTa_AI_text_detection', endpoint='https://huggingface.co', repo_type='model', repo_id='Sharpaxis/RoBERTa_AI_text_detection'), pr_revision=None, pr_num=None)"},"metadata":{}}],"execution_count":49},{"id":"f7a5c9f4-dfd7-4d04-b1aa-8520dba0bd96","cell_type":"code","source":"# Use a pipeline as a high-level helper\nfrom transformers import pipeline\n\npipe = pipeline(\"text-classification\", model=\"Sharpaxis/RoBERTa_AI_text_detection\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T13:52:09.199119Z","iopub.execute_input":"2024-11-29T13:52:09.199376Z","iopub.status.idle":"2024-11-29T13:52:17.777226Z","shell.execute_reply.started":"2024-11-29T13:52:09.199349Z","shell.execute_reply":"2024-11-29T13:52:17.776207Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/615 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a888e8e3762a46c9ba7eb8c4d4066c05"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8de6e674535b47f88da520b733ed62f2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/1.20k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"caff207a13e84e1d97a6a9e1332c653a"}},"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":"a0a1f45189314978b33d58f87faebe15"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/712k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8a1a369c588f4b519adc2e26362160c9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7176ee1b555b4510bdb6456b2ac7e214"}},"metadata":{}}],"execution_count":50},{"id":"76308112-543e-4f15-b944-b695ad596b50","cell_type":"code","source":"pipe(\"Artificial Intelligence represents a transformative technological advancement that has significantly impacted various aspects of modern society. The implementation of machine learning algorithms and neural networks has enabled systems to process vast quantities of data and perform complex pattern recognition tasks with increasing accuracy. Common applications include recommendation systems, facial recognition technology, and natural language processing. While the integration of AI into daily life has proceeded rapidly, considerations regarding its societal implications and ethical deployment remain paramount. The continued evolution of AI capabilities suggests an increasingly automated future across multiple sectors.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T13:52:17.778749Z","iopub.execute_input":"2024-11-29T13:52:17.779165Z","iopub.status.idle":"2024-11-29T13:52:17.941194Z","shell.execute_reply.started":"2024-11-29T13:52:17.779119Z","shell.execute_reply":"2024-11-29T13:52:17.940320Z"}},"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"[{'label': 'LABEL_1', 'score': 0.9984328150749207}]"},"metadata":{}}],"execution_count":51},{"id":"68e0a96c-86ec-4664-ab51-8829f5e558c3","cell_type":"code","source":"def check_if_ai(text):\n    if type(text) != str:\n        text = str(text)\n    out = pipe(text)[0]\n    if out['label'] == 'LABEL_1':\n        print(f\"MODEL PREDICTION: \")\n        print(f\"AI GENERATED || SCORE : {out['score']:.4f}\")\n    else:\n        print(f\"MODEL PREDICTION: \")\n        print(f\"HUMAN GENERATED || SCORE : {out['score']:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T13:52:17.942325Z","iopub.execute_input":"2024-11-29T13:52:17.942634Z","iopub.status.idle":"2024-11-29T13:52:17.949132Z","shell.execute_reply.started":"2024-11-29T13:52:17.942590Z","shell.execute_reply":"2024-11-29T13:52:17.948196Z"}},"outputs":[],"execution_count":52},{"id":"12af1b41-4318-4619-ab2f-ef9ce595cd87","cell_type":"code","source":"check_if_ai(\"Artificial Intelligence represents a transformative technological advancement that has significantly impacted various aspects of modern society. The implementation of machine learning algorithms and neural networks has enabled systems to process vast quantities of data and perform complex pattern recognition tasks with increasing accuracy. Common applications include recommendation systems, facial recognition technology, and natural language processing. While the integration of AI into daily life has proceeded rapidly, considerations regarding its societal implications and ethical deployment remain paramount. The continued evolution of AI capabilities suggests an increasingly automated future across multiple sectors.