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# -*- coding: utf-8 -*-
"""AiProjectTest.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1E4AHYbuRi_FbOMhQntdAMMZMY14hWh2e
"""
from pathlib import Path
from sklearn.model_selection import train_test_split
import torch
from torch.utils.data import Dataset
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
from transformers import Trainer, TrainingArguments
from torch.utils.data import DataLoader
from transformers import AdamW
import pandas as pd
df_train = pd.read_csv('train.csv')
df_test = pd.read_csv('test.csv')
df_test_labels = pd.read_csv('test_labels.csv')
model_name = "distilbert-base-uncased"
def read_file(f):
texts = f['comment_text'].tolist()
labels = []
for i in range(len(f)):
temp = []
temp.append(f['toxic'][i])
temp.append(f['severe_toxic'][i])
temp.append(f['obscene'][i])
temp.append(f['threat'][i])
temp.append(f['insult'][i])
temp.append(f['identity_hate'][i])
labels.append(temp)
return texts, labels
train_texts, train_labels = read_file(df_train)
test_texts = df_test['comment_text'].tolist()
test_labels = []
for i in range(len(df_test_labels)):
temp = []
temp.append(df_test_labels['toxic'][i])
temp.append(df_test_labels['severe_toxic'][i])
temp.append(df_test_labels['obscene'][i])
temp.append(df_test_labels['threat'][i])
temp.append(df_test_labels['insult'][i])
temp.append(df_test_labels['identity_hate'][i])
test_labels.append(temp)
train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)
tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
ind = 0
train_encodings = {'input_ids': [], 'attention_mask': []}
for i in range(len(train_texts)//16):
temp = tokenizer(train_texts[ind:ind+16], truncation=True, padding=True)
train_encodings['input_ids'] += temp['input_ids']
train_encodings['attention_mask'] += temp['attention_mask']
ind += 16
ind = 0
val_encodings = {'input_ids': [], 'attention_mask': []}
for i in range(len(val_texts)//16):
temp = tokenizer(val_texts[ind:ind+16], truncation=True, padding=True)
val_encodings['input_ids'] += temp['input_ids']
val_encodings['attention_mask'] += temp['attention_mask']
ind += 16
ind = 0
test_encodings = {'input_ids': [], 'attention_mask': []}
for i in range(len(test_texts)//16):
temp = tokenizer(test_texts[ind:ind+16], truncation=True, padding=True)
test_encodings['input_ids'] += temp['input_ids']
test_encodings['attention_mask'] += temp['attention_mask']
ind += 16
while True:
if len(train_labels) > len(train_encodings):
train_labels.pop()
else:
break
while True:
if len(val_labels) > len(val_encodings):
val_labels.pop()
else:
break
while True:
if len(test_labels) > len(test_encodings):
test_labels.pop()
else:
break
class dataset(Dataset):
def __init__(self, encodings, labels):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
item['labels'] = torch.tensor(self.labels[idx])
return item
def __len__(self):
return(len(self.labels))
train_dataset_list = [[], [], [], [], [], []]
for i in train_labels:
for j in range(6):
train_dataset_list[j].append(i[j])
val_dataset_list = [[], [], [], [], [], []]
for i in val_labels:
for j in range(6):
val_dataset_list[j].append(i[j])
train_dataset_0 = dataset(train_encodings, train_dataset_list[0])
train_dataset_1 = dataset(train_encodings, train_dataset_list[1])
train_dataset_2 = dataset(train_encodings, train_dataset_list[2])
train_dataset_3 = dataset(train_encodings, train_dataset_list[3])
train_dataset_4 = dataset(train_encodings, train_dataset_list[4])
train_dataset_5 = dataset(train_encodings, train_dataset_list[5])
val_dataset_0 = dataset(val_encodings, val_dataset_list[0])
val_dataset_1 = dataset(val_encodings, val_dataset_list[1])
val_dataset_2 = dataset(val_encodings, val_dataset_list[2])
val_dataset_3 = dataset(val_encodings, val_dataset_list[3])
val_dataset_4 = dataset(val_encodings, val_dataset_list[4])
val_dataset_5 = dataset(val_encodings, val_dataset_list[5])
training_args = TrainingArguments(output_dir='./results',
num_train_epochs=2,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
warmup_steps=500, learning_rate=5e-5,
weight_decay=.01, logging_dir='./logs',
logging_steps=10)
model = DistilBertForSequenceClassification.from_pretrained(model_name)
trainer_0 = Trainer(model=model, args=training_args, train_dataset=train_dataset_0, eval_dataset=val_dataset_0)
trainer_0.train()
trainer_1 = Trainer(model=model, args=training_args, train_dataset=train_dataset_1, eval_dataset=val_dataset_1)
trainer_1.train()
trainer_2 = Trainer(model=model, args=training_args, train_dataset=train_dataset_2, eval_dataset=val_dataset_2)
trainer_2.train()
trainer_3 = Trainer(model=model, args=training_args, train_dataset=train_dataset_3, eval_dataset=val_dataset_3)
trainer_3.train()
trainer_4 = Trainer(model=model, args=training_args, train_dataset=train_dataset_4, eval_dataset=val_dataset_4)
trainer_4.train()
trainer_5 = Trainer(model=model, args=training_args, train_dataset=train_dataset_5, eval_dataset=val_dataset_5)
trainer_5.train()
# train_dataset = dataset(train_encodings, train_labels)
# val_dataset = dataset(val_encodings, val_labels)
# test_dataset = dataset(test_encodings, test_labels)
# -----------------------------------------------------------------
# test_dataset_list = [[], [], [], [], [], []]
# for i in test_labels:
# for j in range(6):
# test_dataset_list[j].append(i[j])
# -----------------------------------------------------------------
# val_dataset = dataset(val_encodings, val_labels)
# test_dataset_0 = dataset(test_encodings, test_dataset_list[0])
# test_dataset_1 = dataset(test_encodings, test_dataset_list[1])
# test_dataset_2 = dataset(test_encodings, test_dataset_list[2])
# test_dataset_3 = dataset(test_encodings, test_dataset_list[3])
# test_dataset_4 = dataset(test_encodings, test_dataset_list[4])
# test_dataset_5 = dataset(test_encodings, test_dataset_list[5])
# -----------------------------------------------------------------
# device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
# model.to(device)
# model.train()
# train_loader = DataLoader(train_dataset_0, batch_size=16, shuffle=True)
# optim = AdamW(model.parameters(), lr=5e-5)
# num_train_epochs = 2
# for epoch in range(num_train_epochs):
# for batch in train_loader:
# optim.zero_grad()
# input_ids = batch['input_ids'].to(device)
# attention_mask = batch['attention_mask'].to(device)
# labels = batch['labels'].to(device)
# outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
# loss = outputs[0]
# loss.backward()
# optim.step()
# model.eval() |