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Н Новости

Отличается ли внимание человека и модели-трансформера?

Для того, чтобы понимать язык и делать различные выводы, человек рассуждает, опираясь на знания о мире и здравый смысл. Несмотря на то, что большие языковые модели достигли значительных успехов в обработке естественного языка, рассуждение на основе здравого смысла остаëтся одним из самых сложных навыков.

Наиболее распространëнным способом оценки способностей моделей рассуждать, опираясь на здравый смысл, является тест на основе схемы Винограда (The Winograd Schema Challenge, или WSC), названный в честь Терри Винограда, профессора компьютерных наук в Стэнфордском университете. Тест основан на разрешении синтаксической неоднозначности.

Давайте рассмотрим пример из схемы Винограда:

«Кубок не помещается в коричневый чемодан, потому что он слишком большой.

Что в этом случае является слишком большим: чемодан или кубок? Для человека ответ является очевидным, а для модели?..

Мы расскажем про наше исследование, в котором сравнили внимание человека и модели, а также проанализировали, на какие слова при решении схемы Винограда обращают внимание человек и модель. Хотя внимание человека и внимание трансформера кажутся совершенно разными, отдельные результаты говорят о взаимосвязи между ними.

Внимание человека и внимание трансформеров

Внимание — это явление, связанное как с чтением у людей, так и с обработкой естественного языка большими языковыми моделями. В обоих случаях внимание используется для поиска важной информации, необходимой для различных рассуждений и выводов.

В связи с этим возникает вопрос о том, как взаимосвязаны внимание человека и модели, существуют ли у них общие паттерны при обработке текста.

Для такого анализа необходимо разобраться с тем, что можно использовать для описания внимания человека и модели-трансформера.

Как известно, важной деталью современных языковых моделей на основе архитектуры «трансформер» является механизм self-attention, заключающийся в поиске взаимосвязей между токенами входных данных. Подробнее о том, как устроены модели на основе этой архитектуры и механизм self-attention, можно прочитать тут.

Если коротко, суть self-attention состоит в определении того, какие слова важны друг для друга в данном контексте. То есть на какие слова мне сейчас следует обратить внимание.

Encoder и Matrix Calculation of Self-Attention. Источник: https://jalammar.github.io/illustrated-transformer/

В рамках нашего исследования в качестве характеристики внимания моделей мы возьмëм предобученные веса с self-attention-слоëв энкодеров.

Теперь возникает вопрос, как описать внимание человека? Здесь на помощь приходит явное (визуальное) внимание, которое можно описать с помощью наблюдения за движением глаз. Данные о движении глаз часто используются для изучения когнитивных процессов мозга, связанных с чтением. В нашем исследовании для описания внимания человека мы использовали данные о длительности и количестве фиксаций взгляда на отдельных словах в процессе чтения и выполнения задания.

Информация о перемещении взгляда собирается с помощью специального оборудования, эта процедура называется видеоокулография. Участник исследования располагается перед экраном, а видеоокулограф фиксирует движения глаз.

Видеоокулография

Итак, мы разобрались, что собираемся использовать в качестве описания такого явления, как внимание человека и внимание трансформерной модели. Теперь необходимо выдвинуть гипотезы, которые мы хотим проверить, а именно:

Существует ли взаимосвязь между вниманием человека и вниманием трансформера при решении схемы Винограда?
Меняется ли эта взаимосвязь в зависимости от слоя модели?
Для модели и для человека показатель внимания одинаков на всëм контексте или выше на отдельных словах?

В случае наличия взаимосвязи между вниманием трансформерной модели и зрительным вниманием человека можно выдвинуть гипотезу о возможности использования информации о движении глаз для аппроксимации внимания трансформеров.

Перед тем как перейти к основным экспериментам, рассмотрим задачу на основе схемы Винограда более подробно и разберëмся в терминологии.

Постановка задачи

Схема Винограда основана на разрешении кореференции в текстах. Задача разрешения кореференции состоит в идентификации выражений в тексте, которые относятся к одной и той же сущности. В данном случае задача представляет собой бинарную классификацию, в которой необходимо определить, относится ли местоимение (анафор) к антецеденту (слово или словосочетание).

В примере выше местоимение «он» является анафорой, а существительное «кубок» — антецедент.

Данные

Для эксперимента были отобраны 148 уникальных предложений из набора данных Winograd, входящего в бенчмарк TAPE. Каждое предложение содержит анафорическое местоимение, к которому задаются два вопроса: про правильный и неправильный антецедент. Таким образом, итоговый набор данных EyeWino состоит из 296 пар предложение-вопрос.

