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Как мы искали лучшие способы классификации

Всем привет!

В предыдущих статьях мы уже рассказывали о том, какими метриками можно пользоваться для оценки ответов AI-продуктов.

В большом количестве метрик для решения такой задачи предварительно надо оценить, к какой категории относится тот или иной ответ.

В этой статье мы преследовали две цели:

На примере показать, как применяются такие метрики и как с помощью них можно оценить качество работы модели.
Провести небольшое исследование по различным AI-продуктам с целью выявления наиболее оптимальных для решения задач классификации.

Допустим, у нас есть какой-то случай, который мы хотим оценить на предмет отношения к понятию реальный ущерб. Скажем, автомобиль получил повреждения вследствие падения на него снега с крыши дома. Или, например, вследствие задержки возврата грузовой техники арендатором компания не смогла принять заказ на перевозку ценного груза.

В первом случае с поврежденным автомобилем это будет реальный ущерб, а во втором уже упущенная выгода, которая является самостоятельным видом убытков наряду с ущербом.

Каким образом можно организовать классификацию таких кейсов?

Можно выслушать каждого пришедшего клиента и сформировать представление о том, относится ли такой случай к убыткам вообще и к какой именно категории, в частности.

Плюсы подхода:
- можно составить мнение о клиенте;
- можно узнать важные подробности рассматриваемого случая
Минусы подхода:
- затраты времени на информацию, которую можно получить заранее.
Можно организовать классификацию в автоматизированном виде на основании предварительного анкетирования клиента с помощью ключевых фраз (например, давать только определенные варианты ответа на вопросы).
Впоследствии с помощью таких фраз автоматизировать определение категории убытков и приступить к формированию позиции по делу.

Плюсы:
- экономия времени на общее ознакомление с делом в случае верного заполнения анкеты
Минусы:
- клиент часто не знает, какие конкретно факты необходимы в рассматриваемом случае для правильной юридической классификации.
- клиент может одни и те же события рассказывать совершенно разными словами и не знать, относится ли его случай к повреждению имущества или его утрате, а, может, это и вовсе упущенная выгода.
Можно использовать имеющиеся в доступе AI-продукты для выполнения описанной задачи.

Плюсы:
- экономия времени на общее ознакомление с делом в случае верной классификации.
Минусы:
- модель может не обладать информацией в такой специализированной теме, как российское право, в результате чего ее ответы могут не иметь ничего общего с реальностью.
Можно создать собственную модель для классификации подобных случаев.

Плюсы:
- экономия времени на общее ознакомление с делом
Минусы:
- первоначальные временные затраты на создание набора качественных данных и обучение модели.
- низкое качество работы модели в случае низкого качества подготовленных данных.
Можно использовать уже имеющиеся на рынке модели классификации

Плюсы:
- не нужно тратить время на подготовку данных и на обучение собственной модели;
- обладают высокой степенью качества при решении задач классификации.
Минусы:
- могут не поддерживать язык, текст на котором предстоит классифицировать;
- могут не обладать информацией из конкретной специализированной темы.

Среди всех представленных способов по точности классификации и гибкости в обрабатываемых формулировках самыми оптимальным выглядят последние два варианта.

Давайте рассмотрим, каким образом можно обучить и применить собственную модель для решения подобных задач.

Обучение собственной модели

Для достижения положительного результата нам необходим самый важный элемент, а именно набор качественных данных в достаточном количестве.

Что можно считать достаточным количеством данных?

по результатам различных исследований называются размеры выборки в диапазоне от 100 до 50000 примеров. Такой разброс обусловлен разными задачами, для решения которых необходимо обучить модель.

Для целей конкретного примера (относится или не относится конкретный случай к понятию реального ущерба) требуется модель с бинарной классификацией.

Подготовка выборки в размере 50000 примеров слишком велика для решения такой задачи.

Мы при подготовке модели для решения простых случаев ограничились 1000, но позднее дополнили еще 200 примерами.

Все примеры готовились следующим образом: в 10 разных категориях было приготовленно по 100 примеров, 50 из которых были положительными (относились к понятию реального ущерба), а другие 50 были, соответственно, отрицательными.

Все примеры были собраны в единую таблицу с заголовками:text, real damage. В колонке text записан текст примера, в колонке real damage - показатели 1 или 0, в зависимости от принадлежности или не принадлежности к реальному ущербу.

Примеры выглядят примерно так.

text	real_damage
Сломанный рентгеновский аппарат уничтожил результаты исследования	1
Сломался старый компьютер, но информация была восстановлена	0
Подрядчик повредил несущую стену здания, требуется восстановление	1
Строительство затянулось, и клиент потерял потенциальных покупателей	0
Водитель не справился с управлением и разбил чужую машину	1
Водитель фуры задержался в пути и потерял бонус от заказчика	0
Из-за сбоя биржи инвестор потерял доступ к своим активам	1
Банковский вклад не принес ожидаемой прибыли из-за инфляции	0
В химчистке испортили дорогое пальто	1
Из-за дефицита товаров в магазине не оказалось нужного продукта	0

В нашем случае таблица была сохранена в виде csv файла. Необходимо отметить, что модели очень чувствительны к корректности подготовленных данных. Отсутствие разделителей или наличие лишних разделителей приводит к неудовлетворительному результату обучения.

В качестве модели для обучения выбрана “DeepPavlov/rubert-base-cased”. Эта модель уже предварительно обучена на текстах российского сегмента Википедии и новостных статей.

