# -*- coding: utf-8 -*- """After model-fitting Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/#fileId=https%3A//storage.googleapis.com/kaggle-colab-exported-notebooks/after-model-fitting-b220d687-d8e5-4eb5-aafd-6a7e94d72073.ipynb%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com/20240128/auto/storage/goog4_request%26X-Goog-Date%3D20240128T102031Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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 """ # IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES # TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK, # THEN FEEL FREE TO DELETE THIS CELL. # NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON # ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR # NOTEBOOK. import os import sys from tempfile import NamedTemporaryFile from urllib.request import urlopen from urllib.parse import unquote, urlparse from urllib.error import HTTPError from zipfile import ZipFile import tarfile import shutil CHUNK_SIZE = 40960 DATA_SOURCE_MAPPING = 'llm-detect-ai-generated-text:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-competitions-data%2Fkaggle-v2%2F61542%2F7516023%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240128%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240128T102030Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%3D038d55997cf8a860737caadb5837a5ebfaaf8477d4523afa1008387fe39c3a0c58c1ddc811284f559dbb78fd8e0f8230fca333e828951b69e5d935955b9163461cbd2f4f8b3f321dd0e73d767e2ef1a8ceb52512ef8f8d99fd19c92abf23c5a856ebd3d9ed4ee28b4c31b83427a7dc10052602e6d604e2c55f51d8e26da1e2dacb2e720476c3b874b22d5a03e8dde81374f227c87a024dea36e5973a7cabcccdcec804ba2fd73b5397d7d334be750de7ea9d4a2c2dcb12b93f4d75c18f063ebf02ff802e8912122dbd5b25695e7658bffc61997b9893958b304068a6e593653b14959b5355f4b8bb09d5d01768dda2839e271941fabfddf3cc5d8cbc5cd06746,argugpt:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F3946973%2F6867914%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240128%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240128T102030Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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,daigt-proper-train-dataset:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F3942644%2F6890527%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240128%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240128T102031Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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' KAGGLE_INPUT_PATH='/kaggle/input' KAGGLE_WORKING_PATH='/kaggle/working' KAGGLE_SYMLINK='kaggle' !umount /kaggle/input/ 2> /dev/null shutil.rmtree('/kaggle/input', ignore_errors=True) os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True) os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True) try: os.symlink(KAGGLE_INPUT_PATH, os.path.join("..", 'input'), target_is_directory=True) except FileExistsError: pass try: os.symlink(KAGGLE_WORKING_PATH, os.path.join("..", 'working'), target_is_directory=True) except FileExistsError: pass for data_source_mapping in DATA_SOURCE_MAPPING.split(','): directory, download_url_encoded = data_source_mapping.split(':') download_url = unquote(download_url_encoded) filename = urlparse(download_url).path destination_path = os.path.join(KAGGLE_INPUT_PATH, directory) try: with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile: total_length = fileres.headers['content-length'] print(f'Downloading {directory}, {total_length} bytes compressed') dl = 0 data = fileres.read(CHUNK_SIZE) while len(data) > 0: dl += len(data) tfile.write(data) done = int(50 * dl / int(total_length)) sys.stdout.write(f"\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded") sys.stdout.flush() data = fileres.read(CHUNK_SIZE) if filename.endswith('.zip'): with ZipFile(tfile) as zfile: zfile.extractall(destination_path) else: with tarfile.open(tfile.name) as tarfile: tarfile.extractall(destination_path) print(f'\nDownloaded and uncompressed: {directory}') except HTTPError as e: print(f'Failed to load (likely expired) {download_url} to path {destination_path}') continue except OSError as e: print(f'Failed to load {download_url} to path {destination_path}') continue print('Data source import complete.') # This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the read-only "../input/" directory # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename)) # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session !git clone https://huggingface.co/spaces/kaitehtzeng/primary_app """## Import Necessary Library""" import torch.nn.functional as F from transformers import AutoModel from transformers import AutoTokenizer from tokenizers import Tokenizer, trainers, pre_tokenizers, models from transformers import DebertaTokenizer from sklearn.model_selection import train_test_split import torch import torch.nn as nn import numpy as np import pandas as pd from tqdm.notebook import tqdm import matplotlib.pyplot as plt import nltk from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from nltk.tokenize.treebank import TreebankWordDetokenizer from collections import Counter #import spacy import re import gc # ---------- import os config = { 'model': '/kaggle/input/transformers-model-downloader-pytorch-tf2-0/microsoft/deberta-v3-base', 'dropout': 0.2, 'max_length': 512, 'batch_size':3, 'epochs': 1, 'lr': 1e-5, 'device': 'cuda' if torch.cuda.is_available() else 'cpu', 'scheduler': 'CosineAnnealingWarmRestarts' } """### Preparation Comparing two essays.