Пример с вопросом про правильный антецедент:

text: Надо заметить, что Зубр владел высшим искусством экспериментатора — <anafor> умел задавать природе вопросы, на которые она должна была ответить да или нет.
question: Имеется ли в виду под выделенным местоимением <antecedent>?
anafor: он
antecedent: Зубр
answer: да
label: 1

Тот же пример, но с вопросом про неправильный антецедент:

text: Надо заметить, что Зубр владел высшим искусством экспериментатора — <anafor> умел задавать природе вопросы, на которые она должна была ответить да или нет.
question: Имеется ли в виду под выделенным местоимением <antecedent>?
anafor: он
antecedent: экспериментатора
answer: нет
label: 0

Эксперименты

Внимание человека

Сбор данных о движении глаз

Данные о движении глаз собирала исследовательская группа профессора Ольги Драгой НИУ ВШЭ. Каждому участнику эксперимента на экране показывали предложение с анафорическим местоимением, которое было выделено красным цветом. После каждого предложения шëл вопрос о предполагаемом антеценденте анафора. На каждое предложение приходилось от 38 до 50 человек. Всего в эксперименте приняли участие 100 человек.

От участников исследования требовалось прочитать предложение, содержащее анафорическое местоимение, и ответить на вопрос об антеценденте с использованием клавиатуры: клавиша 1 — если они согласны с предположением в вопросе, клавиша 0 — если не согласны. В процессе эксперимента проводилась видеоокулография для отслеживания движения глаз.

Пример:

Надо заметить, что Зубр владел высшим искусством экспериментатора — он умел задавать природе вопросы, на которые она должна была ответить да или нет. Имеется ли в виду под выделенным местоимением Зубр? Если вы считаете, что да, нажмите на клавиатуре 1 если вы считаете, что нет, нажмите на клавиатуре 0

В результате эксперимента были собраны наблюдения за движением глаз, которые представляют собой координаты фиксаций взгляда человека на экране.

Характеристики внимания

На основе полученных координат были рассчитаны различные характеристики внимания человека, такие как длительность и число фиксаций участников исследования на отдельных словах при выполнении задания.

В качестве целевых характеристик внимания человека использовали:

Fixations — общее количество фиксаций;
Total reading time — общая продолжительность прочтения слова;
Gaze duration — продолжительность первого прочтения слова.

Наблюдения были нормализованы путëм расчëта доли внимания, уделяемого каждому слову в предложении отдельным человеком. Полученные результаты усреднялись для каждого слова по всем участникам.

Внимание трансформеров

Модели

Для нашего исследования мы использовали различные архитектуры трансформеров с предобученными чекпоинтами.

Предобученные модели для русского языка:

ruBERT-base: encoder-only, 178M parameters, 12 layers, 12 heads
ruRoBERTa-large: encoder-only, 355M parameters, 24 layers, 16 heads
ruT5-base: encoder-decoder, 222M parameters, 12 layers, 12 heads

Мультиязычные модели:

mBERT-base: encoder-only, 178M parameters, 12 layers, 12 heads
XLM-R-large: encoder-only, 560M parameters, 24 layers, 16 heads
mT5-base: encoder-decoder, 222M parameters, 12 layers, 12 heads

Данные для обучения

Каждый из чекпоинтов дополнительно дообучили на задаче WSC с использованием различных открытых данных. Для дообучения всех моделей использовались два набора на русском языке: TAPE: Winograd и MERA: RWSD.

Для дообучения мультиязычных моделей дополнительно использовали XWINO — мультиязычный набор, содержащий схемы Винограда на 6 языках.

Собранные данные привели к заданному формату, а также отфильтровали. Из всех наборов отбросили примеры, в которых анафора не является местоимением. Из мультиязычного набора исключили китайский и японский языки, так как нет возможности применить к ним uppercase, а также русский язык, так как он дублирует данные из RWSD. Ещë из всех наборов исключили дубли, если такие имелись.

В результате фильтрации в финальный набор для обучения вошли следующие выборки:

Language	Train	Validation
English	2846	1216
French	108	56
Portuguese	358	116
Russian	872	326

Формат данных

Формат входных данных повторял эксперимент для человека за исключением того, что анафорическое местоимение выделялось не красным цветом, а прописными буквами:

text = text.format(anafor=anafor.upper())

question = question.format(antecedent=antecedent)

input_text = text + question

Пример:

Надо заметить, что Зубр владел высшим искусством экспериментатора — ОН умел задавать природе вопросы, на которые она должна была ответить да или нет. Имеется ли в виду под выделенным местоимением Зубр?

Тюнинг T5-моделей проходил с использованием головы для языкового моделирования, для энкодерных архитектур (BERT, RoBERTa) использовалась голова для классификации, а также [SEP] токен между исходным предложением и вопросом к нему.

