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import torch
import numpy as np
from bartpho.preprocess import tokenize, normalize
tag_dict = {
"RESTAURANT#GENERAL": "chung về nhà_hàng",
"RESTAURANT#PRICES": "giá của nhà_hàng",
"RESTAURANT#MISCELLANEOUS": "tổng_quát về nhà_hàng",
"FOOD#PRICES": "giá đồ ăn",
"FOOD#QUALITY": "chất_lượng đồ ăn",
"FOOD#STYLE&OPTIONS": "phong_cách và lựa_chọn đồ ăn",
"DRINKS#PRICES": "giá đồ uống",
"DRINKS#QUALITY": "chất_lượng đồ uống",
"DRINKS#STYLE&OPTIONS": "phong_cách và lựa_chọn đồ uống",
"AMBIENCE#GENERAL": "bầu không_khí",
"SERVICE#GENERAL": "dịch_vụ",
"LOCATION#GENERAL": "vị_trí",
}
polarity_dict = {
"không có": "không có",
"positive": "tích_cực",
"neutral": "trung_lập",
"negative": "tiêu_cực"
}
polarity_list = ["không có", "tích_cực", "trung_lập", "tiêu_cực"]
tags = ["chung về nhà_hàng", "giá của nhà_hàng", "tổng_quát về nhà_hàng", "giá đồ ăn",
"chất_lượng đồ ăn", "phong_cách và lựa_chọn đồ ăn", "giá đồ uống", "chất_lượng đồ uống",
"phong_cách và lựa_chọn đồ uống", "bầu không_khí", "dịch_vụ", "vị_trí"]
eng_tags = ["RESTAURANT#GENERAL", "RESTAURANT#PRICES", "RESTAURANT#MISCELLANEOUS", "FOOD#PRICES",
"FOOD#QUALITY", "FOOD#STYLE&OPTIONS", "DRINKS#PRICES", "DRINKS#QUALITY",
"DRINKS#STYLE&OPTIONS", "AMBIENCE#GENERAL", "SERVICE#GENERAL", "LOCATION#GENERAL"]
eng_polarity = ["không có", "positive", "neutral", "negative"]
detect_labels = ['không', 'có']
no_polarity = len(polarity_list)
no_tag = len(tags)
def predict(model, text, tokenizer, model_tokenize=None, processed=True, printout=False):
predicts = []
device = 'cpu'
model.to(device)
model.eval()
model.config.use_cache = False
if not processed:
text = normalize(text)
text = tokenize(text, model_tokenize)
for i in range(no_tag):
tag = tags[i]
score_list = []
input_ids = tokenizer([text] * no_polarity, return_tensors='pt')['input_ids'].to(device)
target_list = ["Nhận_xét " + tag.lower() + " " + polarity.lower() + " ." for polarity in polarity_list]
output_ids = tokenizer(target_list, return_tensors='pt', padding=True, truncation=True)['input_ids'].to(device)
with torch.no_grad():
output = model(input_ids=input_ids.to(device), decoder_input_ids=output_ids.to(device))[0]
logits = output.softmax(dim=-1).to('cpu').numpy()
for m in range(no_polarity):
score = np.sum(np.log(logits[m][range(len(output_ids[m]) - 2), output_ids[m][1:-1]]))
score_list.append(score)
predict = int(np.argmax(score_list)) # Ép kiểu sang int
predicts.append(predict)
if printout:
result = {}
for i in range(no_tag):
if predicts[i] != 0: # Bỏ qua các nhãn không có cảm xúc (mặc định 0)
result[tags[i]] = polarity_list[predicts[i]] # Ánh xạ nhãn
# print(result)
return result
def predict_df(model, df, tokenizer=None, model_tokenize=None, tokenizer_name='vinai/bartpho-word-base', processed=True, printout=True):
model.eval()
device = 'cpu'
model.to(device)
model.config.use_cache = False
if not tokenizer:
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
count_acc = count_detect = f1_detect = f1_absa = pre_detect = rec_detect = pre_absa = rec_absa = 0
total_f1 = len(df)
total = len(df) * no_tag
for i in range(total_f1):
text = df['text'][i]
labels = [df[x][i] for x in eng_tags]
predicts = predict(model, text, tokenizer, model_tokenize, device, processed)
labels_detect = [i for i in range(no_tag) if labels[i] != 0]
predicts_detect = [i for i in range(no_tag) if predicts[i] != 0]
common_detect = [x for x in labels_detect if x in predicts_detect]
if common_detect:
precision_detect = len(common_detect) / len(predicts_detect)
recall_detect = len(common_detect) / len(labels_detect)
f1_detect += (2 * precision_detect * recall_detect / (precision_detect + recall_detect))
pre_detect += precision_detect
rec_detect += recall_detect
labels_absa = [str(i) + '-' + str(labels[i]) for i in range(no_tag) if labels[i] != 0]
predicts_absa = [str(i) + '-' + str(predicts[i]) for i in range(no_tag) if predicts[i] != 0]
common_absa = [x for x in labels_absa if x in predicts_absa]
if common_absa:
precision_absa = len(common_absa) / len(predicts_absa)
recall_absa = len(common_absa) / len(labels_absa)
f1_absa += (2 * precision_absa * recall_absa / (precision_absa + recall_absa))
pre_absa += precision_absa
rec_absa += recall_absa
for j in range(no_tag):
if labels[j] == predicts[j]:
count_acc += 1
count_detect += 1
else:
if labels[j] != 0 and predicts[j] != 0:
count_detect += 1
acc_detect = count_detect / total
pre_detect = pre_detect / total_f1
rec_detect = rec_detect / total_f1
f1_detect = f1_detect / total_f1
acc = count_acc / total
pre_absa = pre_absa / total_f1
rec_absa = rec_absa / total_f1
f1_absa = f1_absa / total_f1
if printout:
print(f"Detect acc: {acc_detect:.4f}%")
print(f"Detect precision: {pre_detect:.4f}%")