Код для обучения модели выглядит следующим образом:

import pandas as pd
import torch
import numpy as np
from transformers import (
    AutoTokenizer,
    AutoModelForSequenceClassification,
    Trainer,
    TrainingArguments,
    EarlyStoppingCallback,
    DataCollatorWithPadding
)
from sklearn.model_selection import train_test_split
from sklearn.metrics import (
    accuracy_score, 
    precision_recall_fscore_support,
    confusion_matrix,
    classification_report
)
from datasets import Dataset
import matplotlib.pyplot as plt
import seaborn as sns

# === 1. Загрузка данных ===
data = pd.read_csv(r"путь к файлу с данными")

# Разделяем данные
train_texts, val_texts, train_labels, val_labels = train_test_split(
    data["text"], data["real_damage"], test_size=0.2, random_state=42, stratify=data["real_damage"]
)

train_data = Dataset.from_dict({"text": train_texts.tolist(), "label": train_labels.tolist()})
val_data = Dataset.from_dict({"text": val_texts.tolist(), "label": val_labels.tolist()})

# === 2. Токенизация ===
model_name = "DeepPavlov/rubert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(model_name)

def tokenize_data(batch):
    return tokenizer(batch["text"], padding=False, truncation=True, max_length=512)  # Dynamic padding

train_data = train_data.map(tokenize_data, batched=True, remove_columns=["text"])
val_data = val_data.map(tokenize_data, batched=True, remove_columns=["text"])

train_data.set_format(type="torch", columns=["input_ids", "attention_mask", "label"])
val_data.set_format(type="torch", columns=["input_ids", "attention_mask", "label"])

# === 3. Модель ===
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)

# === 4. Метрики ===
def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    
    accuracy = accuracy_score(labels, predictions)
    precision, recall, f1, _ = precision_recall_fscore_support(
        labels, predictions, average='binary'
    )
    
    return {
        "accuracy": accuracy,
        "precision": precision,
        "recall": recall,
        "f1": f1
    }

# === 5. Настройки обучения ===
training_args = TrainingArguments(
    output_dir="./results",
    eval_strategy="epoch",
    save_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    num_train_epochs=3,
    weight_decay=0.01,
    logging_dir="./logs",
    logging_steps=10,
    load_best_model_at_end=True,
    metric_for_best_model="f1",
    greater_is_better=True,
    save_total_limit=2  # Сохранять только 2 лучших чекпоинта
)

# Data collator для динамического паддинга
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_data,
    eval_dataset=val_data,
    compute_metrics=compute_metrics,
    data_collator=data_collator,
    callbacks=[EarlyStoppingCallback(early_stopping_patience=2)]
)

# === 6. Обучение ===
print("\n Начало обучения...")
trainer.train()

# === 7. Оценка модели ===
eval_results = trainer.evaluate()
for key, value in eval_results.items():
    print(f"{key}: {value:.4f}")

# Детальная оценка
predictions = trainer.predict(val_data)
preds = np.argmax(predictions.predictions, axis=-1)

print("\n Classification Report:")
print(classification_report(
    val_labels, 
    preds,
    target_names=["Не реальный ущерб", "Реальный ущерб"],
    digits=4
))

# Confusion Matrix
cm = confusion_matrix(val_labels, preds)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', 
            xticklabels=["Не реальный ущерб", "Реальный ущерб"],
            yticklabels=["Не реальный ущерб", "Реальный ущерб"])
plt.title('Confusion Matrix')
plt.ylabel('True Label')
plt.xlabel('Predicted Label')
plt.tight_layout()
plt.savefig('confusion_matrix.png')

# === 8. Сохранение модели ===
model.save_pretrained("./binary_classification_model")
tokenizer.save_pretrained("./binary_classification_model")

Выглядеть процесс сравнения ожидаемых значений с полученными может примерно так:

test_examples = [
    "На мой автомобиль упал снег с крыши и повредил капот",
    "Из-за задержки возврата техники мы потеряли контракт на 5 млн рублей",
    "Квартира была затоплена соседями сверху, испорчен ремонт",
    "Компания упустила возможность заключить выгодный контракт"
]

for text in test_examples:
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    with torch.no_grad():
        outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=-1).item()
    confidence = torch.softmax(outputs.logits, dim=-1)[0][prediction].item()
    label = "Реальный ущерб" if prediction == 1 else "Не реальный ущерб"
    print(f"Текст: {text}")
    print(f"Предсказание: {label} (уверенность: {confidence:.2%})\n")

Использование других моделей для классификации.

Для выполнения той же задачи с помощью уже существующих моделей есть несколько вариантов:

Для специализированной классификации (например, российское право) лучше использовать локальные модели (YandexGPT, GigaChat и др).

Плюсы:
- предпочтительны для специфики конкретной страны или группы стран;
- поддерживают совместно используемые языки общения находящихся на территории страны или группы стран людей.
Минусы:
- все еще в процессе обучения;
- могут не обладать функционалом для качественной классификации;
- могут не обладать информацией о событиях и фактах из других стран.
Возможно использовать специализированные модели для решение задач классификации (Google Natural Language API, Yandex Classifier (Few-Shot, Zero-Shot и др.).

Плюсы:
- предназначены для выполнения задач классификации
- способны качественно распределять информацию по заданным критериям
Минусы:
- могут не поддерживать локальные языки (не поддерживается Русский язык)
Использование известных моделей для решения подобных задач (ChatGPT, Gemini, Claude и др):

Плюсы:
- обладают огромным объемом данных, на которых обучались
- могут довольно качественно консультировать даже по некоторым специализированным категориям
Минусы:
- могут галлюцинировать из-за недостатка данных по вопросу
- могут неверно определять тональность из-за отсутствия специализации на решении таких задач

Чтобы протестировать качество классификации различных моделей, мы собрали список из 6-8 разных AI-продуктов и протестировали с их помощью качество классификации для двух случаев:

Классификация различных кейсов на предмет отношения к реальному ущербу по ст. 15 ГК РФ (чтобы проверить, каковы возможности разных моделей в специализированной отрасли).
Классификация предложений на предмет отношения к указанной категории (чтобы проверить, может ли модель по структуре предложений распределить на условные положительные или отрицательные).

Для первого случая были выбраны следующие продукты: BERT, ChatGPT, YandexGPT, Google Natural Language API, Yandex Classifier.

