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Н Новости

Учим нейронную сеть генерировать текст

Автор статьи: Виктория Ляликова

Всем привет!

На волне популярности различных нейросетевых моделей, позволяющих генерировать связный текст, отвечающих на вопросы и позволяющих вести беседы, хотелось бы рассмотреть вопрос о том, а что вообще происходит внутри таких сетей.

Например, мы хотим научить нейронную сеть отличать кошек от собак или может яблоко от апельсина. Тогда мы просто говорим ей, что есть что, и на основе представленных данных, такая нейронная сеть особенным образом находит закономерности и строит самое обычное уравнение, которое в зависимости от подаваемого ей набора данных, пытается классифицировать изображение. Если мы хотим научить сеть предсказывать какие-то значения (погода, курс валют, отток клиентов банка), то мы понимаем, что ей надо предоставить выборку прошлых лет на основе которой она сможет найти закономерности, также сформировать уравнение и спрогнозировать результат.

С генерацией текста фактически тоже самое. Только здесь нейронная сеть учиться предсказывать всего лишь одно слово на основе представленного ей текста. А генерирует текст она по принципу “слово за слово”. По сути, после генерации каждого нового слова, модель просто заново прогоняет через себя весь предыдущий текст вместе с только что написанным дополнением, и выдает новое последующее слово уже с учетом него. В результате получаем связный текст.

Таким образом, в зависимости от того, какой результат мы хотим получить от сети, на тех данных мы и будем ее обучать, например стихотворения, детективы, программирование и так далее. Но конечно генерировать текст можно не только с помощью нейронных сетей.

Генерируем текст с помощью цепей Маркова

Сначала рассмотрим самый простой и наиболее известный генератор - это генератор на основе цепей Маркова. Он будет похож на работу нейросети, но самом деле никаких нейронов там нет, просто есть формулы и таблицы со связями между элементами.

Цепь Маркова - это последовательность событий, где каждое новое событие зависит только от предыдущего и не учитывает все остальные события. Такие алгоритмы изучают какие связи могут быть между словами и потом проходят по этим связям и составляют новый текст. Такой алгоритм не помнит, что было раньше, а смотрит только на предыдущее состояние.

Рассмотрим такую скороговорку:

Саша машет Маше, Маша машет Саше, Маша Саши краше, Саша краше Паши

В теории цепей - это корпус, то есть исходный материал, по которому мы будем составлять взаимосвязи.

Для простоты опустим знаки препинания и большие буквы. Посчитаем сколько раз встречается каждое слово в тексте:

саша - 2
машет - 2
маше - 1
маша - 2
саше - 1
саши - 1
краше -2
паши -1

Мы получили звенья цепи Маркова. Теперь составим пары связанных событий. То есть возьмем одно слово и посмотрим какие события могут идти после него.

саша→(машет, краше).
машет → (маше, саше)
маше→ (маша)
маша →  (машет, саши)
саше → (маша)
саши→ (краше)
краше→ (саша, паши)

Добавим также пары с началом и концом предложения, чтобы алгоритм знал, когда нужно остановиться.

НАЧАЛО → саша
паши→ КОНЕЦ

То есть получается, что когда наш алгоритм дойдет до слов “саша” “машет”, “маша”, “краше” ему придется с какой-то долей вероятности выбрать одно из слов.

Посмотрим как считать эти вероятности. Возьмем пару

краше→ (саша, паши)

При этом “саша” у нас встречается 2 раза, “паши” один раз. А всего вместе эти слова встречаются 3 раза. Получается, что после “краше” вероятность продолжения текста:

словом “саша” - 2\3
словом “паши” - 1\3

В связи с тем, что существуют вероятности, текст в цепях Маркова может каждый раз получаться разным. Алгоритм с разной вероятностью случайным образом идет по разным путям и дает каждый раз новый текст.

Например, такие результаты могут получиться из наших связей.

Саша краше Паши
Саша краше Саша машет Маше Маша Саши краше Паши.
Саша машет Маше Маша машет Саше Маша машет Маше Маша Саши краше Паши

и так далее.

