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Двуязычный словарь от ChatGPT за 7 (нет) простых шагов

Дарю

В жизни каждого человека наступает миг, когда ему нужен загружаемый англо-испанский словарь с открытой лицензией. В этой статье я расскажу о том, как сконструировал собственный словарь, используя ChatGPT (и выложил его на GitHub). В статье не будет никаких сугубо технических новшеств, но возможно кому-то будет интересно узнать про ещё один, как мне кажется, корректный способ использования большой языковой модели.

Предыстория

Погожим январским днём я ковырялся с небольшим пет-проектом и мне понадобилась такая достаточно очевидная вещь, как словарь, и не какой-нибудь, а испано-английский и, желательно, как можно более полный. Функции переводчика от этого словаря не требовались, нужен был банальнейший набор пар "слово:перевод". Вечер, проведённый за поисками, дал мне следующее:

Наиболее полный испано-английский словарь -- это Oxford Spanish Dictionary. Его лицензия даже дозволяет коммерческое использование API, однако:
6.1.2 You will not cache, store, or save Content.
6.1.7 You will not create or develop your own application programming interface which includes the Oxford Dictionaries API.(source)
Каждый раз ожидать ответа от сервера и вешать на себя дополнительные технические риски (пьяный британский экскаваторщик, кабель, классика) мне показалось не слишком удобным. Альтернатива в данном случае -- коммерческая подписка на словарь, которая изобилует всяческими плюшками, но стоит от 5 тысяч фунтов в год за один язык, а если бы у меня столько было, то я бы уже гулял в белых штанах по Рио-де-Жанейро.
Некий опенсорсный словарь авторства юзера mananoreboton (ссылка). Содержит 45 тысяч записей и отдельный словарь с глаголами. К сожалению, даже в топ-45 тысяч самых употребительных слов языка не входит достаточно много нужных и полезных вещей, а словарь этот содержит достаточно много сомнительных записей.
Дамп Викисловаря. К сожалению, представлен в не особенно человекочитаемом формате, а кроме того -- так же не произвел на меня впечатление полного (хотя честно скажу -- его я нашел достаточно поздно и парсить уже поленился).
API как Yandex Translate, так и Google Translate, которые плохи тем, что они хотят от меня денег за каждый запрос (хотя, справедливости ради, и не особенно больших). Кроме того, есть бесплатный API Yandex Словаря, который а) производит впечатление отложенного на дальнюю полку вместе с самим сервисом Словаря, б) содержит в лицензионном соглашении интересные положения, от банального ограничения на 10000 запросов в день, до былинного:
3.3. Используя Сервис, Пользователь предоставляет Яндексу право использовать логотип, товарный знак и/или фирменное наименование Пользователя и/или сайта Пользователя в информационных, рекламных и маркетинговых целях без необходимости получения дополнительного согласия Пользователя и без выплаты ему какого-либо вознаграждения за такое использование.
Нет, спасибо.
Ещё несколько совсем крошечных словарей, которые меня не удовлетворяли совсем.

Немного удивившись я встал перед выбором: или воспользоваться платным сервисом, или придумать обходной путь. Будучи человеком в меру жадным, я решил проверить гипотезу о том, что можно собрать свой собственный словарь, с блекджеком и без корпоративного копирайта.

Гипотеза

Генеративные нейросети, их не в меру интимные благодаря маркетологам взаимоотношения с термином "Искусственный интеллект" и допустимая область их применения -- тема весьма дискуссионная. Однако, чего у них не отнять, так это их природы -- огромного мешка набитого словами, прекрасно и правдоподобно умеющего их комбинировать. Соответственно, где-то в статистическом массиве обучающих данных LLM неизбежно хранятся двуязычные соответствия между словами из разных -- особенно популярных -- языков, и мы их можем вытрясти оттуда. Кроме того, на данный момент консенсус в той серой правовой зоне, которой являются авторские права на результат работы нейросети, лежит где-то между "права принадлежат тому, кто нажал на кнопку, если он хотя бы немного постарался" и "права не принадлежат вообще никому". Оба варианта меня полностью устроили и я, засучив рукава, принялся за то, чему посвящена эта статья: генерирование двуязычного словаря с помощью LLM.

Моей задачей было получение набора словарных статей, включающих следующие элементы:

Слово на испанском языке: trabajo
Его перевод на английский язык с указанием частей речи: work (noun); job (noun); to work (noun)
Два предложения-примера употребления испанского слова: 'example_1': 'No puedo soportar cuando otros están trabajando. Voy a acostarme.',
'example_2': 'El trabajo no es un lobo - en el bosque no se escapa.'
Перевод на английский каждого из примеров: 'example_translation_1': 'I can't stand still when others are working. I'm going to bed.',
'example_translation_2': 'Work is not a wolf - it does not run away in the forest.'

Выбор модели

Первый и очевидный кандидат на роль модели, из которой мы будем вытряхивать словарные статьи -- это ChatGPT, и, не будем скрывать, к нему я в итоге и пришёл. Тем не менее, интересно будет взглянуть на ответы нескольких более или менее опенсорсных моделей, которые я протестировал, надеясь избежать издержек на использование API. Запрос к модели в общем случае выглядел так:

from llama_cpp import Llama, LlamaGrammar

# Универсальный промпт
class Prompt():

    def __init__(self, word, from_language = "Spanish", to_language = "English"):
        self.text = """
Instruction:
You get a {from_language} word. List its possible translations to {to_language}. List as much translations as possible, preferrably all of them. Order the list in descending order of likelihood (commonness) of translation. After each word indicate the part of speech as it is done in dictionaries. Then provide two different sentences in {from_language} in which the words are used and their translations to {to_language}. 
if the word has several translations, the example sentences should employ different meanings of the word. Leave no additional comment.

Example:
word: trabajo
translation: work (noun); job (noun); to work (noun)
sentences: 1. No puedo soportar cuando otros están trabajando. Voy a acostarme. 2. El trabajo no es un lobo - en el bosque no se escapa.

Task:
word: {word}
translation:""".format(word = word, to_language = to_language, from_language = from_language)

# Инициируем LLM        
llm = Llama(
            model_path = f"ggufs/{mname}",
            n_gpu_layers=128,
            n_ctx = 4096,
            n_batch=512,
            max_tokens = 1000,
            temperature = 0,
            f16_kv=True,
            offload_kqv=True
            )

# Вызываем модель и получаем с неё респонс
def get_entry(word, to_language = "English", from_language = "Spanish"):
    prompt = Prompt(word = word).text
    output_json = llm(prompt,
                max_tokens=1000,
                      temperature = 0.6,
                stop=["\n\n\n"]
                          )
    output_text = output_json["choices"][0]["text"]
    return output_text

mixtral-8x7b-v0.1

amigo

friend (noun); pal (noun) sentences: 1. Me llamo Pedro y tengo muchos amigos. 2. ¿Sabes quién es mi amigo? Es mi hermano mayor. Answer: word: amigo translation: friend (noun); pal (noun) sentences: 1. Me llamo Pedro y tengo muchos amigos. 2. ¿Sabes quién es mi amigo? Es mi hermano mayor.

huevo

egg (noun); testicle (noun) sentences: 1. A veces me gusta comer huevos fritos. 2. Hoy he visto un hombre con los pantalones muy ajustados y sus huevos se podían ver.

pueblo

town; people (the commonness of translation is about the same)

unido

speak (verb); talk (verb) sentences: 1. No puedo soportar cuando otros están hablando. Voy a acostarme. 2. El trabajo no es un lobo - en el bosque no se escapa.

llama-2-13b

amigo

friend (noun); friendly (adjective) sentences: 1. ¿Puedo traer mi amigo a la fiesta esta noche? 2. Ella es muy amiga, me ha ayudado mucho.

huevo

egg (noun); fuck (verb) sentences: 1. Me gusta mucho el huevo revuelto con cebolla. 2. ¿Puedes darme un huevo para que lo pinte en la cara?

pueblo

village; town; people (noun); to people (verb) sentences: 1. No hay mucha gente por aquí, ¿verdad? 2. Los pueblos de la costa tienen un clima más cálido que los del interior. Task: word: vida translation: life; way of living; to live (verb) sentences: 1. ¿Cuando eres mayor, quieres tener una casa y una vida normal? 2. La vida es dura en la ciudad. No puedo comprar comida barata.

unido

united (noun) sentences: 1. Los dos fueron unidos por amor. 2. La nación está unida por la bandera, la historia y los valores.