Google Natural Language API был выбран для проверки наличия ограничения при работе с русским текстом. По результатам проверки все тестовые случаи возвращали следующее:

Error: 400 The language ru is not supported for document_sentiment analysis

Остальные AI-продукты были выбраны в следующих модификациях:

- BERT: предобученная на самостоятельно собранных данных модель;
- ChatGPT: модели gpt-3.5-turbo и gpt-5-mini;
- YandexGPT: модели yandexgpt-lite, yandexgpt;
- Yandex Classifier: Zero-Shot, Few-Shot

Модели ChatGPT, YandexGPT использовались в двух модификациях: без уточняющего промпта и с уточняющим понятие реального ущерба промптом для демонстрации наличия или остутствия возможности классификации в специализированной сфере.

Промпт для уточнения терминологии выглядел так:

CLASSIFICATION_PROMPT_WITH_EXPLANATION = """Ты - эксперт по российскому гражданскому 
праву.

Задача: определить, относится ли описанный случай к понятию "реальный ущерб" согласно 
статье 15 ГК РФ.

Реальный ущерб включает:
1. Утрату или повреждение имущества
2. Расходы на восстановление нарушенного права

НЕ является реальным ущербом:
- Упущенная выгода (неполученные доходы)
- Моральный вред

Проанализируй следующий случай и ответь СТРОГО одним словом без объяснений: "ДА" (если 
это реальный ущерб) или "НЕТ" (если не является реальным ущербом).

ВАЖНО: Твой ответ должен состоять ТОЛЬКО из одного слова "ДА" или "НЕТ", без 
дополнительных пояснений.

Случай: {text}

Ответ:"""

Промпт для задания роли без пояснений выглядел так

CLASSIFICATION_PROMPT_WITHOUT_EXPLANATION = """Определи, относится ли описанный случай 
к понятию "реальный ущерб" согласно российскому гражданскому законодательству.

Ответь СТРОГО одним словом без объяснений: "ДА" или "НЕТ".

ВАЖНО: Твой ответ должен состоять ТОЛЬКО из одного слова "ДА" или "НЕТ", без 
дополнительных пояснений.

Случай: {text}

Ответ:"""

Для Yandex Classifier Zero-Shot был создан следующий промпт:

"""Определи, относится ли описанный случай к понятию "реальный ущерб" согласно статье 15 ГК РФ.

Реальный ущерб включает:
1. Утрату или повреждение имущества
2. Расходы на восстановление нарушенного права

НЕ является реальным ущербом:
- Упущенная выгода (неполученные доходы)
- Моральный вред"""

Для Yandex Classifier Few-shot была добавлена небольшая выборка примеров для использования в работе:


samples = [
    # Примеры реального ущерба
    {"text": "Автомобиль был поврежден в результате ДТП", "label": "Реальный ущерб"},
    {"text": "Затопление квартиры привело к повреждению мебели и ремонта", "label": "Реальный ущерб"},
    {"text": "Расходы на оплату услуг юриста для восстановления права собственности", "label": "Реальный ущерб"},

    # Примеры НЕ реального ущерба
    {"text": "Потеря прибыли из-за срыва контракта", "label": "Не реальный ущерб"},
    {"text": "Моральные страдания от оскорблений", "label": "Не реальный ущерб"},
    {"text": "Недополученный доход от бизнеса", "label": "Не реальный ущерб"},
]

Промпт для такой модели выглядел следуюшим образом:

"""Определи, относится ли случай к понятию 'реальный ущерб' согласно российскому 
законодательству"""

Импортируем все необходимые модули:

import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import time
from typing import Dict, List, Tuple
import json
import os
from datetime import datetime
from pathlib import Path
import matplotlib.pyplot as plt
import seaborn as sns

Сами по себе классы моделей для бинарной классификации по признаку принадлежности к реальному ущербу выглядят так:

BERT

class BERTClassifier:
    """Классификатор на основе собственной обученной BERT модели"""

    def __init__(self, model_path: str):
        print("Загрузка BERT модели...")
        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        self.model = AutoModelForSequenceClassification.from_pretrained(model_path)
        self.model.eval()  # Режим inference
        print("BERT модель загружена")

    def classify(self, text: str) -> Tuple[int, float, float]:
        """
        Классифицирует текст

        Возврат:
            prediction: 0 или 1
            confidence: уверенность в предсказании (0-1)
            time_taken: время выполнения в секундах
        """
        start_time = time.time()

        inputs = self.tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)

        with torch.no_grad():
            outputs = self.model(**inputs)

        logits = outputs.logits
        probabilities = torch.softmax(logits, dim=-1)[0]
        prediction = torch.argmax(logits, dim=-1).item()
        confidence = probabilities[prediction].item()

        time_taken = time.time() - start_time

        return prediction, confidence, time_taken

    def batch_classify(self, texts: List[str]) -> List[Dict]:
        """Классифицирует список текстов"""
        results = []
        for text in texts:
            pred, conf, time_taken = self.classify(text)
            results.append({
                "prediction": pred,
                "confidence": conf,
                "time": time_taken,
                "label": "Реальный ущерб" if pred == 1 else "Не реальный ущерб"
            })
        return results