На самом деле серьезные алгоритмы используют не схемы, а матрицы, внутри которых и записаны все вероятности и переходы.

Можно также составлять пары не из одних слов, а из словосочетаний. Можем, например, взять все сочетания из двух слов, которые есть в тексте и составим пары к каждому из них. Это более сложно и ресурсоемко, зато текст получается похожий на человеческий.

Готовим данные

Теперь попробуем генератор цепей Маркова в деле, а для этого нам нужен исходный текст (или корпус). В качестве набора данных возьмем датасет, содержащий стихотворения русских писателей, например, Лермонтова, Пушкина, Маяковского, Фета и т.д. Подключим библиотеки и посмотрим на данные с которыми будем работать:

import numpy as np
df = pd.read_csv('poems.csv')
df=df.dropna()
df.head()

Мы будем работать только с текстом, поэтому будем использовать только столбец “text”. Сначала проведем очистку текста. Для этого объединим все стихотворения в одну строку, а затем приведем текст к нижнему регистру и удалим знаки пунктуации (для упрощения модели), используя регулярные выражения.

# объединяем все поэмы в один текст
combined_text = " ".join(df['text'])
# обработка текста
def preprocess_text(text):
   # перевод в нижний регистр и удаление пунктуации
   text = re.sub(r'[^\w\s]', '', text.lower())
   return text
processed_text = preprocess_text(combined_text)

Реализуем цепь Маркова

После получения обработанного текста, можем приступать к генерации текста. Для этого мы разобьем текст на слова. Все слова, которые стоят рядом, соединим в пары. Далее используя эти пары, составим словарь цепочек, где указано первое слово и все, которые могут идти после него. Потом выберем случайное число для старта, зададим длину текста на выходе и получим результат.

corpus = processed_text.split()
# функция-генератор
def make_pairs(corpus):
   # перебираем все слова в корпусе, кроме последнего
   for i in range(len(corpus) - 1):
       # генерируем новую пару 
       yield (corpus[i], corpus[i + 1])
# вызываем генератор и получаем все пары слов
pairs = make_pairs(corpus)
# словарь, на старте пока пустой
word_dict = {}
# перебираем все слова попарно из списка пар
for word_1, word_2 in pairs:
   # если первое слово уже есть в словаре
   if word_1 in word_dict.keys():
# добавляем второе слово как возможное продолжение 
первого
       word_dict[word_1].append(word_2)
   # если же первого слова у нас в словаре не было
   else:
       # создаём новую запись в словаре и указываем второе слово как продолжение первого
       word_dict[word_1] = [word_2]
# случайно выбираем первое слово для старта
first_word = np.random.choice(corpus)
# делаем наше первое слово первым звеном
chain = [first_word]
# сколько слов будет в готовом тексте
n_words = 30
# делаем цикл с заданным количеством слов
for i in range(n_words):
   # на каждом шаге добавляем следующее слово из словаря, выбирая его случайным образом из доступных вариантов
   chain.append(np.random.choice(word_dict[chain[-1]]))
# выводим результат
print(' '.join(chain))

Смотрим на результат.

23 ноября игумен строгий властелин когда от нашей власти беззаконных ты ищешь круг стань ни разума довольство здравие тебе кураж тресотин сердцем верю чуду опьянена мечта родит земля в роще рассыпался тонкой коркою все как дети в зеленях утонула в спокойствии и к мечте не стоило жить по службе вниманье и

Генерируем текст с помощью нейронной сети

Теперь обратимся к генерации текста с помощью нейронной сети. В поисках информации по данной теме оказалось, что достаточно мало простых и понятных примеров (либо я плохо ищу), на которых можно потренироваться и как-то двигаться дальше в этом направлении. Поэтому примером, который будет рассмотрен ниже, хотелось бы просто наглядно проиллюстрировать, каким образом мы можем заставить нейронную сеть генерировать текст. При этом обращаю внимание, что текст не обязательно должен быть связанным, просто посмотрим, что нам может предложить не сложная нейронная сеть на моем наборе данных. Чтобы получить какой-то приличный результат, для этого нужна очень большая выборка для обучения, много времени, терпения и высокие мощности компьютера.