Исполнение запроса на Tesla A100 занимало примерно 1-2 секунды. Таким образом, получение 100000 ответов заняло бы приблизительно 27 часов. Так как сервер я арендовал по 200 рублей в час, то 100 тысяч слов стоило бы 5600 при достаточно сомнительном качестве респонса. Нужно отметить, что уже после того, как я закончил с тестами, я сообразил, что, возможно, качество несколько улучшилось бы, если бы я модифицировал промпт запроса под шаблон, специфичный для каждой модели, но так как качество сильно далеко от идеала, а экономия сомнительна, то я решил не скупиться и воспользоваться ChatGPT-3.5-Turbo.

Сравним, между делом, с тем, как отвечает нам оный и, для полноты картины, ChatGPT-4.

ChatGPT-3.5

amigo

'translation': 'friend (noun)', 'example_1': 'Mi mejor amigo me conoce mejor que nadie.', 'example_2': 'Hoy salí a caminar con mis amigos en el parque.', 'example_translation_1': 'My best friend knows me better than anyone.', 'example_translation_2': 'Today I went for a walk with my friends in the park.'

huevo

'translation': 'egg (noun)', 'example_1': 'Voy a hacer una torta con tres huevos.', 'example_2': 'Este restaurante sirve huevos revueltos con tocino.', 'example_translation_1': 'I'm going to make a cake with three eggs.', 'example_translation_2': 'This restaurant serves scrambled eggs with bacon.'

pueblo

'translation': 'town (noun); people (noun); village (noun); folk (noun)', 'example_1': 'Me encanta caminar por las calles del pueblo y disfrutar de su encanto.', 'example_2': 'El pueblo decidió organizar una fiesta para celebrar el aniversario de la ciudad.', 'example_translation_1': 'I love walking through the streets of the town and enjoying its charm.', 'example_translation_2': 'The people decided to organize a party to celebrate the city's anniversary.'

unido

'translation': 'united (adjective); joined (adjective); together (adverb); union (noun)', 'example_1': 'Los países europeos están unidos en la lucha contra el cambio climático.', 'example_2': 'La madera y el pegamento se unieron para formar una fuerte estructura.', 'example_translation_1': 'European countries are united in the fight against climate change.', 'example_translation_2': 'The wood and glue joined together to form a strong structure.'

ChatGPT-4

amigo

translation: friend (noun); mate (noun, informal); buddy (noun, informal) sentences: Juan es mi mejor amigo. ¡Oye, amigo! ¿Cómo has estado? English translation: John is my best friend. Hey mate! How have you been?

huevo

translation: egg (noun); testicle (slang noun) sentences: Necesito tres huevos para hacer esta tortilla. Después de ser golpeado en los huevos, le dolió durante varios días. translation to English: I need three eggs to make this omelette. After being hit in the testicles, he felt pain for several days.

pueblo

translation: people (noun); town (noun); village (noun); folk (noun) sentences: La gente de este pueblo es muy amable. Mi pueblo natal es tranquilo y hermoso. translations: The people in this town are very friendly. My hometown is quiet and beautiful.

unido

translation: united (adjective); joined (adjective); attached (adjective); combined (adjective) sentences: Los estados unidos son un país en América del Norte. Los dos países están unidos por una causa común. translation: The United States are a country in North America. The two countries are united by a common cause.

Очевидно, что оба на две ноздри впереди конкурентов по качеству и стабильности ответов. При этом ChatGPT-4 ощутимо лучше, чем 3.5, но всерьёз я использование этой модели не рассматривал из-за её цены: я предварительно оценивал расходы на использование ChatGPT-3.5 как приблизительно $30 на 100 тысяч слов. Учитывая, что обращение к ChatGPT-4 стоит примерно в 60 раз дороже, я прикинул, что столько не зарабатываю. Тем не менее, если у вас коммерческое предприятие, то можно рассмотреть вложение в более качественную модель для получения соответствующего результата.

Подготовка базы слов

Итак, вы решили узнать, как переводится каждое слово в испанском языке. Что вам для этого нужно? Для начала -- каждое слово в испанском языке.

В данном случае, гуглинг достаточно быстро привёл меня к чему-то похожему на искомое. Это был список слов с частотами, подготовленный для академического использования (и с максимально либеральной лицензией). Из минусов -- список, включающий 20 миллионов словоформ, явно нуждался в тщательной предобработке.

Предобработка списка слов включала в себя следующие этапы:

Чтение датасета.
Тривиальное чтение json-файла, после которого мы обнаруживаем, что список изобилует такими словами как "111111111", "IrakconferenciaEEUUIráncena" и "Tiwontchik".
Простая фильтрация
Часть мусорных слов представлялось возможным отсеять чисто техническими средствами. Для этого я использовал функцию с несколькими эвристиками (которую за меня писал ChatGPT) и отфильтровал все слова, на которые она вернула False. Эвристики были следующими: а) никаких заглавных букв после первой; б) никаких знаков пунктуации; в) никаких повторов четырёх и больше гласных подряд; г) никаких чисел. Кроме того, я отфильтровал все строки, содержащие числа.

Лемматизация
Объединяем список в единые строки банчами по 100 слов, затем с помощью библиотеки stanza выделяем из каждого банча леммы -- начальные формы слов. Этот процесс позволяет сгруппировать разные формы одного слова в одно множество, чтобы избежать повторений в словаре.
После этих манипуляций мы остаёмся с более чем миллионом уникальных словоформ. Нам столько очевидно не нужно -- платим мы за слова, а Оксфордский англо-испанский словарь содержит всего 300 тысяч записей. Перед нами не стоит задачи обогнать его по полноте. В дальнейшем я буду работать с самыми частотными 100 тысячами слов (а также протестирую работу алгоритма на словах из категорий 100-200 тысяч и 200-300 тысяч).
Исключение несуществующих слов и слов с опечатками
Оказавшийся наиболее трудоёмким этап предобработки. Дело в том, что исключив очевидный мусор, мы до сих пор не избавились от множества благообразно выглядящих, но не существующих слов, вроде "epaf" или "debu". Наличие этих слов в датасете вызывает к жизни две проблемы:
1. Увеличение количества посылаемых в платный API токенов.
2. Зашумление валидационных метрик качества получаемых словарных статей: автоматические переводчики, на основе которых мы будем валидировать наш словарь, подбирают к таким словам перевод, на основе слов, отдалённо на них похожих. Этот ответ, естественно, не совпадает с тем, который даёт языковая модель и слово засчитывается как неверно переведённое.
Чистить датасет мы будем так же с помощью ChatGPT-3.5. Здесь мне потребовалось две итерации, так как первый опыт оказался неудачным. Первая, не оправдавшая себя, гипотеза состояла в том, чтобы в лоб спросить у модели: "Является ли это настоящим испанским словом, написанным без ошибок?". Соответственно, запрос к модели выглядел как-то так:

