Datasets:
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import warnings
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
from tqdm import tqdm
from sklearn import metrics
import transformers
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler
from transformers import DistilBertTokenizer, DistilBertModel,AutoModel,AutoTokenizer,AutoConfig,AutoModelForSequenceClassification
import logging
logging.basicConfig(level=logging.ERROR)
import os
from itertools import permutations
from torch import cuda
device = 'cuda' if cuda.is_available() else 'cpu'
print(device)
models = ['vinai/bertweet-base',
'./hate_bert',
'Twitter/TwHIN-BERT-base',
'cardiffnlp/twitter-roberta-base',
'Xuhui/ToxDect-roberta-large',
'bert-base-cased',
'roberta-base']
model_names = [
'BERTweet',
'HateBERT',
'TwHIN-BERT',
'Twitter-RoBERTa',
'ToxDect-RoBERTa',
'BERT',
'RoBERTa'
]
countries = ['United States','Australia','United Kingdom','South Africa','Singapore']
codes = ['US', 'AU', 'GB', 'ZA', 'SG']
_hate_cols = [f'{country.replace(" ","_")}_Hate' for country in countries]
def hamming_score(y_true, y_pred, normalize=True, sample_weight=None):
acc_list = []
for i in range(y_true.shape[0]):
set_true = set( np.where(y_true[i])[0] )
set_pred = set( np.where(y_pred[i])[0] )
tmp_a = None
if len(set_true) == 0 and len(set_pred) == 0:
tmp_a = 1
else:
tmp_a = len(set_true.intersection(set_pred))/\
float( len(set_true.union(set_pred)) )
acc_list.append(tmp_a)
return np.mean(acc_list)
class MultiLabelDataset(Dataset):
def __init__(self, dataframe, tokenizer, max_len):
self.tokenizer = tokenizer
self.data = dataframe
self.text = dataframe.text
self.targets = self.data.labels
self.max_len = max_len
def __len__(self):
return len(self.text)
def __getitem__(self, index):
text = str(self.text[index])
inputs = self.tokenizer.encode_plus(
text,
None,
truncation=True,
add_special_tokens=True,
max_length=self.max_len,
pad_to_max_length=True,
return_token_type_ids=True
)
ids = inputs['input_ids']
mask = inputs['attention_mask']
token_type_ids = inputs["token_type_ids"]
return {
'ids': torch.tensor(ids, dtype=torch.long),
'mask': torch.tensor(mask, dtype=torch.long),
'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),
'targets': torch.tensor(self.targets[index], dtype=torch.float)
}
class Classifier(torch.nn.Module):
def __init__(self,model_name,tokenizer):
super(Classifier, self).__init__()
self.l1 = AutoModel.from_pretrained(model_name)
self.l1.resize_token_embeddings(len(tokenizer))
config = AutoConfig.from_pretrained(model_name)
self.pre_classifier = torch.nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = torch.nn.Dropout(0.1)
self.classifier = torch.nn.Linear(config.hidden_size, 5)
def forward(self, input_ids, attention_mask, token_type_ids):
output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask)
hidden_state = output_1[0]
pooler = hidden_state[:, 0]
pooler = self.pre_classifier(pooler)
pooler = torch.nn.Tanh()(pooler)
pooler = self.dropout(pooler)
output = self.classifier(pooler)
return output
def loss_fn(outputs, targets):
return torch.nn.BCEWithLogitsLoss()(outputs, targets)
def train(epoch,model,training_loader):
model.train()
loop = tqdm(enumerate(training_loader, 0),total=len(training_loader))
loop.set_description(f"Epoch {epoch}")
for _,data in loop:
ids = data['ids'].to(device, dtype = torch.long)
mask = data['mask'].to(device, dtype = torch.long)
token_type_ids = data['token_type_ids'].to(device, dtype = torch.long)
targets = data['targets'].to(device, dtype = torch.float)
outputs = model(input_ids=ids, attention_mask=mask, labels=targets)
optimizer.zero_grad()
loss = outputs.loss
loop.set_postfix(loss=loss.mean().item())
loss.mean().backward()
optimizer.step()
def validation(testing_loader,model):
model.eval()
fin_targets=[]
fin_outputs=[]
with torch.no_grad():
for _, data in tqdm(enumerate(testing_loader, 0),total=len(testing_loader)):
ids = data['ids'].to(device, dtype = torch.long)
mask = data['mask'].to(device, dtype = torch.long)
token_type_ids = data['token_type_ids'].to(device, dtype = torch.long)
targets = data['targets'].to(device, dtype = torch.float)
outputs = model(input_ids=ids, attention_mask=mask, ).logits
fin_targets.extend(targets.cpu().detach().numpy().tolist())
fin_outputs.extend(torch.sigmoid(outputs).cpu().detach().numpy().tolist())
return fin_outputs, fin_targets
MAX_LEN = 128
TRAIN_BATCH_SIZE = 32
VALID_BATCH_SIZE = 32