Рассмотрим сначала такое понятие как n-граммы, которое используется в лингвистике. По сути n-грамма - это последовательность из n элементов (звуков, слогов, слов или символов), идущих в каком-то тексте подряд. На практике чаще имеют в виду ряд слов (реже символов). Последовательность из двух элементов называют биграмма, из трех элементов - триграмма.

Возьмем, например предложение “Мой дядя самых честных правил” и составим из него униграмму, биграмму и триграмму.

Униграмма → (Мой дядя самых честных правил)
Биграмма → (Мой, дядя), (дядя, самых), (самых, честных), (честных правил)
Триграмма → (Мой, дядя, самых) (самых, честных, правил)

Таким образом с помощью n-грамм мы можем предсказывать следующее слово после n-1 слов на основе вероятности их сочетания.

Готовим данные для обучения

Для генерации текста лучше всего подходят LSTM (Long Short Term Memory) нейронные сети. Опираясь на то, что было написано выше про генерацию текста, получается, что наша нейросетевая модель будет учиться предсказывать следующее слово в последовательности столько раз, сколько мы ей скажем, получая таким образом новый текст, обучаясь на том наборе данных, который мы ей предоставим. Данные те же самые, с которыми мы работали с генератором Маркова. Датасет конечно большой и содержит аж 19000 небольших стихотворений (отрывков), но чтобы можно было увидеть результат, я буду использовать только его часть.

После обработки текста, которая была сделана выше, посмотрим на фрагмент текста. Для упрощения модели знаки препинания отсутствуют. Дальнейшие выкладки по преобразованию текста для нейросетевой модели специально буду приводить именно для этого фрагмента, чтобы наглядно продемонстрировать, что происходит.

'на серебряные шпоры\nя в раздумии гляжу\nза тебя скакун мой скорый\nза бока твои дрожу\nнаши предки их не знали\nи гарцуя средь степей\nтолстой плеткой погоняли\nнедоезжаных коней\nно с успехом просвещенья\nвместо грубой старины\nвведены изобретенья\nчужеземной стороны\nв наше время кормят холят\nберегут спинную честь\nпрежде били  нынче колют\nчто же выгодней  бог весть'

Теперь нам надо создать массив с индексами слов, где каждому слову в текстовых данных будет присваиваться уникальный индекс. Для этого будем использовать инструмент Tokenizer библиотеки keras и метод fit_on_texts().

from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

# Токенизация
tokenizer = Tokenizer()
# подгонка текста
tokenizer.fit_on_texts([processed_text])

И выведем индексы слов, какие получились после токенизации, для фрагмента текста, представленного выше. Это можно сделать с помощью метода word_index.

tokenizer.word_index
{'в': 1,
 'за': 2,
 'на': 3,
 'серебряные': 4,
 'шпоры': 5,
 'я': 6,
 'раздумии': 7,
 'гляжу': 8,
 'тебя': 9,
 'скакун': 10,
 'мой': 11,
 'скорый': 12,
 'бока': 13,
 'твои': 14,
 'дрожу': 15,
 'наши': 16,
 'предки': 17,
 'их': 18,
 'не': 19,
 'знали': 20,
 'и': 21,
 'гарцуя': 22,
 'средь': 23,
 'степей': 24,
.......

Далее нам необходимо перебрать все строки (каждую строку будем рассматривать как отдельное предложение) и создать входные последовательности для нейронной сети путем постепенного добавления токенов (индекса слова). Далее из каждой строки (последовательности) создаем n-грамму последовательностей и добавляем ее в список.

input_sequences = []
for line in processed_text.split('\n'):
	token_list = tokenizer.texts_to_sequences([line])[0]
	for i in range(1, len(token_list)):
    	n_gram_sequence = token_list[:i+1]
    	input_sequences.append(n_gram_sequence)

Результат применения метода text_to_sequences, который переводит текст в последовательность целых чисел путем замены каждого слова на его номер в словаре.