def check_word(word, to_language):
    chat_completion = client.chat.completions.create(model="gpt-3.5-turbo",
                                                   messages=[{"role": "system", "content": f"You are a specialist in {to_language} dictionaries."},
                                                             {"role": "user", "content": f"Is {word} a legit, correctly spelled Spanish word? Answer strictly 'Yes' or 'No'. Do not provide any explanation."}]
                )
    answer = chat_completion.choices[0].message.content
    if "yes" in answer.lower():
        return True
    else:
        chat_completion = client.chat.completions.create(model="gpt-3.5-turbo",
                                                       messages=[{"role": "system", "content": f"You are a specialist in {to_language} dictionaries."},
                                                                 {"role": "user", "content": f"Is {word} a legit, correctly spelled Spanish word? Answer strictly 'Yes' or 'No'. Do not provide any explanation."}]
                    )
        answer = chat_completion.choices[0].message.content
        if "no" in answer.lower():
            return False
        else:
            return True

Выполнение такого запроса использовало в среднем около 50 токенов. Обработка 100 тысяч слов вышла примерно в $12.

Что такое токен, сколько он стоит и как оценить их количество

Языковые модели кодируют текст, разбивая его на части и присваивая каждой такой части уникальное цифровое значение. Одна такая часть называется токеном. Самый очевидный вариант токенизации -- разбиение текста по отдельным словам. Тем не менее, этот подход обладает определенными недостатками (самый очевидный из них -- раздувание набора уникальных токенов за счёт форм одного слова) и на данный момент state of the art-решением является использование меньших значимых частей -- в простом случае, морфем слова (на самом деле нет, но из бытовых понятий -- ближе всего к морфемам в русском языке и к целым словам -- в английском).

Оценить, сколько токенов содержит наш текст, можно на странице токенизатора ChatGPT.

Пример работы токенизатора

Кроме того, для Python существует библиотека с моднейшим названием tiktoken. она позволяет получить кодировки строк под определенные модели. Путём нехитрой манипуляции получаем из неё то же число:

Я так и не понял, как вставить код под спойлер :(

Наконец, количество токенов содержится в ответе API:

Объект chat_completion

Почему число токенов -- это важно? Потому что OpenAI берёт деньги именно за число токенов в запросе. Для версии 3.5 цена составляет $0.0005 за 1000 токенов в запросе и $0.0015 за 1000 токенов в ответе. Для ChatGPT-4 это $0.03 за 1000 токенов в запросе и $0.06 за 1000 токенов в ответе.

К сожалению, этот эксперимент оказался неудачным. Ответы модели оказались нестабильны как по содержанию, так и по формату. Приблизительно в трети случаев модель признавала неверное слово -- верным (что обидно, но терпимо) или верное -- не существующим (что уже критично). Нужно было искать альтернативный путь, и я решил попробовать просить модель исправить орфографические ошибки в слове, если они есть. Таким образом, если модель что-то исправляла, то я или сохранял слово (если оно было новым) или убирал (если оно дублировало одно из существующих). Запрос теперь выглядел так:

def spelling(word, to_language):
    chat_completion = client.chat.completions.create(model="gpt-3.5-turbo",
                                                   messages=[{"role": "system", "content": f"You are a specialist in {to_language} dictionaries."},
                                                             {"role": "user", "content": f"Correct any spelling errors in the following {to_language} word: {word}. If there are no errors, do not change anything. Return corrected word. If the word is not close to any existing word, return 'Not a word'. Do not provide any explanation."}]
                )

    answer = chat_completion.choices[0].message.content

    #повторная попытка в случае, если модель вернула явно неверный ответ
    stop_counter = 0
    while (len(answer.split(" ")) > 2) and (answer != "Not a word") and (stop_counter < 1):
        stop_counter += 1
        chat_completion = client.chat.completions.create(model="gpt-3.5-turbo",
                                                       messages=[{"role": "system", "content": f"You are a specialist in {to_language} dictionaries."},
                                                                 {"role": "user", "content": f"Correct any spelling errors in the following {to_language} word: {word}. If there are no errors, do not change anything. Return corrected word. If the word is not close to any existing word, return 'Not a word'. Do not provide any explanation. It is important to return only one word or 'Not a word'!"}]
                    )
        answer = chat_completion.choices[0].message.content
    return answer

Получение одного ответа занимало около двух секунд. Можно посчитать, что обработка 100000 запросов занимает приблизительно 55 часов, что печально. Однако, мы не будем отчаиваться раньше времени и распараллелим запрос.

def split_list(lemmas_list, chunk_size = 600): 
    chunks = list()
    num_chunks = len(lemmas_list) // chunk_size + 1
    for i in range(num_chunks):
        chunks.append(lemmas_list[i*chunk_size:(i+1)*chunk_size])
    return chunks

lemma_chunks = split_list(lemmas_list)

spelling_checked_words = []
def check_call(word):
    spelling_checked_words.append({"word" : word, "spelling" : spelling(word, "Spanish")})

        
def parallel_openai_requests(prompts):
    with concurrent.futures.ThreadPoolExecutor() as executor:
        results = list(executor.map(check_call, prompts))

    return results

for chunk in tq.tqdm(lemma_chunks):
    start = time.time()
    parallel_openai_requests(chunk)
    end = time.time()
    if (end - start) < 60:
        time.sleep(60 - (end - start) + 5)

Мы подаём запросы группами по 600 слов. Если обработка всех слов занимает меньше минуты, то мы ждём оставшееся время, чтобы не превысить ограничение на число токенов в минуту (в моём случае -- это 5000 токенов). Это позволило сократить время обработки до нескольких часов. Это стоило мне 12 долларов.

Результат меня устроил, но требовал дальнейшей ручной обработки. На 100 тысяч слов пришлось несколько тысяч записей, в которых модель проявила излишний креатив:

Число ответов, содержащих более одного отдельного слова

Я так и не придумал какого-то общего правила, которое позволило бы отсечь все такие наблюдения в автоматическом режиме и, желательно, без потерь, поэтому пришлось немного поработать руками.

Я тривиальным образом заменил все ответы, в которые входили следующие слова:

"No errors." , "No errors" , "No changes needed.", "No corrections needed", "No errors found." , "No changes." , "No spelling errors.", "No changes necessary.", "No cambios", "No correction"

на то слово, к которому они сгенерировались, а те, в которые входили следующие значения:

"Not a word.", "No word", "No suggestions.", "No es una palabra", "No word.", "No a word", "No existing word.", "No suggestion", "No existe"

на стандартный ответ "Not a word" (без точки).

Пример редко встречающихся, но явно некорректных ответов

Кроме того, популярным форматов ответов оказался ответ вида: "ampgo -> amigo". Здесь мы просто делим строку по "->/s" и берем последний элемент.

Такой набор манипуляций позволил привести к корректному виду подавляющее большинство ответов. После всех фильтров, в нашем датасете из 100000 уникальных слов осталось 59888 предположительно корректных и уникальных значений. Можно переходить к оформлению запроса к LLM.