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T13:52:17.950235Z","iopub.execute_input":"2024-11-29T13:52:17.950732Z","iopub.status.idle":"2024-11-29T13:52:18.022478Z","shell.execute_reply.started":"2024-11-29T13:52:17.950698Z","shell.execute_reply":"2024-11-29T13:52:18.021645Z"}},"outputs":[{"name":"stdout","text":"MODEL PREDICTION: \nAI GENERATED || SCORE : 0.9984\n","output_type":"stream"}],"execution_count":53},{"id":"59bb7f38-39e3-4881-82ff-d41ce1b50e4d","cell_type":"code","source":"check_if_ai(\"You know what's crazy about AI? The other day I was trying to explain it to my mom, and I caught myself rambling about neural networks and deep learning - totally lost her after 30 seconds. But then I thought about how we actually use AI every day. Like when Netflix somehow knows I want to watch another cooking show after binging Chef's Table, or when my phone recognizes my face even with my morning bedhead. It's everywhere, but we barely notice it anymore. Kind of wild how quickly something so sci-fi became just... normal life, right? Sometimes I wonder if we're moving too fast with all this, but then again, that's probably what people said about electricity too\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T13:52:18.023824Z","iopub.execute_input":"2024-11-29T13:52:18.024195Z","iopub.status.idle":"2024-11-29T13:52:18.108622Z","shell.execute_reply.started":"2024-11-29T13:52:18.024154Z","shell.execute_reply":"2024-11-29T13:52:18.107751Z"}},"outputs":[{"name":"stdout","text":"MODEL PREDICTION: \nAI GENERATED || SCORE : 0.9974\n","output_type":"stream"}],"execution_count":54},{"id":"cf310714-4f5e-4cc8-95f4-fb85de05b68b","cell_type":"code","source":"check_if_ai(\"\"\"Shakespeare's plays portray recognisable people in situations that we can all relate to - including love, marriage, death, mourning, guilt, the need to make difficult choices, separation, reunion and reconciliation. They do so with great humanity, tolerance, and wisdom. They help us to understand what it is to be human, and to cope with the problems of being so.  \"\"\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T13:52:18.109903Z","iopub.execute_input":"2024-11-29T13:52:18.110336Z","iopub.status.idle":"2024-11-29T13:52:18.170707Z","shell.execute_reply.started":"2024-11-29T13:52:18.110293Z","shell.execute_reply":"2024-11-29T13:52:18.169840Z"}},"outputs":[{"name":"stdout","text":"MODEL PREDICTION: \nAI GENERATED || SCORE : 0.9971\n","output_type":"stream"}],"execution_count":55},{"id":"7b5352c6-5e3f-49d4-84e0-4e5d4edb8baa","cell_type":"code","source":"y_val = trainer.predict(text['test'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T13:52:18.171852Z","iopub.execute_input":"2024-11-29T13:52:18.172214Z","iopub.status.idle":"2024-11-29T13:55:15.308671Z","shell.execute_reply.started":"2024-11-29T13:52:18.172175Z","shell.execute_reply":"2024-11-29T13:55:15.307938Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":""},"metadata":{}}],"execution_count":56},{"id":"2f4bea03-4bb5-4ec0-893c-b07ed670d8fb","cell_type":"code","source":"preds = np.argmax(y_val.predictions,axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T13:55:15.309663Z","iopub.execute_input":"2024-11-29T13:55:15.309936Z","iopub.status.idle":"2024-11-29T13:55:15.314980Z","shell.execute_reply.started":"2024-11-29T13:55:15.309909Z","shell.execute_reply":"2024-11-29T13:55:15.314016Z"}},"outputs":[],"execution_count":57},{"id":"54c03b72-97c5-4b20-ad8a-5223d357a095","cell_type":"code","source":"labels = np.array(text['test']['label'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T13:55:15.316001Z","iopub.execute_input":"2024-11-29T13:55:15.316371Z","iopub.status.idle":"2024-11-29T13:55:15.335734Z","shell.execute_reply.started":"2024-11-29T13:55:15.316340Z","shell.execute_reply":"2024-11-29T13:55:15.334712Z"}},"outputs":[],"execution_count":58},{"id":"7a9ec8b0-d229-4c7e-9d3a-935ce7a93bf6","cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(labels,preds))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-29T13:55:15.336697Z","iopub.execute_input":"2024-11-29T13:55:15.336980Z","iopub.status.idle":"2024-11-29T13:55:15.371450Z","shell.execute_reply.started":"2024-11-29T13:55:15.336956Z","shell.execute_reply":"2024-11-29T13:55:15.370682Z"}},"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       1.00      0.98      0.99     11266\n           1       0.97      1.00      0.98      8412\n\n    accuracy                           0.99     19678\n   macro avg       0.98      0.99      0.99     19678\nweighted avg       0.99      0.99      0.99     19678\n\n","output_type":"stream"}],"execution_count":59}]}