[3, 4, 5]
[6, 1, 7, 8]
[2, 9, 10, 11, 12]
[2, 13, 14, 15]

Исходный текст был

На серебряные шпоры
Я в раздумии гляжу
За тебя скакун мой скорый
За бока твои дрожу
.......

После получения n-грамм посмотрим как выглядит наша последовательность

input_sequences
[[3, 4],
 [3, 4, 5],
 [6, 1],
 [6, 1, 7],
 [6, 1, 7, 8],
 [2, 9],
 [2, 9, 10],
 [2, 9, 10, 11],
 [2, 9, 10, 11, 12],
 [2, 13],
 [2, 13, 14],
 [2, 13, 14, 15]]
.......

То есть сначала в нашей n-грамме 2 слова, а затем мы постепенно добавляем новые слова по одному, каждый раз создавая новые последовательности, пока не закончатся слова в строке. И так поступаем с каждой строкой.

Далее нам необходимо обеспечить одинаковую длину для всех последовательностей, для этого дополним нулями каждую последовательность до максимальной длины. Здесь поможет функция pad_sequences().

max_sequence_len = max([len(x) for x in input_sequences])
input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))
array([[ 0,  0,  0,  3,  4],
       [ 0,  0,  3,  4,  5],
       [ 0,  0,  0,  6,  1],
       [ 0,  0,  6,  1,  7],
       [ 0,  6,  1,  7,  8],
       [ 0,  0,  0,  2,  9],
       [ 0,  0,  2,  9, 10],
       [ 0,  2,  9, 10, 11],
       [ 2,  9, 10, 11, 12],
       [ 0,  0,  0,  2, 13],
       [ 0,  0,  2, 13, 14],
       [ 0,  2, 13, 14, 15]

Все, выборка для обучения сети готова.

Строим модель

Разделим теперь выборку на вектор признаков X и вектор меток Y. Вектор Х будет содержать дополненные входные последовательности без последнего слова. Вектор меток Y будет содержать последнее слово каждой дополненной последовательности.

x= input_sequences[:,:-1]
labels = input_sequences[:,-1]

Определим размер входного вектора, который будет равен общему количеству уникальных слов в нашем словаре + еще одно слово для учета дополненного слова:

# считаем количество слов
total_words = len(tokenizer.word_index) + 1

Нейронная сеть будет состоять из трех уровней.

Embedding слой, на вход поступает последовательность из количества уникальных слов, на выходе получаем векторы размером 200 для каждого слова во входной последовательности.
LSTM уровень состоит из 150 модулей LSTM.
Dense является полносвязным слоем с функцией активации softmax. Принимает входные данные слоя LSTM и прогнозирует распределение вероятностей по всем словам в словаре, которые равны числу классов.

Общее количество обучаемых параметров составляет 10 668 996 для датасета из 500 отрывков стихотворений.

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense
model = Sequential()
model.add(Embedding(total_words, 200))
model.add(LSTM(150))
model.add(Dense(total_words, activation='softmax'))

Компилируем нейронную сеть, используя оптимизатор Адама и разреженную категориальную кросс-энтропию, так как у нас много классов и чтобы избежать методов преобразования вектора Y в категориальный формат, так как это требует много ресурсов.

model.compile(optimizer='adam',
              	loss='sparse_categorical_crossentropy',
              	metrics=['accuracy'])

Будем обучать модель в течение 100 эпох

history = model.fit(x,labels, epoch=100, verbose=1)

Оцениваем модель

Допустим модель обучена, как мы можем оценить насколько хорошо она может генерировать новый текст. В обработке текстов есть такое понятие как коэффициент неопределенности или перплексия. Перплексия не является метрикой точности или полноты модели, но она помогает определить, насколько хорошо модель может обобщать на новые данные. Чем меньше значение перплексии, тем луше модель. Перплексия - это мера того, насколько хорошо модель может предсказывать последовательности слов, которые не были включены в обучающий набор данных.

Например, если показатель перплексии равен 100, это означает, что когда модели приходится предсказывать следующее слово, это приводит модель в такую же «растерянность», как если бы ей пришлось выбирать одно из 100 слов.