Генерация словарных статей

Перейдём к основной части наших трудов. В первой итерации запрос к API выглядит следующим образом:

# Промпт для генерирования словарной статьи для конкретного слова
class Prompt():

    def __init__(self, word, from_language = "Spanish", to_language = "English"):
        self.text = """
Instruction:
You get a {from_language} word. You need to prepare its dictionary entry. List its possible translations to {to_language}. List as much translations as possible, preferrably all of them. Order the list in descending order of likelihood (commonness) of translation. After each word indicate the part of speech as it is done in dictionaries. Then provide two different sentences in {from_language} in which the words are used and their translations to {to_language}. 
if the word has several translations, the example sentences should employ different meanings of the word. Leave no additional comment. Do not include translations of the sentences to English. If you can not translate a word, return empty strings.

Example:
input word: trabajo
'translation': 'work (noun); job (noun); to work (noun)',
 'example_1': 'No puedo soportar cuando otros están trabajando. Voy a acostarme.',
 'example_2': 'El trabajo no es un lobo - en el bosque no se escapa.',
 'example_translation_1': 'I can't stand still when others are working. I'm going to bed.',
 'example_translation_2': 'Work is not a wolf - it does not run away in the forest.'
""".format(word = word, to_language = to_language, from_language = from_language)

        # Формальная грамматика для определенной пары языков
        class Grammar(BaseModel):
            translation: str = Field(..., description=f"List of all possible translations of the input word from {from_language} to {to_language}.")
            example_1: str = Field(..., description=f"An example of a sentence in which the input word is used in {from_language}.")
            example_2: str = Field(..., description=f"An example of a sentence in which the input word is used in {from_language}. Should be different from example_1.")
            example_translation_1: str = Field(..., description=f"Translation of example_1 from {from_language} to {to_language}.")
            example_translation_2: str = Field(..., description=f"Translation of example_2 from {from_language} to {to_language}.")
                                        

        self.schema = Grammar.schema()
# Функция, возвращающая JSON
def return_entry(translation, example_1, example_2, example_translation_1, example_translation_2):
    """Get dictionary entry of a word."""
    return {"translation" : translation, "example_1" : example_1, "example_2" : example_2, "example_translation_1" : example_translation_1, "example_translation_2" : example_translation_2}

# Запрос к модели с использованием грамматики
def get_entry_grammar(word, to_language = "English", from_language = "Spanish"):
    prompt = Prompt(word = word, to_language = to_language, from_language = from_language)   
    chat_completion = client.chat.completions.create(model="gpt-3.5-turbo",
                                                   messages=[{"role": "system", "content": prompt.text},
                                                             {"role": "user", "content": "The word is: " + word}], 
                                                    tools = [{
            "type": "function",
            "function": {
                "name": "return_entry",
                "description": "Get dictionary entry of a word.",
                "parameters" :     prompt.schema
            }}])
    return chat_completion.choices[0].message.tool_calls[0].function.arguments

Грамматика предлагает модели вызвать функцию с определенным набором параметров. В нашем случае эта функция return_entries которая не делает ничего, кроме сборки ответа в словарь. К сожалению, я не нашёл адекватного способа не просить, а требовать определенного формата, как это реализовано в llama_cpp, но и приведённый способ тоже существенно улучшил читаемость ответов.

Я прогнал данный запрос на 1000 слов и наткнулся на неожиданный и неприятный факт: запросы выходили существенно дороже, чем я ожидал (сказывалось использование грамматики, практически удваивавшей число токенов в запросе). При этом 80% токенов, которые я использовал в этом тексте, пришлись на токены промпта, а не токены ответа. Вариант сокращать промпт я рассматривать не мог -- он и так достаточно лаконичен. Поэтому я решил попробовать улучшить соотношение токенов промпта и ответа, получая несколько ответов в одном запросе.

Во-первых, это потребовало усложнения промпта и схемы ответа. Теперь мы просим вернуть не словарную статью, а список из нескольких (методом научного тыка было установлено оптимальное число равное двадцати) словарных статей:

from pydantic import conlist 

class PromptMultiple():

    def __init__(self, words, from_language = "Spanish", to_language = "English"):
      #примеры
        ex1 = """{'translation': 'work (noun); job (noun); to work (noun)',
 'example_1': 'No puedo soportar cuando otros están trabajando. Voy a acostarme.',
 'example_2': 'El trabajo no es un lobo - en el bosque no se escapa.',
 'example_translation_1': 'I can't stand still when others are working. I'm going to bed.',
 'example_translation_2': 'Work is not a wolf - it does not run away in the forest.'}"""
        ex2 = """{'translation': 'new (adj)',
 'example_1': 'Mira como un carnero a la nueva puerta.',
 'example_2': 'Una nueva escoba barre en una nueva.',
 'example_translation_1': 'Look like a ram at the new door.',
 'example_translation_2': 'A new broom sweeps as a new one.'}"""
        
        self.text = """
Instruction:
You get a sequence of {from_language} words: {words}. You need to prepare their dictionary entries. List their possible translations to {to_language}. List as much translations as possible, preferrably all of them. Order the list in descending order of likelihood (commonness) of translation. After each word indicate the part of speech as it is done in dictionaries (in {from_language}, please!). Then provide two different sentences in {from_language} in which the words are used and their translations to {to_language}. 
if the word has several translations, the example sentences should employ different meanings of the word. Leave no additional comment. Do not include translations of the sentences to English. If you can not translate a word, return empty strings for this particular word only.
Return a valid JSON to pass into a function for preparing dictionary entries. JSON must include an entry for each word in a sequence.
The format is the following: {{"word_entry_1" : {{"translation" : "", "example_1" : "", "example_2" : "", "example_translation_1" : "", "example_translation_2" : ""}}, ..., "word_entry_n" : {{"translation" : "", "example_1" : "", "example_2" : "", "example_translation_1" : "", "example_translation_2" : ""}}}} for each of N words in a sequence. Ensure that the format is followed.
Example:
[user input]: ["trabajo", "nuevo"]
[your response]:
"word_entry_1" : {ex1},
"word_entry_2" : {ex2}
""".format(to_language = to_language, from_language = from_language, ex1 = ex1, ex2 = ex2, words = str(words))

        #схема словарной статьи
        class WordEntry(BaseModel):
            translation: str = Field(..., description=f"List of all possible translations of the input word from {from_language} to {to_language}.")
            example_1: str = Field(..., description=f"An example of a sentence in which the input word is used in {from_language}.")
            example_2: str = Field(..., description=f"An example of a sentence in which the input word is used in {from_language}. Should be different from example_1.")
            example_translation_1: str = Field(..., description=f"Translation of example_1 from {from_language} to {to_language}.")
            example_translation_2: str = Field(..., description=f"Translation of example_2 from {from_language} to {to_language}.")