One predicted written by students, one predicted written by LLM """ train_essays = pd.read_csv("/kaggle/input/llm-detect-ai-generated-text/train_essays.csv") external = pd.read_csv("/kaggle/input/daigt-proper-train-dataset/train_drcat_04.csv") df = pd.concat([ external[external.source=="persuade_corpus"].sample(10000,random_state=101), external[external.source!='persuade_corpus'] ]) df = df.reset_index() df['stratify'] = df.label.astype(str)+df.source.astype(str) train_df,val_df = train_test_split(df,test_size=0.2,random_state = 101,stratify=df['stratify']) train_df, val_df = train_df.reset_index(), val_df.reset_index() import transformers print('transformers version:', transformers.__version__) #train_df,val_df = train_test_split(train_essays,test_size=0.2,random_state = 101) #train_df, val_df = train_df.reset_index(), val_df.reset_index() #print('dataframe shapes:',train_df.shape, val_df.shape) tokenizer = AutoTokenizer.from_pretrained(config['model']) tokenizer.train_new_from_iterator(train_essays['text'], 52000) """### Building Training Dataset and Loader""" class EssayDataset: def __init__(self, df, config,tokenizer, is_test = False): self.df = df self.tokenizer = tokenizer self.is_test = is_test self.config = config def token_start(self, idx): sample_text = self.df.loc[idx,'text'] tokenized = tokenizer.encode_plus(sample_text, None, add_special_tokens=True, max_length= self.config['max_length'], truncation=True, padding="max_length" ) inputs = { "input_ids": torch.tensor(tokenized['input_ids'],dtype=torch.long), "token_type_ids": torch.tensor(tokenized['token_type_ids'],dtype=torch.long), "attention_mask": torch.tensor(tokenized['attention_mask'],dtype = torch.long) } return inputs def __getitem__(self,idx): input_text = self.token_start(idx) if self.is_test: return input_text else: labels = self.df.loc[idx,'label'] targets = {'labels' : torch.tensor(labels,dtype = torch.float32)} return input_text,targets def __len__(self): return len(self.df) eval_ds = EssayDataset(val_df,config,tokenizer = tokenizer,is_test=True) eval_loader = torch.utils.data.DataLoader(eval_ds, batch_size= config['batch_size']) """Build the Model""" class mymodel(nn.Module): def __init__(self,config): super(mymodel,self).__init__() self.model_name = config['model'] self.deberta = AutoModel.from_pretrained(self.model_name) #12801 = len(tokenizer) self.deberta.resize_token_embeddings(128001) self.dropout = nn.Dropout(config['dropout']) self.fn0 = nn.Linear(self.deberta.config.hidden_size,256) self.fn2 = nn.Linear(256,1) self.pooling = MeanPooling() def forward(self, input): output = self.deberta(**input,return_dict = True) output = self.pooling(output['last_hidden_state'],input['attention_mask']) output = self.dropout(output) output = self.fn0(output) output = self.dropout(output) output = self.fn2(output) output = torch.sigmoid(output) return output import torch.nn as nn class MeanPooling(nn.Module): def __init__(self): super(MeanPooling,self).__init__() def forward(self,last_hidden_state, attention_mask): new_weight = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float() final = torch.sum(new_weight*last_hidden_state,1) total_weight = new_weight.sum(1) total_weight = torch.clamp(total_weight, min = 1e-9) mean_embedding = final/total_weight return mean_embedding model = mymodel(config).to(device=config['device']) model.load_state_dict(torch.load('/kaggle/input/fine-tune-model/my_model.pth')) model.eval() #preds = [] #for (inputs) in eval_loader: # inputs = {k:inputs[k].to(device=config['device']) for k in inputs.keys()} # # outputs = model(inputs) # preds.append(outputs.detach().cpu()) #preds = torch.concat(preds) #val_df['preds'] = preds.numpy() #val_df['AI'] = val_df['preds']>0.5 #sample_predict_AI = val_df.loc[val_df['AI'] == True].iloc[0]['text'] #sample_predict_student = val_df.loc[val_df['AI'] == False].iloc[0]['text'] #sample_predict_AI #sample_predict_student def trial(text): tokenized = tokenizer.encode_plus(text, None, add_special_tokens=True, max_length= config['max_length'], truncation=True, padding="max_length" ) inputs = { "input_ids": torch.tensor(tokenized['input_ids'],dtype=torch.long), "token_type_ids": torch.tensor(tokenized['token_type_ids'],dtype=torch.long), "attention_mask": torch.tensor(tokenized['attention_mask'],dtype = torch.long) } inputs = {k:inputs[k].unsqueeze(0).to(device=config['device']) for k in inputs.keys()} if model(inputs).item()>=0.5: return "AI" else: return "Student" !pip install -q gradio==3.45.0 import gradio as gr trial('hello fuck you') demo = gr.Interface( fn=trial, inputs=gr.Textbox(placeholder="..."), outputs="textbox" ) demo.launch(share=True)