print(f"Detect recall: {rec_detect:.4f}%")
print(f"Detect f1: {f1_detect:.4f}%")
print()
print(f"Absa acc: {acc:.4f}%")
print(f"Absa precision: {pre_absa:.4f}%")
print(f"Absa recall: {rec_absa:.4f}%")
print(f"Absa f1: {f1_absa:.4f}%")
return acc_detect, pre_detect, rec_detect, f1_detect, acc, pre_absa, rec_absa, f1_absa
def predict_detect(model, text, tokenizer, model_tokenize=None, processed=True, printout=False):
detect_predicts = []
device = 'cpu'
model.to(device)
model.eval()
model.config.use_cache = False
if not processed:
text = normalize(text)
text = tokenize(text, model_tokenize)
for i in range(no_tag):
tag = tags[i]
detect_score_list = []
input_ids = tokenizer([text] * 2, return_tensors='pt')['input_ids']
target_list = [tag.lower() + " " + detect_label.lower() + " được nhận_xét ." for detect_label in detect_labels]
output_ids = tokenizer(target_list, return_tensors='pt', padding=True, truncation=True)['input_ids']
with torch.no_grad():
output = model(input_ids=input_ids.to(device), decoder_input_ids=output_ids.to(device))[0]
logits = output.softmax(dim=-1).to('cpu').numpy()
for m in range(2):
detect_score = 1
for n in range(logits[m].shape[0] - 2):
detect_score *= logits[m][n][output_ids[m][n+1]]
detect_score_list.append(detect_score)
detect_predict = np.argmax(detect_score_list)
detect_predicts.append(detect_predict)
predicts = []
for i in range(no_tag):
if detect_predicts[i] == 0:
predicts.append(0)
else:
tag = tags[i]
score_list = []
input_ids = tokenizer([text] * (no_polarity - 1), return_tensors='pt')['input_ids']
target_list = ["Nhận_xét " + tag.lower() + " " + polarity.lower() + " ." for polarity in polarity_list if polarity != "không có"]
output_ids = tokenizer(target_list, return_tensors='pt', padding=True, truncation=True)['input_ids']
with torch.no_grad():
output = model(input_ids=input_ids.to(device), decoder_input_ids=output_ids.to(device))[0]
logits = output.softmax(dim=-1).to('cpu').numpy()
for m in range(no_polarity - 1):
score = 1
for n in range(logits[m].shape[0] - 2):
score *= logits[m][n][output_ids[m][n + 1]]
score_list.append(score)
predict = np.argmax(score_list) + 1
predicts.append(predict)
if printout:
result = {}
for i in range(no_tag):
if predicts[i] != 0:
result[eng_tags[i]] = eng_polarity[predicts[i]]
print(result)
return predicts
def predict_df_detect(model, df, tokenizer=None, model_tokenize=None, tokenizer_name='vinai/bartpho-word-base', printout=True):
model.eval()
device = 'cpu'
model.to(device)
model.config.use_cache = False
if not tokenizer:
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
count_acc = count_detect = f1_detect = f1_absa = pre_detect = rec_detect = pre_absa = rec_absa = 0
total_f1 = len(df)
total = len(df) * no_tag
for i in range(total_f1):
text = df['text'][i]
labels = [df[x][i] for x in eng_tags]
predicts = predict(model, text, tokenizer, model_tokenize, processed, device)
labels_detect = [i for i in range(no_tag) if labels[i] != 0]
predicts_detect = [i for i in range(no_tag) if predicts[i] != 0]
common_detect = [x for x in labels_detect if x in predicts_detect]
if common_detect:
precision_detect = len(common_detect) / len(predicts_detect)
recall_detect = len(common_detect) / len(labels_detect)
f1_detect += (2 * precision_detect * recall_detect / (precision_detect + recall_detect))
pre_detect += precision_detect
rec_detect += recall_detect
labels_absa = [str(i) + '-' + str(labels[i]) for i in range(no_tag) if labels[i] != 0]
predicts_absa = [str(i) + '-' + str(predicts[i]) for i in range(no_tag) if predicts[i] != 0]
common_absa = [x for x in labels_absa if x in predicts_absa]
if common_absa:
precision_absa = len(common_absa) / len(predicts_absa)
recall_absa = len(common_absa) / len(labels_absa)
f1_absa += (2 * precision_absa * recall_absa / (precision_absa + recall_absa))
pre_absa += precision_absa
rec_absa += recall_absa
for j in range(no_tag):
if labels[j] == predicts[j]:
count_acc += 1
count_detect += 1
else:
if labels[j] != 0 and predicts[j] != 0:
count_detect += 1
acc_detect = count_detect / total
pre_detect = pre_detect / total_f1
rec_detect = rec_detect / total_f1
f1_detect = f1_detect / total_f1
acc = count_acc / total
pre_absa = pre_absa / total_f1
rec_absa = rec_absa / total_f1
f1_absa = f1_absa / total_f1
if printout:
print(f"Detect acc: {acc_detect:.4f}%")
print(f"Detect precision: {pre_detect:.4f}%")
print(f"Detect recall: {rec_detect:.4f}%")
print(f"Detect f1: {f1_detect:.4f}%")
print()
print(f"Absa acc: {acc:.4f}%")
print(f"Absa precision: {pre_absa:.4f}%")
print(f"Absa recall: {rec_absa:.4f}%")
print(f"Absa f1: {f1_absa:.4f}%")
return acc_detect, pre_detect, rec_detect, f1_detect, acc, pre_absa, rec_absa, f1_absa
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