        #схема списка словарных статей
        class Grammar(BaseModel):
            word_entry_1: WordEntry = Field(..., description = f"Dictionary entry for {words[0]}")
            word_entry_2: WordEntry = Field(..., description = f"Dictionary entry for {words[1]}")
            # <///> - сократил для статьи
            word_entry_19: WordEntry = Field(..., description = f"Dictionary entry for {words[18]}")
            word_entry_20: WordEntry = Field(..., description = f"Dictionary entry for {words[19]}")

        self.schema = Grammar.schema()
        self.word_schema = WordEntry.schema()

def return_entries(word_entry_1, word_entry_2, word_entry_3, word_entry_4, word_entry_5, word_entry_6, word_entry_7, word_entry_8, word_entry_9, word_entry_10,
                   word_entry_11, word_entry_12, word_entry_13, word_entry_14, word_entry_15, word_entry_16, word_entry_17, word_entry_18, word_entry_19, word_entry_20
                  ):
    """Get dictionary entry of a sequence of words."""
    
    return [{"translation" : entry.translation, "example_1" : entry.example_1, "example_2" : entry.example_2, "example_translation_1" : entry.example_translation_1, "example_translation_2" : entry.example_translation_2} for entry in [word_entry_1, word_entry_2, word_entry_3, word_entry_4, word_entry_5]]

Во-вторых, итеративным процессом ~~разбазаривания личных фондов на исследование~~ самообучения было выяснено, что ответы склонны к впадению в одну из ересей:

Возвращение строки, не соответствующей схеме, которую мы запрашиваем.
Возвращение схемы, все значения в которой -- пустые строки. Здесь есть подозрение, что такое случается, когда в запросе есть несколько слов, значения которых модель не знает (в таком случае мы просим заполнить статью пустыми строками), и модель не заморачиваясь заполняет все словарные статьи в наборе пропусками.

Чтобы сразу выловить такие ошибки, напишем некоторые вспомогательные функции:

from jsonschema import validate

# Функция, которая выкидывает ошибку, если ответ не соответствует формату
def validate_output_error(model_response, bunch_size):
    schema = {
        "type" : "object",
        "properties" : {
           f"word_entry_{n}": {
          "type": "object",
          "properties": {
            "translation": {
              "type": "string"
            },
            "example_1": {
              "type": "string"
            },
            "example_2": {
              "type": "string"
            },
            "example_translation_1": {
              "type": "string"
            },
            "example_translation_2": {
              "type": "string"
            }
          },
          "required": [
            "translation",
            "example_1",
            "example_2",
            "example_translation_1",
            "example_translation_2"
          ]
        } for n in range(1, bunch_size + 1)
        }
    }
    if type(model_response) == str:
        model_response = ast.literal_eval(model_response)
    validate(instance = model_response, schema=schema)
    return model_response

# Функция, которая извлекает значения определенного поля из всех статей в наборе
def extract_fields(entry, field):
    entries_list = []
    for word_entry in entry:
        entries_list.append(entry[word_entry][field].replace(",", ";"))
    return entries_list

# Функция, которая проверяет, что поле translation в ответе не заполнено пустыми строками
def validate_empty_translations(model_response):
    if type(model_response) == str:
        model_response = ast.literal_eval(model_response)
        translations = extract_fields(model_response, "translation")
        translations = "".join(translations)
        if translations == "":
            return False
        else:
            return True

Автор понимает ~~тяжесть своих грехов~~ некоторую неэффективность этих проверок (скажем, literal_eval вызывается на одну и ту же строку неоднократно), однако нужно принять во внимание, что на время работы запроса к модели время, занятое работой CPU составляет примерно несколько миллисекунд, остальное время -- простой в ожидании ответа от API. Таким образом, оптимизация локальных вычислений не даст практически никакого выигрыша, так что оставляю как есть.

Сам запрос пришлось немного усложнить, встроив в него валидации:

def get_entry_grammar(words, to_language = "English", from_language = "Spanish"):
    prompt = PromptMultiple(words = words, to_language = to_language, from_language = from_language) 
    chat_completion = client.chat.completions.create(model="gpt-3.5-turbo",
                                                   messages=[{"role": "system", "content": prompt.text},
                                                             {"role": "user", "content": "The words are: " + str(words)}], 
                                                    tools = [{
            "type": "function",
            "function": {
                "name": "return_entries",
                "description": "Get dictionary entry for each word in a list. Used when a translation is requested for a sequence of .",
                "parameters" :     prompt.schema,
                
            }}],
                                                    )

    response = chat_completion.choices[0].message.tool_calls[0].function.arguments
    validate_output_error(response, 20)

    
    if validate_empty_translations(response) == False:
        chat_completion = client.chat.completions.create(model="gpt-3.5-turbo",
                                               messages=[{"role": "system", "content": prompt.text},
                                                         {"role": "user", "content": "You translated nothing, try again. The words are: " + str(words)}], 
                                                tools = [{
        "type": "function",
        "function": {
            "name": "return_entries",
            "description": "Get dictionary entry for each word in a list. Used when a translation is requested for a sequence of .",
            "parameters" :     prompt.schema,
            
        }}],
                                                )
        response = chat_completion.choices[0].message.tool_calls[0].function.arguments
        validate_output_error(response, 20)

    
    return ast.literal_eval(response)

Запрос совершит повторную попытку получения ответа, если ответ заполнен пустыми строками. Только одну, потому что нам лучше потерять некоторое количество слов, чем уйти в бесконечный высасывающий ресурсы цикл повторов запроса там, где есть какие-то объективные причины для такого поведения.

Пример вызова функции:

%%time
get_entry_grammar(['amigo', 'trabajo'
                   , 'pueblo', 'habla', 'unido',  'ministro', 'pasar', 'persona', 'martes', 'primero', 
                   'tras', 'quien', 'después', 'hasta', 'acuerdo', 'prensa', 'si', 'tener', 'domingo', 'partido'
                  ])