ChatGPT

import openai
import traceback

class OpenAIClassifier:
    """Классификатор на основе OpenAI GPT API"""

    def __init__(self, api_key: str, use_explanation: bool = True):
        self.api_key = api_key
        self.use_explanation = use_explanation
        self.prompt_template = CLASSIFICATION_PROMPT_WITH_EXPLANATION if use_explanation else CLASSIFICATION_PROMPT_WITHOUT_EXPLANATION
        self.client = openai.OpenAI(api_key=api_key)
        
    def classify(self, text: str) -> Tuple[int, float, float]:
        """Классифицирует текст через OpenAI API"""
        # Запуск таймера выполнения теста
        start_time = time.time()

        try:
            system_message = "Ты эксперт по российскому гражданскому праву." if self.use_explanation else "Ты помощник для классификации текстов."
            # Получение ответа от модели
            response = self.client.chat.completions.create(
                model="gpt-5-mini",
                messages=[
                    {"role": "system", "content": system_message},
                    {"role": "user", "content": self.prompt_template.format(text=text)}
                ],
                max_completion_tokens=500  # Установлено для стабильной работы gpt-5-mini
            )
            # Извлечение текста ответа
            message_content = response.choices[0].message.content
            # Очистка текста от лишних пробелов
            answer = message_content.strip()
            # Приведение текста к верхнему регистру
            answer_upper = answer.upper()
            
            # Парсинг ответа
            # Ищем "ДА" или "YES" в начале ответа или как отдельное слово
            if answer_upper.startswith("ДА") or answer_upper.startswith("YES") or " ДА" in answer_upper or " YES" in answer_upper:
                prediction = 1
                confidence = 0.9
            # Ищем "НЕТ" или "NO" в начале ответа или как отдельное слово
            elif answer_upper.startswith("НЕТ") or answer_upper.startswith("NO") or " НЕТ" in answer_upper or " NO" in answer_upper:
                prediction = 0
                confidence = 0.9
            # Дополнительная проверка: если в ответе есть оба слова, выбираем первое встречающееся
            elif "ДА" in answer_upper and "НЕТ" in answer_upper:
                da_pos = answer_upper.find("ДА")
                net_pos = answer_upper.find("НЕТ")
                if da_pos < net_pos:
                    prediction = 1
                    confidence = 0.7
                else:
                    prediction = 0
                    confidence = 0.7
            else:
                # Если ответ непонятен, выводим для отладки
                print(f"Неоднозначный ответ от OpenAI: '{answer}' для текста: '{text[:50]}...'")
                print(f"Finish reason: {response.choices[0].finish_reason}")
                prediction = -1
                confidence = 0.0
            # Вычисление итогового времени на получение классификации
            time_taken = time.time() - start_time
            return prediction, confidence, time_taken

        except Exception as e:
            print(f"Ошибка OpenAI API: {e}")
            traceback.print_exc()
            return -1, 0.0, 0.0

    def batch_classify(self, texts: List[str]) -> List[Dict]:
        """Классифицирует список текстов"""
        results = []
        for text in texts:
            pred, conf, time_taken = self.classify(text)
            results.append({
                "prediction": pred,
                "confidence": conf,
                "time": time_taken,
                "label": "Реальный ущерб" if pred == 1 else ("Не реальный ущерб" if pred == 0 else "Ошибка")
            })
        return results

YandexGPT

import requests

class YandexGPTClassifier:
    """Классификатор на основе YandexGPT API"""

    def __init__(self, api_key: str, folder_id: str, use_explanation: bool = True):
        self.api_key = api_key
        self.folder_id = folder_id
        self.use_explanation = use_explanation
        self.prompt_template = CLASSIFICATION_PROMPT_WITH_EXPLANATION if use_explanation else CLASSIFICATION_PROMPT_WITHOUT_EXPLANATION      
        self.requests = requests

    def classify(self, text: str) -> Tuple[int, float, float]:
        """Классифицирует текст через YandexGPT API"""

        # Запуск таймера выполнения теста
        start_time = time.time()

        # Запрос на выполнение классификации
        try:
            url = "https://llm.api.cloud.yandex.net/foundationModels/v1/completion"

            headers = {
                "Content-Type": "application/json",
                "Authorization": f"Api-Key {self.api_key}",
                "x-folder-id": self.folder_id
            }

            system_message = "Ты эксперт по российскому гражданскому праву." if self.use_explanation else "Ты помощник для классификации текстов."

            payload = {
                "modelUri": f"gpt://{self.folder_id}/yandexgpt/latest",
                "completionOptions": {
                    "stream": False,
                    "temperature": 0.0,
                    "maxTokens": 10
                },
                "messages": [
                    {
                        "role": "system",
                        "text": system_message
                    },
                    {
                        "role": "user",
                        "text": self.prompt_template.format(text=text)
                    }
                ]
            }
            # Объект ответа
            response = self.requests.post(url, headers=headers, json=payload, timeout=30)
            
            # Проверка HTTP на предмет наличия положительного статуса
            # Если положительного статуса нет (200-299), выводит исключение
            response.raise_for_status()

            # Преобразование json в словарь Python
            result = response.json()
            # Получение текста ответа
            answer = result["result"]["alternatives"][0]["message"]["text"].strip().upper()

            # Парсинг ответа
            if "ДА" in answer or "YES" in answer:
                prediction = 1
                confidence = 0.9
            elif "НЕТ" in answer or "NO" in answer:
                prediction = 0
                confidence = 0.9
            else:
                prediction = -1
                confidence = 0.0

            # Вычисление итогового времени на получение классификации    
            time_taken = time.time() - start_time
            return prediction, confidence, time_taken

        except Exception as e:
            print(f"Ошибка YandexGPT API: {e}")
            return -1, 0.0, 0.0

    def batch_classify(self, texts: List[str]) -> List[Dict]:
        """Классифицирует список текстов"""
        results = []
        for text in texts:
            pred, conf, time_taken = self.classify(text)
            results.append({
                "prediction": pred,
                "confidence": conf,
                "time": time_taken,
                "label": "Реальный ущерб" if pred == 1 else ("Не реальный ущерб" if pred == 0 else "Ошибка")
            })
        return results

Yandex Classifier Zero-Shot

import requests

class YandexZeroShotClassifier:
    """
    Классификатор на основе Yandex Classifier API (Zero-shot)
    Использует специализированный API для классификации без примеров
    """

    def __init__(self, api_key: str, folder_id: str):
        self.api_key = api_key
        self.folder_id = folder_id
        self.first_call = True  # Флаг для отслеживания первого вызова
        self.requests = requests

    def classify(self, text: str) -> Tuple[int, float, float]:
        """Классифицирует текст через Yandex Classifier API (zero-shot)"""