EPOCHS = 6
LEARNING_RATE = 2e-5
special_tokens = ["[US]","[AU]","[GB]","[ZA]","[SG]","@USER","URL"]
col_idx_permutation = list(permutations(range(5)))
for model_path,model_name in zip(models[1:],model_names[1:]):
tokenizer = AutoTokenizer.from_pretrained(model_path, truncation=True)
tokenizer.add_tokens(special_tokens)
res_df = pd.DataFrame()
train_file = './data_splits/CREHate_train.csv'
valid_file = './data_splits/CREHate_valid.csv'
test_file = './data_splits/CREHate_test.csv'
train_data = pd.read_csv(train_file)
valid_data = pd.read_csv(valid_file)
test_data = pd.read_csv(test_file)
for idx,idx_permute in enumerate(col_idx_permutation):
hate_cols = [_hate_cols[i] for i in idx_permute]
train_df = pd.DataFrame()
train_df['text'] = train_data['Text']
train_df['labels'] = train_data[hate_cols].values.tolist()
valid_df = pd.DataFrame()
valid_df['text'] = valid_data['Text']
valid_df['labels'] = valid_data[hate_cols].values.tolist()
test_df = pd.DataFrame()
test_df['text'] = test_data['Text']
test_df['labels'] = test_data[hate_cols].values.tolist()
training_set = MultiLabelDataset(train_df, tokenizer, MAX_LEN)
valid_set = MultiLabelDataset(valid_df, tokenizer, MAX_LEN)
testing_set = MultiLabelDataset(test_df, tokenizer, MAX_LEN)
train_params = {'batch_size': TRAIN_BATCH_SIZE,
'shuffle': True,
'num_workers': torch.cuda.device_count()
}
valid_params = {'batch_size': VALID_BATCH_SIZE,
'shuffle': True,
'num_workers': torch.cuda.device_count()
}
test_params = {'batch_size': VALID_BATCH_SIZE,
'shuffle': False,
'num_workers': torch.cuda.device_count()
}
training_loader = DataLoader(training_set, **train_params)
valid_loader = DataLoader(valid_set, **valid_params)
testing_loader = DataLoader(testing_set, **test_params)
model = AutoModelForSequenceClassification.from_pretrained(model_path,
problem_type="multi_label_classification",
num_labels=5, ignore_mismatched_sizes=True)
model.resize_token_embeddings(len(tokenizer))
print(list(range(torch.cuda.device_count())))
model = nn.DataParallel(model, device_ids = list(range(torch.cuda.device_count()))).to(device)
optimizer = torch.optim.AdamW(params = model.parameters(), lr=LEARNING_RATE, eps=1e-8)
min_hamming_loss = 1
best_model = None
for epoch in range(EPOCHS):
train(epoch,model,training_loader)
outputs, targets = validation(valid_loader,model)
final_outputs = np.array(outputs) >=0.5
val_hamming_loss = metrics.hamming_loss(targets, final_outputs)
val_hamming_score = hamming_score(np.array(targets), np.array(final_outputs))
print(f"Hamming Score = {val_hamming_score}")
print(f"Hamming Loss = {val_hamming_loss}")
if val_hamming_loss < min_hamming_loss:
min_hamming_loss = val_hamming_loss
best_model = model
if best_model is not None:
outputs, targets = validation(testing_loader,best_model)
final_outputs = np.array(outputs) >=0.5
tst_hamming_loss = metrics.hamming_loss(targets, final_outputs)
tst_hamming_score = hamming_score(np.array(targets), np.array(final_outputs))
final_outputs = 1*final_outputs
cols = [f'{model_name}-ML-{country}' for country in [codes[i] for i in idx_permute]]
outputs_df = pd.DataFrame(final_outputs,columns=cols)
total = pd.concat([test_data[hate_cols],outputs_df],axis=1)
total.to_csv(f'./res/{model_name}-ML-ALL-P-{idx}-res.csv',index=False)
test_data = pd.concat([test_data,outputs_df],axis=1)
test_data.to_csv(test_file,index=False)
print(test_data)
print(total)
print('\tAcc\tF1\tH-F1\tN-F1')
row = []
for hate_col,code in zip(hate_cols,[codes[i] for i in idx_permute]):
acc = metrics.accuracy_score(test_data[hate_col],outputs_df[f'{model_name}-ML-{code}'])
f1 = metrics.f1_score(test_data[hate_col], outputs_df[f'{model_name}-ML-{code}'],average='macro')
n,h = metrics.f1_score(test_data[hate_col], outputs_df[f'{model_name}-ML-{code}'],average=None)
r = metrics.recall_score(test_data[hate_col], outputs_df[f'{model_name}-ML-{code}'])
print(f'{code}:\t{acc:.4f}\t{f1:.4f}\t{n:.4f}\t{h:.4f}\t{r:.4f}')
row += [acc,f1,n,h,r]
res_cols = []
for code in [codes[i] for i in idx_permute]:
res_cols += [f'{code}-{score}' for score in ['acc','f1','h','n','r']]
res_df_row = pd.DataFrame([row],index=[idx],columns=res_cols)
res_df = pd.concat([res_df,res_df_row])
if 'avg' in res_df.index:
res_df.drop('avg',inplace=True)
res_df.loc['avg'] = res_df.mean(axis=0)
print(res_df)
res_df.to_csv(f'./res/{model_name}-ML-ALL-P-res-scores.csv')
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