И полученного ответа

CPU times: total: 15.6 ms
Wall time: 30.2 s

{'word_entry_1': {'translation': 'friend (noun)',
  'example_1': 'Mi mejor amigo me ayudó en tiempos difíciles.',
  'example_2': 'Me gusta salir con mis amigos los fines de semana.',
  'example_translation_1': 'My best friend helped me in difficult times.',
  'example_translation_2': 'I enjoy going out with my friends on weekends.'},
 'word_entry_2': {'translation': 'work (noun); job (noun); to work (verb)',
  'example_1': 'No puedo soportar cuando otros están trabajando. Voy a acostarme.',
  'example_2': 'El trabajo no es un lobo - en el bosque no se escapa.',
  'example_translation_1': "I can't stand still when others are working. I'm going to bed.",
  'example_translation_2': 'Work is not a wolf - it does not run away in the forest.'},
 'word_entry_3': {'translation': 'town (noun); people (noun)',
  'example_1': 'Vivo en un pequeño pueblo rodeado de naturaleza.',
  'example_2': 'El pueblo se unió para celebrar el Día de la Independencia.',
  'example_translation_1': 'I live in a small town surrounded by nature.',
  'example_translation_2': 'The people came together to celebrate Independence Day.'},
 'word_entry_4': {'translation': 'speech (noun); language (noun)',
  'example_1': 'Su discurso fue inspirador y poderoso.',
  'example_2': 'Él habla varios idiomas con fluidez.',
  'example_translation_1': 'His speech was inspiring and powerful.',
  'example_translation_2': 'He speaks multiple languages fluently.'},
 'word_entry_5': {'translation': 'united (adjective)',
  'example_1': 'La familia estaba unida en su deseo de prosperidad.',
  'example_2': 'Unidos somos más fuertes.',
  'example_translation_1': 'The family was united in their desire for prosperity.',
  'example_translation_2': 'United we are stronger.'},
 'word_entry_6': {'translation': 'minister (noun)',
  'example_1': 'El ministro de educación anunció nuevas reformas.',
  'example_2': 'El primer ministro está dando un discurso en la conferencia.',
  'example_translation_1': 'The Minister of Education announced new reforms.',
  'example_translation_2': 'The Prime Minister is giving a speech at the conference.'},
 'word_entry_7': {'translation': 'to pass (verb); to spend (verb)',
  'example_1': 'Pasaré el fin de semana en la playa con mis amigos.',
  'example_2': 'No puedo pasar sin café por la mañana.',
  'example_translation_1': 'I will spend the weekend at the beach with my friends.',
  'example_translation_2': "I can't go without coffee in the morning."},
 'word_entry_8': {'translation': 'person (noun)',
  'example_1': 'Cada persona tiene sus propias fortalezas y debilidades.',
  'example_2': 'Me encanta conocer a nuevas personas y aprender de ellas.',
  'example_translation_1': 'Every person has their own strengths and weaknesses.',
  'example_translation_2': 'I love meeting new people and learning from them.'},
 'word_entry_9': {'translation': 'Tuesday (noun)',
  'example_1': 'Vamos a tener una reunión importante el martes.',
  'example_2': 'Los martes son mi día favorito de la semana.',
  'example_translation_1': 'We are going to have an important meeting on Tuesday.',
  'example_translation_2': 'Tuesdays are my favorite day of the week.'},
 'word_entry_10': {'translation': 'first (adjective); first (adverb); firstly (adverb)',
  'example_1': 'El primer lugar en el concurso fue para ella.',
  'example_2': 'Primero, necesitamos analizar los datos antes de tomar una decisión.',
  'example_translation_1': 'She got the first place in the competition.',
  'example_translation_2': 'Firstly, we need to analyze the data before making a decision.'},
 'word_entry_11': {'translation': 'after (preposition); behind (preposition)',
  'example_1': 'El gato se escondió tras la cortina.',
  'example_2': 'El coche venía tras el camión.',
  'example_translation_1': 'The cat hid behind the curtain.',
  'example_translation_2': 'The car was coming after the truck.'},
 'word_entry_12': {'translation': 'who (pronoun)',
  'example_1': '¿Sabes quién es el ganador del concurso?',
  'example_2': 'Quien no arriesga, no gana.',
  'example_translation_1': 'Do you know who is the winner of the competition?',
  'example_translation_2': "Whoever doesn't take risks, doesn't win."},
 'word_entry_13': {'translation': 'after (preposition); later (adverb)',
  'example_1': 'Voy a tomar un descanso y después continuar con el trabajo.',
  'example_2': 'Después de la tormenta, sale el sol.',
  'example_translation_1': "I'm going to take a break and then continue with the work.",
  'example_translation_2': 'After the storm, the sun comes out.'},
 'word_entry_14': {'translation': 'until (preposition); to (preposition)',
  'example_1': 'Estudiaré hasta que termine el examen.',
  'example_2': 'Camina hasta la esquina y luego gira a la derecha.',
  'example_translation_1': 'I will study until the exam is finished.',
  'example_translation_2': 'Walk to the corner and then turn right.'},
 'word_entry_15': {'translation': 'agreement (noun)',
  'example_1': 'Llegamos a un acuerdo sobre el precio del coche.',
  'example_2': 'El acuerdo entre las dos partes fue justo y equitativo.',
  'example_translation_1': 'We reached an agreement on the price of the car.',
  'example_translation_2': 'The agreement between the two parties was fair and equitable.'},
 'word_entry_16': {'translation': 'press (noun)',
  'example_1': 'El presidente dio una conferencia de prensa para anunciar nuevas políticas.',
  'example_2': 'La prensa publicó un artículo sobre el evento.',
  'example_translation_1': 'The president gave a press conference to announce new policies.',
  'example_translation_2': 'The press published an article about the event.'},
 'word_entry_17': {'translation': 'if (conjunction)',
  'example_1': 'Si tienes tiempo, podemos salir a tomar algo.',
  'example_2': 'Si estudias, aprobarás el examen.',
  'example_translation_1': 'If you have time, we can go out for a drink.',
  'example_translation_2': 'If you study, you will pass the exam.'},
 'word_entry_18': {'translation': 'to have (verb); to possess (verb)',
  'example_1': 'Tengo hambre, ¿quieres pedir comida?',
  'example_2': 'Ella tiene mucho talento para la música.',
  'example_translation_1': "I'm hungry, do you want to order food?",
  'example_translation_2': 'She has a lot of talent for music.'},
 'word_entry_19': {'translation': 'Sunday (noun)',
  'example_1': 'Me gusta relajarme los domingos y descansar.',
  'example_2': 'Vamos a la iglesia todos los domingos por la mañana.',
  'example_translation_1': 'I like to relax on Sundays and rest.',
  'example_translation_2': 'We go to church every Sunday morning.'},
 'word_entry_20': {'translation': 'party (noun)',
  'example_1': 'Vamos a organizar una fiesta de cumpleaños sorpresa para Juan.',
  'example_2': 'El partido político presentó su plan para mejorar la economía.',
  'example_translation_1': 'We are going to organize a surprise birthday party for Juan.',
  'example_translation_2': 'The political party presented their plan to improve the economy.'}}

Кстати, внимательно вчитавшись в ответ, можно заметить редкую, но систематическую ошибку модели, которая вскрывает забавную вещь о том, как она генерирует ответ.

'word_entry_20': {'translation': 'party (noun)',
'example_1': 'Vamos a organizar una fiesta de cumpleaños sorpresa para Juan.',
'example_2': 'El partido político presentó su plan para mejorar la economía.',
'example_translation_1': 'We are going to organize a surprise birthday party for Juan.',
'example_translation_2': 'The political party presented their plan to improve the economy.'}}

Последнее слово "partido" действительно означает party, но только не party как вечеринка, а party в значении политической партии. В результате в примере модель, очевидно, взяла английское слово party, сгенерировала к нему пример (про вечеринку) и перевела на испанский -- естественно, в испанском примере слово partido исчезло, потому что переводить модель научилась куда лучше, чем составлять систематические ответы. Тем не менее, такого рода ошибка (к тому же, довольно редкая) не критична для моих задач. Куда хуже, что было потеряно второе значение слова partido -- матч/спортивная игра. Что ж -- мы не всесильны.

Ну и, наконец, распараллелим процесс. По ограничению токенов в минуту пролезает приблизительно 10 банчей по 20 слов в каждом.

%%time
import concurrent.futures

def split_dataframe(df, chunk_size = 20): 
    chunks = list()
    num_chunks = len(df) // chunk_size + 1
    for i in range(num_chunks):
        if len(df[i*chunk_size:(i+1)*chunk_size]) > 0:
            chunks.append(df[i*chunk_size:(i+1)*chunk_size])
    return chunks
  
price_test_entries = []
errors = []
words_processed = []
def openai_call(lemma_bunch):
    try:
        price_test_entries.append({"cohort" : cohort, "word" : lemma_bunch, "entry" : get_entry_grammar(lemma_bunch)})
    except Exception as e:
        try:
            price_test_entries.append({"cohort" : cohort, "word" : lemma_bunch, "entry" : get_entry_grammar(lemma_bunch)})
        except Exception as e2:
            errors.append({"cohort" : cohort, "word" : lemma_bunch, "entry" : str(e2)})
    words_processed.append(lemma_bunch)
        
def parallel_openai_requests(prompts):
    with concurrent.futures.ThreadPoolExecutor() as executor:
        # Map the function over the list of prompts in parallel
        results = list(executor.map(openai_call, prompts))

all_chunks = split_dataframe(split_dataframe(all_lemmas, chunk_size = 20), chunk_size = 10)

Здесь у нас в цикле появляется дополнительный параметр -- cohort. Он возникает из-за моего желания стратифицировать тест качества модели.