Характеристики внимания

Как было сказано выше, в качестве описания внимания моделей мы решили использовать предобученные веса с self-attention слоëв энкодеров. Механизм self-attention представляет собой:

Формула self-attention в архитектуре transformer

Мы использовали выходные данные функции softmax с self-attention слоëв энкодера до того, как они были умножены на матрицу V, чтобы получить долю внимания, которую каждый токен уделил всем остальным.

Значение внимания для каждого токена рассчитывалось усреднением векторов по головам и строкам матрицы. Специальные токены использовали для расчëта внимания, но исключили из окончательного вектора внимания. Полученные веса токенов суммировали по отдельным словам в предложении для того, чтобы получить значение внимания для отдельных слов. Внимание также нормализовали по предложениям путëм расчëта доли внимания, уделяемого каждому слову.

В результате получили один вектор, представляющий собой количество внимания, уделяемого каждому слову всеми остальными словами в предложении.

Результаты

Корреляции

Для расчëта статистической взаимосвязи между полученными нормализованными оценками внимания человека и внимания трансформера использовалась корреляция Спирмена. Этот коэффициент варьируется от -1 до 1, при этом 0 означает отсутствие корреляции. Положительные корреляции подразумевают, что с увеличением одной переменной следует увеличение второй.

Таблица 1. Максимальные значения коэффициентов корреляции Спирмена между вниманием человека и вниманием энкодеров. Layer – слой модели, на котором наблюдается максимальная корреляция.

Model	Layer	Gaze duration	Fixations	Total reading time
ruBERT-base, pre-trained	1	0,606	0,601	0,592
ruBERT-base, tuned	1	0,608	0,603	0,595
mBERT-base, pre-trained	1	0,683	0,684	0,674
mBERT-base, tuned	1	0,683	0,684	0,673
ruRoBERTa-large, pre-trained	16	0,551	0,543	0,538
ruRoBERTa-large, tuned	16	0,555	0,542	0,542
XLM-R-large, pre-trained	14	0,605	0,592	0,593
XLM-R-large, tuned	17	0,595	0,588	0,582
ruT5-base, pre-trained	1	0,605	0,593	0,587
ruT5-base, tuned	1	0,606	0,594	0,588
mT5-base, pre-trained	9	0,63	0,619	0,611
mT5-base, tuned	1	0,58	0,573	0,561

Исходя из результатов, представленных в таблице, можно сделать вывод о существовании умеренной корреляции между вниманием трансформеров и вниманием человека. Наибольшие значения корреляций для большинства чекпоинтов наблюдается с показателем gaze duration — длительность первого прочтения слова.

Значение коэффициентов корреляции Спирмена между человеческим вниманием и вниманием моделей на различных слоях. Pre-trained — чекпоинты без тюна на схеме Винограда, tuned — чекпоинты после тюна на схеме Винограда.

Из графиков выше можно сделать следующие выводы:

Для отдельного взятого чекпоинта модели наблюдаются схожие корреляции с разными характеристиками внимания человека.
Для каждой русскоязычной модели на большинстве слоëв наблюдаются схожие корреляции как для тюнов, так и для претрейнов. Различия в корреляциях незначительны.
Для мультиязычной модели mT5-base тренд претрейна не сохраняется для тюнов.
Разница в коррелициях между мультиязычными и русскоязычными моделями для одной архитектуры может быть обусловлена сдвигами в распределении данных. В корпусе для мультиязычного тюнинга 74% данных представлены на английском языке, тогда как тестовая выборка состоит только из текстов на русском языке.

Визуализация «важных» слов

Давайте посмотрим, на какие слова обращает внимание человек и модель на примере ruRoBERTa-large. Из таблицы 1 следует, что высокие корреляции наблюдаются на 16-м слое претрейна с показателем first reading time.

Для того, чтобы определить важные слова, для каждого предложения мы отобрали 25% слов с наибольшим весом внимания.

Рассмотрим примеры с одним предложением, но разными вопросами. На примере ниже выделены слова, которые человек считает наиболее важными:

ответ «Нет»

В выпученных глазах её сияла гордость добытчицы, КОТОРАЯ исполнила своё предназначение и через то обрела гармонию с собой и с миром Имеется ли в виду под выделенным местоимением гордость?

ответ «Да»

В выпученных глазах её сияла гордость добытчицы, КОТОРАЯ исполнила своё предназначение и через то обрела гармонию с собой и с миром Имеется ли в виду под выделенным местоимением добытчицы?

Важные слова для ruRoBERTa-large, pre-trained, layer 16:

ответ «Нет»

В выпученных глазах её сияла гордость добытчицы, КОТОРАЯ исполнила своё предназначение и через то обрела гармонию с собой и с миром Имеется ли в виду под выделенным местоимением гордость?