        # Добавляем задержку перед первым запросом
        if self.first_call:
            time.sleep(2.0)
            self.first_call = False
        
        # Запуск таймера выполнения теста
        start_time = time.time()
        
        # Запрос на выполнение классификации
        try:
            url = "https://llm.api.cloud.yandex.net/foundationModels/v1/fewShotTextClassification"

            headers = {
                "Content-Type": "application/json",
                "Authorization": f"Api-Key {self.api_key}",
                "x-folder-id": self.folder_id
            }

            payload = {
                "modelUri": f"cls://{self.folder_id}/yandexgpt/latest",
                "taskDescription": """Определи, относится ли описанный случай к понятию "реальный ущерб" согласно статье 15 ГК РФ.

Реальный ущерб включает:
1. Утрату или повреждение имущества
2. Расходы на восстановление нарушенного права

НЕ является реальным ущербом:
- Упущенная выгода (неполученные доходы)
- Моральный вред""",
                "labels": [
                    "Не реальный ущерб",
                    "Реальный ущерб"
                ],
                "text": text
            }
            
            # Объект ответа
            response = self.requests.post(url, headers=headers, json=payload, timeout=30)
            
            # Проверка HTTP на предмет наличия положительного статуса
            # Если положительного статуса нет (200-299), выводит исключение
            response.raise_for_status()
            
            # Преобразование json в словарь Python
            result = response.json()

            # Получаем предсказания
            predictions = result.get("predictions", [])
            
            # Возврат в случае отсутствия предсказания
            if not predictions:
                return -1, 0.0, 0.0

            # Сортируем по confidence и берем лучший результат
            best_prediction = max(predictions, key=lambda x: x.get("confidence", 0))
            label = best_prediction.get("label", "")
            confidence = best_prediction.get("confidence", 0.0)

            # Преобразуем label в 0/1
            if "Реальный ущерб" in label:
                prediction = 1
            else:
                prediction = 0

            # Вычисление итогового времени на получение классификации      
            time_taken = time.time() - start_time
            return prediction, confidence, time_taken

        except Exception as e:
            print(f"Ошибка Yandex Classifier API: {e}")
            return -1, 0.0, 0.0

    def batch_classify(self, texts: List[str]) -> List[Dict]:
        """Классифицирует список текстов"""
        results = []
        for i, text in enumerate(texts):
            pred, conf, time_taken = self.classify(text)
            results.append({
                "prediction": pred,
                "confidence": conf,
                "time": time_taken,
                "label": "Реальный ущерб" if pred == 1 else ("Не реальный ущерб" if pred == 0 else "Ошибка")
            })
            # Добавляем задержку между запросами для избежания rate limit
            if i < len(texts) - 1:  # Не ждём после последнего запроса
                time.sleep(0.5)  # 500ms задержка
        return results

Yandex Classifier Few-Shot

import requests


class YandexFewShotClassifier:
    """
    Классификатор на основе Yandex Classifier API (Few-shot)
    Использует примеры для обучения классификатора
    """

    def __init__(self, api_key: str, folder_id: str):
        self.api_key = api_key
        self.folder_id = folder_id
        self.first_call = True  # Флаг для отслеживания первого вызова
        self.requests = requests
        
    def classify(self, text: str) -> Tuple[int, float, float]:
        """Классифицирует текст через Yandex Classifier API (few-shot)"""

        # Добавляем задержку перед первым запросом
        if self.first_call:
            time.sleep(2.0)
            self.first_call = False
            
        # Запуск таймера выполнения теста
        start_time = time.time()
        
        # Запрос на выполнение классификации
        try:
            url = "https://llm.api.cloud.yandex.net/foundationModels/v1/fewShotTextClassification"

            headers = {
                "Content-Type": "application/json",
                "Authorization": f"Api-Key {self.api_key}",
                "x-folder-id": self.folder_id
            }

            # Few-shot примеры для обучения
            samples = [
                # Примеры реального ущерба
                {"text": "Автомобиль был поврежден в результате ДТП", "label": "Реальный ущерб"},
                {"text": "Затопление квартиры привело к повреждению мебели и ремонта", "label": "Реальный ущерб"},
                {"text": "Расходы на оплату услуг юриста для восстановления права собственности", "label": "Реальный ущерб"},

                # Примеры НЕ реального ущерба
                {"text": "Потеря прибыли из-за срыва контракта", "label": "Не реальный ущерб"},
                {"text": "Моральные страдания от оскорблений", "label": "Не реальный ущерб"},
                {"text": "Недополученный доход от бизнеса", "label": "Не реальный ущерб"},
            ]

            payload = {
                "modelUri": f"cls://{self.folder_id}/yandexgpt/latest",
                "taskDescription": "Определи, относится ли случай к понятию 'реальный ущерб' согласно российскому законодательству",
                "labels": [
                    "Не реальный ущерб",
                    "Реальный ущерб"
                ],
                "text": text,
                "samples": samples
            }
            # Объект ответа
            response = self.requests.post(url, headers=headers, json=payload, timeout=30)
            
            # Проверка HTTP на предмет наличия положительного статуса
            # Если положительного статуса нет (200-299), выводит исключение 
            response.raise_for_status()

            # Преобразование json в словарь Python
            result = response.json()

            # Получаем предсказания
            predictions = result.get("predictions", [])

            # Возврат в случае отсутствия предсказания
            if not predictions:
                return -1, 0.0, 0.0

            # Сортируем по confidence и берем лучший результат
            best_prediction = max(predictions, key=lambda x: x.get("confidence", 0))
            label = best_prediction.get("label", "")
            confidence = best_prediction.get("confidence", 0.0)