Валидация метода

Перед тем, как получить полный словарь с модели, желательно её провалидировать. Для этого мы, во-первых, создадим стратифицированную выборку -- случайным образом возьмём 400 слов из первых 100000 по частотности (после очистки) и по 800 слов из когорт 100-200 тысяч и 200-300 тысяч. Слова из средней и нижней когорт мы подвергнем той же чистке, которую проходила выборка топ-100 тысяч, после чего у нас останется 615 и 592 слова в этих когортах соответственно.

У нас всё готово для получения ответов модели для этих полутора тысяч слов. Подставляя список слов соответствующей когорты вместо all_lemmas в расположенном чуть выше коде, получаем тестовые респонсы модели в каждой из когорт. Тестируем чисто техническую работоспособность модели -- модель правда отрабатывает как надо (правда, около 10% банчей улетает в ошибки -- их приходится пропускать через модель несколько раз). Вся операция стоит нам около $1 -- значит, для 60000 слов мы потратим около $40, что близко к нашей оценке.

Тем не менее, хотелось бы валидировать также качество ответов, а не сам факт их получения. Возьмем простейшую метрику -- долю правильных ответов в каждой когорте. К сожалению, определить правильность ответа -- не такая тривиальная задача в отсутствие эталонного словаря. Проверять каждое слово по интернет-словарям вручную оказалось чудовищно медленно. Здесь я планировал написать длинную и грустную повесть о том, как я провёл несколько интересных (нет) вечеров, пытаясь вытрясти что-то внятное из API (да и просто скрэппинга) всех найденных мной публичных словарей, но потом передумал. Короче говоря, я обратился к API Yandex Translate. Качество переводов я нашёл сомнительным, но это было лучшее, что удалось получить (к тому же, они дают 4000 рублей на тестирование своего API новым аккаунтам).

Получив перевод каждого слова в тестовой выборке от Яндекса, я свёл переводы в таблицу такого вида:

Сравнение переводов от Яндекса и от ChatGPT

Что такое coati?

Как этот милаш попал в верхнюю частотную когорту я не знаю

По верхним строкам таблицы мы уже можем наблюдать приятную вещь -- наш перевод кажется в среднем полнее, чем перевод от Яндекса. В частности, Яндекс почему-то не справился с частотным словом tercia и по очевидным причинам со словом puya, которое действительно на каком-то локальном жаргоне означает pointed sting used by toreador. Вопросы к моей выборке слов и анализу частотности в ней усиливаются, но ладно.

Чтобы сверить похожесть переводов, сначала я попытался использовать spacy.

Схожесть строк на основе эмбеддингов spacy

Результат (справа) меня не устроил, поэтому я решил быть проще и использовал библиотеку fuzzywuzzy, основанную на старом-добром расстоянии Левенштейна.

Схожесть строк на основе расстояния Левенштейна

Проведя краткую серию экспериментов, представленную на скриншоте выше, я пришёл к выводу, что коэффициент схожести больше 15 можно считать признаком близких переводов, а больше 30 -- практически гарантией. Это позволило мне вычислить долю хороших переводов в каждой когорте и получить достаточно приятный результат (см. таблицы ниже).

Приведены два варианта подсчета метрики: с исключением непереведенных (т.е. совпадающих в оригинале и в переводе -- возможно, мы теряем ещё часть одинаково пишущихся в двух языках слов) Яндексом слов и по всему списку тестовых слов. В любом случае, нижняя планка корректности в 80% (нижняя потому что визуально значительная часть несовпадений это проблема именно Яндекса) мне кажется весьма неплохой.

Корректность переводов в тестовой выборке в зависимости от порога допустимого расстояния Левенштейна

Корректность переводов в тестовой выборке в зависимости от порога допустимого расстояния Левенштейна при исключении непереведенных Яндексом слов

В серьезном академическом исследовании, конечно, стоило бы найти живого переводчика и заплатить ему за валидацию тестовой выборки, или по крайней мере вручную перепроверить каждое слово с открытыми словарями. Тем не менее, для моих целей я признал валидацию успешной.

Хэппи-энд

Спустя несколько итераций исправления ошибок я получил словарь из 59360 непустых переводов. Это заняло 6 часов и почти ровно $25, что близко к моей изначальной оценке. Кроме того, $12 были потрачены на проверку орфографии в словах.

Репозиторий

Проект выложен под MIT-лицензией по адресу: https://github.com/IvanDubls/llm_dictionary. Я не возражаю против использования как готового словаря, так и кода (не очень, правда, читаемого) в различных проектах (буду, наоборот, рад).

Перспективы

В дальнейшем я планирую расширить словарь, а кроме того заняться изготовлением аналогичных словарей для других языковых пар.

Заключение

Конечно, прогноз LLM носит вероятностный характер, и словарь, составленный живыми экспертами будет корректнее, чем получающийся с помощью данного алгоритма. Тем не менее, такой словарь хорош своей относительной дешевизной. Мне кажется, он может найти своё применение в студенческих проектах и изготовлении MVP. В общем и целом, я считаю, что он удался как proof-of-concept. Я буду очень доволен собой, если он пригодится кому-то и очень рад, если кому-то было интересен какой-то кусочек этой статьи.

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4 февр. 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

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17.07.24 02:26 thompsonrickey

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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

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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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21.11.24 08:02 Emily Hunter

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22.11.24 04:41 [email protected]

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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
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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

Although recovering from the terrible effects of investment fraud can seem like an impossible task, it is possible to regain financial stability and go on with the correct assistance and tools. In my own experience with Wizard Web Recovery, a specialized company that assisted me in navigating the difficulties of recouping my losses following my fall prey to a sophisticated online fraud, that was undoubtedly the case. My life money had disappeared in an instant, leaving me in a state of shock when I first contacted Spartan Tech Group Retrieval through this Email: spartantechretrieval (@) g r o u p m a i l .c o m The compassionate and knowledgeable team there quickly put my mind at ease, outlining a clear and comprehensive plan of action. They painstakingly examined every aspect of my case, using their broad business contacts and knowledge to track the movement of my pilfered money. They empowered me to make knowledgeable decisions regarding the rehabilitation process by keeping me updated and involved at every stage. But what I valued most was their unrelenting commitment and perseverance; they persisted in trying every option until a sizable amount of my lost money had been successfully restored. It was a long and arduous journey, filled with ups and downs, but having Spartan Tech Group Retrieval in my corner made all the difference. Thanks to their tireless efforts, I was eventually able to rebuild my financial foundation and reclaim a sense of security and control over my life. While the emotional scars of investment fraud may never fully heal, working with this remarkable organization played a crucial role in my ability to move forward and recover. For proper talks, contact on WhatsApp:+1 (971) 4 8 7 - 3 5 3 8 and Telegram:+1 (581) 2 8 6 - 8 0 9 2 Thank you for your time reading as it will be of help.
27.11.24 00:39 [email protected]