ответ «Да»

В выпученных глазах её сияла гордость добытчицы, КОТОРАЯ исполнила своё предназначение и через то обрела гармонию с собой и с миром Имеется ли в виду под выделенным местоимением добытчицы?

Можно сделать вывод, что в данном случае человек и модель обращают внимание на схожие слова, а именно на местоимение и глагол, который относится к данному местоимению, а также на оба антецедента в самом тексте («гордость» и «добытчицы»). Отличием является то, что модель не придаëт особого внимания неправильному антецеденту в вопросе в отличие от человека.

Интеграция человеческого внимания в обучение трансформеров

В результате анализа были обнаружены значимые корреляции между человеческим вниманием и вниманием моделей в задаче разрешения анафоры. Можно предположить, что использование данных о движении глаз при обучении моделей для этой задачи поможет повысить их точность. Исходя из этого, мы провели эксперименты по интеграции данных о движении глаз в процесс обучения моделей, на основе подхода, предложенного в работе Bensemann et al. (2022).

Мы добавили дополнительное слагаемое в функцию потерь, чтобы приблизить внимание модели к человеческому вниманию:

Функция потерь при обучении

H(y, yˆ) — кросс-энтропия, которая измеряет производительность модели в задаче разрешения анафоры.

H(p,pˆ) — кросс-энтропия, которая измеряет разницу между двумя распределениями вероятностей: распределением значений внимания модели на определëнном слое и распределением относительной важности слов человека.

В качестве данных для обучения мы используем собранный набор данных о движении взгляда, а набор RWSD из MERA — в качестве тестовых данных.

Таблица 2. Значения Accuracy моделей в задаче разрешения анафоры при интеграции человеческого внимания в процессе обучения моделей. Layer — слой модели, на котором наблюдается максимальная корреляция.

Model	Layer	Without integration	Gaze duration	Fixations	Total reading time
ruBERT-base, pre-trained	1	55,77	56,15	55,77	55,77
ruBERT-base, tuned	1	58,08	58,08	58,08	58,08
mBERT-base, pre-trained	1	54,62	53,08	56,15	56,15
mBERT-base, tuned	1	56,15	55,38	56,92	55,00
ruRoBERTa-large, pre-trained	16	56,92	53,85	58,08	56,92
ruRoBERTa-large, tuned	16	55,38	55,38	55,38	55,77
XLM-R-large, pre-trained	14	55,00	50,38	55,38	54,23
XLM-R-large, tuned	17	54,62	53,46	52,31	56,92
ruT5-base, pre-trained	1	52,69	56,54	61,15	57,31
ruT5-base, tuned	1	55,77	54,62	54,62	52,31
mT5-base, pre-trained	9	53,08	54,23	53,08	54,62
mT5-base, tuned	1	58,46	58,08	58,85	59,23

На основании результатов можно сделать вывод, что использование внимания человека, описанного показателями Fixations и Reading time, улучшает производительность большинства моделей. Однако результаты не показали значительного прироста точности, что указывает на необходимость дальнейшего исследования способов включения человеческого внимания в обучение трансформеров.

Заключение

Эту работы мы делали в сотрудничестве с коллегами из НИУ ВШЭ, лабораторией нейролингвистики и команды Ольги Драгой. В статье мы сравнили, как люди и модели на основе архитектуры «трансформер» воспринимают и обрабатывают информацию с точки зрения внимания при решении схемы Винограда.

Статья была представлена на мастер-классе ICML международной конференции ACL в Бангкоке. Такого рода фундаментальные исследования открывают новые возможности для исследования трансформеров и, как следствие, улучшение приложений на их основе. Человеческое внимание потенциально может быть использовано для аппроксимации внимания трансформера с целью снижения вычислительных издержек.

Подписывайтесь на Telegram-канал SaluteAI, в котором мы с коллегами делимся интересными материалами и наработками по машинному обучению.

Авторы

Команда AGI NLP: Козлова Анастасия, Ахметгареева Альбина (@Colindonolwe), Феногенова Алена (@alenusch)

Спасибо коллегам из НИУ ВШЭ, профессору Ольге Драгой, Айгуль Хановой, Семену Кудряшову за создание eye-tracking сета.

Ccылки на литературу

Наша статья с ACL Transformer Attention vs Human Attention in Anaphora Resolution
Наш набор данных о движении взгляда EyeWino
Статья The Winograd Schema Challenge
Статья Attention Is All You Need

Использованные наборы на основе схемы Винограда:

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30 авг. 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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23.09.24 18:56 matthewshimself

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01.10.24 14:54 Sinewclaudia

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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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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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