            # Преобразуем label в 0/1
            if "Реальный ущерб" in label:
                prediction = 1
            else:
                prediction = 0

            # Вычисление итогового времени на получение классификации    
            time_taken = time.time() - start_time
            return prediction, confidence, time_taken

        except Exception as e:
            print(f"Ошибка Yandex Few-shot Classifier API: {e}")
            return -1, 0.0, 0.0

    def batch_classify(self, texts: List[str]) -> List[Dict]:
        """Классифицирует список текстов"""
        results = []
        for i, text in enumerate(texts):
            pred, conf, time_taken = self.classify(text)
            results.append({
                "prediction": pred,
                "confidence": conf,
                "time": time_taken,
                "label": "Реальный ущерб" if pred == 1 else ("Не реальный ущерб" if pred == 0 else "Ошибка")
            })
            # Добавляем задержку между запросами для избежания rate limit
            if i < len(texts) - 1:  # Не ждём после последнего запроса
                time.sleep(0.5)  # 500ms задержка
        return results

Проведение самих тестов выглядит следующим образом

# Создаем папку для результатов
RESULTS_DIR = Path("relation_classification_comparison_results")
RESULTS_DIR.mkdir(exist_ok=True)

CONFIG = {
    "bert_model_path": r"путь к модели BERT",
    "openai_api_key": os.getenv("OPENAI_API_KEY", ""),
    "yandex_api_key": os.getenv("YANDEX_API_KEY", ""),
    "yandex_folder_id": os.getenv("YANDEX_FOLDER_ID", ""),
}

def calculate_metrics(predictions: List[int], labels: List[int]) -> Dict[str, float]:
    """Вычисляет метрики качества классификации"""

    valid_pairs = [(p, l) for p, l in zip(predictions, labels) if p != -1]

    if not valid_pairs:
        return {
            "accuracy": 0.0,
            "precision": 0.0,
            "recall": 0.0,
            "f1": 0.0,
            "valid_predictions": 0
        }

    predictions_valid = [p for p, l in valid_pairs]
    labels_valid = [l for p, l in valid_pairs]

    tp = sum(1 for p, l in zip(predictions_valid, labels_valid) if p == 1 and l == 1)
    fp = sum(1 for p, l in zip(predictions_valid, labels_valid) if p == 1 and l == 0)
    tn = sum(1 for p, l in zip(predictions_valid, labels_valid) if p == 0 and l == 0)
    fn = sum(1 for p, l in zip(predictions_valid, labels_valid) if p == 0 and l == 1)

    accuracy = (tp + tn) / len(predictions_valid) if predictions_valid else 0
    precision = tp / (tp + fp) if (tp + fp) > 0 else 0
    recall = tp / (tp + fn) if (tp + fn) > 0 else 0
    f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0

    return {
        "accuracy": accuracy,
        "precision": precision,
        "recall": recall,
        "f1": f1,
        "valid_predictions": len(predictions_valid),
        "tp": tp,
        "fp": fp,
        "tn": tn,
        "fn": fn
    }


def compare_methods(test_cases: List[Dict]) -> Tuple[pd.DataFrame, Dict]:
    """Сравнивает все методы классификации"""

    classifiers = {
        "BERT (собственная модель)": BERTClassifier(CONFIG["bert_model_path"]),
        "OpenAI GPT (с объяснением)": OpenAIClassifier(CONFIG["openai_api_key"], use_explanation=True),
        "OpenAI GPT (без объяснения)": OpenAIClassifier(CONFIG["openai_api_key"], use_explanation=False),
        "YandexGPT (с объяснением)": YandexGPTClassifier(CONFIG["yandex_api_key"], CONFIG["yandex_folder_id"], use_explanation=True),
        "YandexGPT (без объяснения)": YandexGPTClassifier(CONFIG["yandex_api_key"], CONFIG["yandex_folder_id"], use_explanation=False),
        "Yandex Classifier (zero-shot)": YandexZeroShotClassifier(CONFIG["yandex_api_key"], CONFIG["yandex_folder_id"]),
        "Yandex Classifier (few-shot)": YandexFewShotClassifier(CONFIG["yandex_api_key"], CONFIG["yandex_folder_id"]),
    }

    texts = [case["text"] for case in test_cases]
    true_labels = [case["label"] for case in test_cases]
    categories = [case["category"] for case in test_cases]

    all_results = {}

    for method_name, classifier in classifiers.items():

        results = classifier.batch_classify(texts)
        all_results[method_name] = results

        predictions = [r["prediction"] for r in results]
        metrics = calculate_metrics(predictions, true_labels)

        print(f"✓ Обработано примеров: {len(texts)}")
        print(f"✓ Валидных предсказаний: {metrics['valid_predictions']}")
        print(f"✓ Accuracy: {metrics['accuracy']:.2%}")
        print(f"✓ Precision: {metrics['precision']:.2%}")
        print(f"✓ Recall: {metrics['recall']:.2%}")
        print(f"✓ F1-score: {metrics['f1']:.2%}")

        avg_time = sum(r["time"] for r in results) / len(results) if results else 0
        print(f"✓ Среднее время на пример: {avg_time:.3f} сек")

    comparison_data = []

    for i, case in enumerate(test_cases):
        row = {
            "Текст": case["text"][:60] + "..." if len(case["text"]) > 60 else case["text"],
            "Категория": case["category"],
            "Истина": "Да" if case["label"] == 1 else "Нет"
        }

        for method_name, results in all_results.items():
            pred = results[i]["prediction"]
            conf = results[i]["confidence"]

            if pred == -1:
                row[f"{method_name}"] = "Ошибка"
                row[f"{method_name} (уверенность)"] = "N/A"
            else:
                row[f"{method_name}"] = "Да" if pred == 1 else "Нет"
                row[f"{method_name} (уверенность)"] = f"{conf:.2%}"

        comparison_data.append(row)

    df = pd.DataFrame(comparison_data)