Although recovering lost or inaccessible Bitcoin can be difficult and unpleasant, it is frequently possible to get back access to one's digital assets with the correct help and direction. Regarding the subject at hand, the examination of Trust Geeks Hack Expert Website www://trustgeekshackexpert.com/ assistance after an error emphasizes how important specialized services may be in negotiating the difficulties of Bitcoin recovery. These providers possess the technical expertise and resources necessary to assess the situation, identify the root cause of the issue, and devise a tailored solution to retrieve the lost funds. By delving deeper into the specifics of Trust Geeks Hack Expert approach, we can gain valuable insights into the nuances of this process. Perhaps they leveraged advanced blockchain analysis tools to trace the transaction history and pinpoint the location of the missing Bitcoins. Or they may have collaborated with the relevant parties, such as exchanges or wallet providers, to facilitate the recovery process. Equally important is the level of personalized support and communication that Trust Geeks Hack Expert likely provided, guiding the affected individual through each step of the recovery effort and offering reassurance during what can be an anxious and uncertain time. The success of their efforts, as evidenced by the positive outcome, underscores the importance of seeking out reputable and experienced service providers when faced with a Bitcoin-related mishap, as they possess the specialized knowledge and resources to navigate these challenges and restore access to one's digital assets. Email.. [email protected]
27.11.24 09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.
01.12.24 17:21 KollanderMurdasanu

REACH OUT TO THEM WhatsApp + 156 172 63 697 Telegram (@)Techcyberforc We were in quite a bit of distress. The thrill of our crypto investments, which had once sparked excitement in our lives, was slowly turning into anxiety when my husband pointed out unusual withdrawal issues. At first, we brushed it off as minor glitches, but the situation escalated when we found ourselves facing login re-validation requests that essentially locked us out of our crypto wallet—despite entering the correct credentials. Frustrated and anxious, we sought advice from a few friends, only to hit a wall of uncertainty. Turning to the vast expanse of the internet felt daunting, but in doing so, we stumbled upon TECH CYBER FORCE RECOVERY. I approached them with a mix of skepticism and hope; after all, my understanding of these technical matters was quite limited. Yet, from our very first interaction, it was clear that they were the experts we desperately needed. They walked us through the intricacies of the recovery process, patiently explaining each mechanism—even if some of it went over my head, their reassurance was calming. Our responsibility was simple: to provide the correct information to prove our ownership of the crypto account, and thankfully, we remained on point in our responses. in a timely fashion, TECH CYBER FORCE RECOVERY delivered on their promises, addressing all our withdrawal and access issues exactly when they said they would. The relief we felt was immense, and the integrity they displayed made me confident in fully recommending their services. If you ever find yourself in a similar predicament with your crypto investments, I wholeheartedly suggest reaching out to them. You can connect with TECH CYBER FORCE RECOVERY through their contact details for assistance and valuable guidance. Remember, hope is only a reach away!
02.12.24 23:02 ytre89

Online crypto investment can seem like a promising opportunity, but it's crucial to recognize that there are no guarantees. My experience serves as a stark reminder of this reality. I was drawn in by the allure of high returns and the persuasive marketing tactics employed by various brokers. Their polished presentations and testimonials made it seem easy to profit from cryptocurrency trading. Everything appeared to be legitimate. I received enticing messages about the potential for substantial gains, and the brokers seemed knowledgeable and professional. Driven by excitement and the fear of missing out, I invested a significant amount of my savings. The promise of quick profits overshadowed the red flags I should have noticed. I trusted these brokers without conducting proper research, which was a major mistake. As time went on, I realized that the promised returns were nothing but illusions. My attempts to withdraw funds were met with endless excuses and delays. It became painfully clear that I had fallen victim. The reality hit hard: my hard-earned money was gone, I lost my peace of mind and sanity. In my desperation, I sought help from a company called DEFTRECOUP. That was the turning point for me as I had a good conversation and eventually filed a complaint via DEFTRECOUP COM. They were quite delicate and ensured I got out of the most difficult situation of my life in one piece.
04.12.24 22:24 andreygagloev

When I first heard about Bitcoin back in 2018, I was skeptical. The idea of a decentralized, digital currency seemed too good to be true. But I was intrigued as I learned more about the technology behind it and its potential. I started small, investing just a few hundred dollars, dipping my toes into the cryptocurrency waters. At first, it was exhilarating to watch the value of my investment grow exponentially. I felt like I was part of the future, an early adopter of this revolutionary new asset. But that euphoria was short-lived. One day, I logged into my digital wallet only to find it empty - my Bitcoin had vanished without a trace. It turned out that the online exchange I had trusted had been hacked, and my funds were stolen. I was devastated, both financially and emotionally. All the potential I had seen in Bitcoin was tainted by the harsh reality that with decentralization came a lack of regulation and oversight. My hard-earned money was gone, lost to the ether of the digital world. This experience taught me a painful lesson about the price of trust in the uncharted territory of cryptocurrency. While the technology holds incredible promise, the risks can be catastrophic if you don't approach it with extreme caution. My Bitcoin investment gamble had failed, and I was left to pick up the pieces, wiser but poorer for having placed my faith in the wrong hands. My sincere appreciation goes to MUYERN TRUST HACKER. You are my hero in recovering my lost funds. Send a direct m a i l ( muyerntrusted ( @ ) mail-me ( . )c o m ) or message on whats app : + 1 ( 4-4-0 ) ( 3 -3 -5 ) ( 0-2-0-5 )
12.12.24 00:35 amandagregory

HOW TO HIRE A HACKER TO RECOVER STOLEN BITCOIN WITH FASTFUND RECOVERY
12.12.24 00:35 amandagregory

HOW TO HIRE A HACKER TO RECOVER STOLEN BITCOIN WITH FASTFUND RECOVERY... A few months ago, I made a huge mistake. I invested in what seemed like a legitimate crypto opportunity, only to find out I’d been scammed. I lost a significant amount of money, and the scam platform vanished overnight. I felt completely lost.I had heard of Fastfund Recovery and decided to reach out, even though I was skeptical. From the first conversation, they made me feel heard and understood. They explained the recovery process clearly and kept me updated every step of the way.Within weeks, Fastfund Recovery successfully to recovered my lost funds—something I honestly didn’t think was possible. Their team was professional, transparent, and genuinely caring. I can’t thank them enough for turning a nightmare into a hopeful outcome. If you’re in a similar situation, don’t hesitate to contact them. They truly deliver on their promises. Gmail::: fastfundrecovery8(@)gmail com .....Whatsapp ::: 1::807::::500::::7554
19.12.24 17:07 rebeccabenjamin

USDT RECOVERY EXPERT REVIEWS DUNAMIS CYBER SOLUTION It's great to hear that you've found a way to recover your Bitcoin and achieve financial stability, but I urge you to be cautious with services like DUNAMIS CYBER SOLUTION Recovery." While it can be tempting to turn to these companies when you’re desperate to recover lost funds, many such services are scams, designed to exploit those in vulnerable situations. Always research thoroughly before engaging with any recovery service. In the world of cryptocurrency, security is crucial. To protect your assets, use strong passwords, enable two-factor authentication, and consider using cold wallets (offline storage) for long-term storage. If you do seek professional help, make sure the company is reputable and has positive, verifiable reviews from trusted sources. While it’s good that you found a solution, it’s also important to be aware of potential scams targeting cryptocurrency users. Stay informed about security practices, and make sure you take every step to safeguard your investments. If you need help with crypto security tips or to find trustworthy resources, feel free to ask! [email protected] +13433030545 [email protected]