Представим, что мы хотим обучить модель на предсказание следующего слова в предложении "Я люблю есть ...". То есть модель должна выбрать слово, которое наиболее вероятно следует за "есть". Разумно предположить, что модель, которая выберет слово "яблоки", с высокой вероятностью будет иметь более низкую перплексию, чем модель, которая выберет слово "крокодил".

Теперь вопрос, а как рассчитать это показатель перплексии? Если кратко, то для определения перплексии в обработке естественного языка используется такая формула

$H(q) = e^{-\frac{1}{N}\sum_{i=1}^{N} ln q(x_i)}$

q(x_i) - это предсказания сделанные нашей моделью на основании обучающего множества

Создадим функцию для вычисления перплексии:

def calculate_perplexity(model, xs):
	# вычисление вероятностей для каждого слова в последовательности
	predictions = model.predict(xs)
	cross_entropy = 0.0
    
	for i in range(len(predictions)):
    	    cross_entropy += -np.log(np.max(predictions[i]))
	avg_cross_entropy = cross_entropy / len(predictions)
    
	# вычисляем перплексию
	perplexity = np.exp(avg_cross_entropy)
	return perplexity

# вычисляем перплексию для нашей модели
perplexity = calculate_perplexity(model, x)
print("Perplexity:", perplexity)

Генерируем текст

После того, как сеть обучена, посчитана перплексия, можно пробовать генерировать текст. Процесс генерации текста включает в себя токенизацию входного текста и его заполнение в соответствии с входным размером модели для прогнозирования следующего слова. Предсказанное слово добавляется к входному тексту, и процесс повторяется итеративно для создания последовательности слов.

Теперь немного поговорим о том, какой результат мы получаем от сети. У нас большое количество классов, равное числу уникальных слов в нашем наборе данных. И предсказанием сети является вектор, который содержит вероятности для следующего слова, с помощью которых мы и будем выбирать наше предсказание. Мы можем всегда выбирать слово с максимальной вероятностью для получения нового текста, но тогда мы можем получить, что каждый раз будем предсказывать одно и тоже слово и зациклимся на одной фразе. Поэтому мы внесем некоторую случайность в наш генерируемый текст, выбирая новое слово случайным образом из последовательности.

Создаем функцию для генерации текста:

def generate_text(seed_text, model, tokenizer, max_sequence_len, num_words):
	generated_text = seed_text
	for _ in range(num_words):
      # токенизация текста
    	token_list = tokenizer.texts_to_sequences([seed_text])[0]
      # заполнение токенизированной последовательности
    	token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
     # получаем вероятности предсказаний для каждого слова
    	predictions = model.predict(token_list, verbose=0)
   	 
    	# Получаем индекс для следующего слова, используя случайное слово из распределения вероятностей
    	predicted_index = np.random.choice(len(predictions[0]), p=predictions[0])
   	# Сопоставляем индекс с соответствующим словом
    	output_word = ""
    	for word, index in tokenizer.word_index.items():
        	if index == predicted_index:
            	output_word = word
            	break	 
    	# Добавляем предсказанное слово в сгенерированный текст
    	generated_text += " " + output_word
     	# Обновляем исходный текст
    	seed_text = " ".join(seed_text.split()[1:]) + " " + output_word
    	return generated_text

Для генерации текста в функцию необходимо передать начальный текст, модель обученной нейронной сети, токенизатор и количество слов, которое мы хотим получить в тексте.

# Входной текст
seed_text = "Я любил"
generated_text = generate_text(seed_text, model, tokenizer, max_sequence_len, num_words=35)
print("Generated Text:")

Также мы можем сохранить токенизатор и обученную языковую модель в отдельные файлы:

# сохранение обученной модели
model.save('my_model.h5')
# сохранение токенизатора в файл
with open('tokenizer.pickle', 'wb') as handle:
    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)

Результаты

После 10 эпох обучения на наборе в 100 отрывков стихотворений:

Perplexity: 7.058923777230348

Generated Text:
Я любил тебя вас ее колют от ушей заслоненный наше очи произнес страстей весло убей моей смелый вечность звучный бой открыл моей бесплодной стук отвагой и ночи седой через печали жалок была смущенный их несет за пшено тревогу.