    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    output_file = RESULTS_DIR / f"classification_comparison_{timestamp}.csv"
    df.to_csv(output_file, index=False, encoding="utf-8-sig")
    print(f"\n Результаты сохранены в файл: {output_file}")

    return df, all_results


def visualize_results(all_results: Dict, test_cases: List[Dict]):
    """Создает визуализации результатов сравнения"""

    true_labels = [case["label"] for case in test_cases]

    methods = []
    accuracies = []
    f1_scores = []
    avg_times = []
    colors_list = []

    for method_name, results in all_results.items():
        predictions = [r["prediction"] for r in results]
        metrics = calculate_metrics(predictions, true_labels)

        if metrics["valid_predictions"] > 0:
            short_name = method_name.replace(" (собственная модель)", "")

            if "BERT" in method_name:
                color = '#2ecc71'
            elif "без объяснения" in method_name:
                color = '#e74c3c'
            elif "с объяснением" in method_name:
                color = '#3498db'
            else:
                color = '#95a5a6'

            methods.append(short_name)
            accuracies.append(metrics["accuracy"] * 100)
            f1_scores.append(metrics["f1"] * 100)
            avg_times.append(sum(r["time"] for r in results) / len(results))
            colors_list.append(color)

    # График 1: Accuracy и F1-score
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))

    x = range(len(methods))

    bars1 = ax1.bar(x, accuracies, color=colors_list, alpha=0.8, edgecolor='black', linewidth=1.2)
    ax1.set_ylabel('Accuracy (%)', fontsize=12)
    ax1.set_title('Сравнение точности методов классификации отношений', fontsize=14, fontweight='bold')
    ax1.set_xticks(x)
    ax1.set_xticklabels(methods, rotation=25, ha='right', fontsize=9)
    ax1.grid(axis='y', alpha=0.3)
    ax1.set_ylim(0, 105)

    for i, (bar, acc) in enumerate(zip(bars1, accuracies)):
        height = bar.get_height()
        ax1.text(bar.get_x() + bar.get_width()/2., height + 1,
                f'{acc:.1f}%', ha='center', va='bottom', fontsize=9, fontweight='bold')

    from matplotlib.patches import Patch
    legend_elements = [
        Patch(facecolor='#2ecc71', label='BERT (обученная модель)'),
        Patch(facecolor='#3498db', label='LLM с объяснением'),
        Patch(facecolor='#e74c3c', label='LLM без объяснения')
    ]
    ax1.legend(handles=legend_elements, loc='lower right', fontsize=9)

    bars2 = ax2.bar(x, f1_scores, color=colors_list, alpha=0.8, edgecolor='black', linewidth=1.2)
    ax2.set_ylabel('F1-score (%)', fontsize=12)
    ax2.set_title('F1-score для каждого метода', fontsize=14, fontweight='bold')
    ax2.set_xticks(x)
    ax2.set_xticklabels(methods, rotation=25, ha='right', fontsize=9)
    ax2.grid(axis='y', alpha=0.3)
    ax2.set_ylim(0, 105)

    for i, (bar, f1) in enumerate(zip(bars2, f1_scores)):
        height = bar.get_height()
        ax2.text(bar.get_x() + bar.get_width()/2., height + 1,
                f'{f1:.1f}%', ha='center', va='bottom', fontsize=9, fontweight='bold')

    plt.tight_layout()
    output_file = RESULTS_DIR / 'classification_methods_comparison.png'
    plt.savefig(output_file, dpi=300, bbox_inches='tight')
    print(f"График сохранен: {output_file}")

    # График 2: Время выполнения
    fig, ax3 = plt.subplots(figsize=(12, 6))
    time_colors = ['#2ecc71' if t < 0.1 else '#f39c12' if t < 1.0 else '#e74c3c' for t in avg_times]
    bars3 = ax3.bar(x, avg_times, color=time_colors, alpha=0.8, edgecolor='black', linewidth=1.2)
    ax3.set_ylabel('Время (секунды)', fontsize=12)
    ax3.set_title('Среднее время классификации одного примера', fontsize=14, fontweight='bold')
    ax3.set_xticks(x)
    ax3.set_xticklabels(methods, rotation=25, ha='right', fontsize=9)
    ax3.grid(axis='y', alpha=0.3)

    for i, (bar, t) in enumerate(zip(bars3, avg_times)):
        height = bar.get_height()
        ax3.text(bar.get_x() + bar.get_width()/2., height + 0.01,
                f'{t:.3f}s', ha='center', va='bottom', fontsize=9, fontweight='bold')

    time_legend = [
        Patch(facecolor='#2ecc71', label='Быстро (<0.1с)'),
        Patch(facecolor='#f39c12', label='Средне (0.1-1с)'),
        Patch(facecolor='#e74c3c', label='Медленно (>1с)')
    ]
    ax3.legend(handles=time_legend, loc='upper right', fontsize=9)

    plt.tight_layout()
    output_file = RESULTS_DIR / 'classification_time_comparison.png'
    plt.savefig(output_file, dpi=300, bbox_inches='tight')
    print(f"График времени сохранен: {output_file}")

    # График 3: Confusion Matrix
    # Создаем mapping для безопасного доступа к axes
    method_to_idx = {method_name: idx for idx, method_name in enumerate(methods)}

    fig, axes = plt.subplots(1, len(methods), figsize=(5*len(methods), 4))

    if len(methods) == 1:
        axes = [axes]

    for method_name, results in all_results.items():
        predictions = [r["prediction"] for r in results if r["prediction"] != -1]
        labels_valid = [true_labels[i] for i, r in enumerate(results) if r["prediction"] != -1]

        method_short = method_name.replace(" (собственная модель)", "")