После 50 эпох обучения на наборе в 100 отрывков стихотворений:

Perplexity: 1.7203394007635984

Generated Text:
Я любил но был под бурей тягостных не излечит горе над озером видал я тебя с душой безнадежной иногда презирая на бале быть проводником к мелькнет на коней будут метель число коня кинул медленно грызет от дневных русский весь в траве почти в тебе доныне как всякий биться рад так вдруг в

После 100 эпох обучения на наборе в 100 отрывков стихотворений:

Perplexity: 1.5875433038328622

Generated Text:
Я любил но был она как смерть темна и видят ктото на влажной взор и вдруг знакомый измаил как она на каменной скале сырой два спокойно родная род и в глубине пещеры своих зачем своих ей или тот на борзом чудной уж забыты сходятся венцом и девы бледные черты его недвижный трон

После 30 эпох обучения на наборе в 500 отрывков

Perplexity: 1.9448061951154562

Generated Text:
Я любил тебя тот рок моей любви не знает но она сама придет но ты не обвиняй страдальца молодого кустов отгоняя светлей мгновенно блеск земля ее язык скрывался я писал с презреньем гордым кинешь за порог и в

После 150 эпох обучения на наборе в 500 отрывков

Perplexity: 1.5106264558327417

Generated Text:
Я любил послушать ни тихих ласк ночных им до два глаз твоей статую иолая и отметил этеокла не глядит и с каждой ветром над тихим висела приближаться страстей между веку скал раздор да хром лгут царей чаще

Да, к сожалению осмысленный текст не появился, но прогресс по мере обучения все равно есть. Конечно, на вход можно подавать и другой текст, на любом языке, менять параметры сети и получать совершенно другие результаты.

Про генерацию текста и другие инструменты и возможности машинного обучения эксперты OTUS рассказывают в рамках практических онлайн-курсов. С полным каталогом курсов можно ознакомиться по ссылке.

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28 мая 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
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27.08.24 12:50 James889900

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06.09.24 01:44 Celinagarcia

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23.09.24 18:56 matthewshimself

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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
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31.10.24 00:13 ytre89

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02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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21.11.24 08:02 Emily Hunter

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22.11.24 04:41 [email protected]

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22.11.24 23:43 teresaborja

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24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

COMMENT ON I NEED A HACKER TO RECOVER MONEY FROM BINARY TRADING. HIRE FASTFUND RECOVERY
25.11.24 02:20 briankennedy

After countless hours of research and desperate attempts to find a solution, I stumbled upon FASTFUND RECOVERY. It was like finding an oasis in the middle of a desert. Their website promised to help victims of scams reclaim what was rightfully theirs, and I instantly knew I had to give them a shot. Before diving headfirst into the recovery process, I wanted to make sure that FASTFUND RECOVERY was the real deal. So, I did my due diligence and looked into their expertise and reputation. To my relief, I found that they had an impeccable track record, successfully assisting countless individuals in recovering their lost funds. Their team consisted of experts in cybersecurity and financial fraud, armed with the knowledge and tools needed to tackle even the most intricate scams. With their reputation preceding them, I felt a renewed sense of hope. FASTFUND RECOVERY successfully came to my aid and got back the amount I lost to these scammers and for this, I am sending this article for clarification. The info of FASTFUND RECOVERY is email: Fastfundrecovery8 (@)Gmail (.) com. Web fastfundrecovery(.)com. (W/A 1 807/500/7554)
26.11.24 21:59 [email protected]

In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com
26.11.24 23:12 rickrobinson8

FAST SOLUTION FOR CYPTOCURRENCY RECOVERY SPARTAN TECH GROUP RETRIEVAL
26.11.24 23:12 rickrobinson8

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27.11.24 00:39 [email protected]

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27.11.24 09:10 Michal Novotny

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