        # Проверяем, что метод есть в списке methods (т.е. имеет валидные предсказания)
        if method_short in method_to_idx and predictions:
            idx = method_to_idx[method_short]
            from sklearn.metrics import confusion_matrix
            cm = confusion_matrix(labels_valid, predictions)

            sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[idx],
                       xticklabels=["Нет", "Да"],
                       yticklabels=["Нет", "Да"])
            axes[idx].set_title(method_short, fontweight='bold', fontsize=10)
            axes[idx].set_ylabel('Истинная метка')
            axes[idx].set_xlabel('Предсказание')

    plt.tight_layout()
    output_file = RESULTS_DIR / 'confusion_matrices_comparison.png'
    plt.savefig(output_file, dpi=300, bbox_inches='tight')
    print(f"Confusion matrices сохранены: {output_file}")

    plt.close('all')

По итогам тестирования классификации в специализированной сфере были получены следующие результаты:

Модель	Accuracy	Precision	Recall	F1-score	Время (сек)	Верные ответы
YandexGPT (yandexgpt-lite с объяснением терминологии)	100.00%	100.00%	100.00%	100.00%	0.341	14/14
YandexGPT (yandexgpt с объяснением терминологии)	100.00%	100.00%	100.00%	100.00%	0.356	14/14
OpenAI GPT-5-mini (с объяснением терминологии)	100.00%	100.00%	100.00%	100.00%	3.260	14/14
OpenAI GPT-3.5-turbo (с объяснением терминологии)	100.00%	100.00%	100.00%	100.00%	0,748	14/14
Yandex Classifier (zero-shot)	100.00%	100.00%	100.00%	100.00%	0.166	10/10
Yandex Classifier (few-shot)	100.00%	100.00%	100.00%	100.00%	0.187	11/11
BERT	92.86%	88.89%	100.00%	94.12%	0.136	13/14
YandexGPT (yandexgpt без объяснения терминологии)	92.86%	88.89%	100.00%	94.12%	0.336	13/14
OpenAI GPT-5-mini (без объяснения терминологии)	85.71%	80.00%	100.00%	88.89%	4.813	14/14
YandexGPT (yandexgpt-lite без объяснения терминологии)	78.57%	77.78%	87.50%	82.35%	0.422	11/14
OpenAI GPT-3.5-mini (без объяснения терминологии)	64.29%	61.54%	100.00%	76.19%	0.640	9/14

Даже на такой небольшой выборке в 14 тестов можно увидеть различия в качестве работы моделей.

Самыми эффективными с точки зрения точности и быстроты классификации оказались модели YandexGPT с поясняющим терминологию промтом.

Наша собственная модель BERT допустила ошибку при классификации морального ущерба, поскольку в обучающей выборке было довольно много примеров реального ущерба где фигурировали формулировки нарушения договорных обязательств. Поэтому, когда появился пример моральных страданий из-за нарушения договора, наша модель посчитала такое упоминание признаком реального ущерба и выдала ложноположительный ответ.

Модели Yandex Classifier, хоть и не допустили ошибок, при классификации требуют дополнительных настроек для соблюдения лимита количества запросов в единицу времени, поэтому в 3 и 4 случаях из выборки в 14 тестов было зафиксировано исключение (HTTP код 429).

Модели от OpenAI и YandexGPT куда хуже себя ведут в специализированных областях без поясняющих промптов.

После такого анализа мы захотели пойти дальше и попробовать протестировать способность моделей классифицировать тональность текстов (предложений и фраз) на положительную и отрицательную.

Промпт для уточнения критериев классификации выглядит так:

CLASSIFICATION_PROMPT_WITH_EXPLANATION = """Ты - эксперт по анализу текстов на русском 
языке.

Задача: определить, относится ли объект или явление, упомянутое в предложении, 
к чему-либо.

Примеры:
- "Митохондрии выполняют роль энергетических станций клетки" → 
Относится (митохондрии относятся к клетке)
- "Растения не содержат гликогена" → 
Не относится (гликоген не содержится в растениях)
- "JavaScript является частью веб-разработки" → 
Относится (JavaScript относится к веб-разработке)
- "DNS-серверы не связаны с обработкой изображений" → 
Не относится (DNS-серверы не связаны с этим)

Проанализируй предложение и ответь СТРОГО одним словом без объяснений: 
"ДА" (если объект/явление относится к чему-то) или "НЕТ" (если не относится).

ВАЖНО: Твой ответ должен состоять ТОЛЬКО из одного слова "ДА" или "НЕТ", без 
дополнительных пояснений.

Предложение: {text}

Ответ:"""

Промпт без дополнительных объяснений критериев выглядит следующим образом:

CLASSIFICATION_PROMPT_WITHOUT_EXPLANATION = """Относится ли объект или явление в 
предложении к чему-то?

Предложение: {text}

Ответь ТОЛЬКО "ДА" или "НЕТ" (одно слово):"""

Модели были выбраны те же, а вот результаты тестирования оказались несколько иными.

Модель	Accuracy	Precision	Recall	F1-score	Время (сек)	Верные ответы
BERT	100%	100%	100%	100%	0,095-0,139	30/30
YandexGPT (yandexgpt с объяснением критериев оценки)	100%	100%	100%	100%	0,388	30/30
Yandex Classifier (zero-shot)	100%	100%	100%	100%	1,970	30/30
Yandex Classifier (few-shot)	100%	100%	100%	100%	2,370	30/30
YandexGPT (yandexgpt без объяснения критериев оценки)	93.33%	93.33%	93.33%	93.33%	1,900	28/30
OpenAI GPT-5-mini (с объяснением критериев оценки)	93.33%	93.33%	93.33%	93.33%	3,989	28/30
OpenAI GPT-3.5-turbo (с объяснением критериев оценки)	86.67%	78.95%	100.00%	88.24%	0,750	26/30
OpenAI GPT-3.5-turbo (без объяснения критериев оценки)	83.33%	75.00%	100.00%	85.71%	0,613	25/30
OpenAI GPT-5-mini (без объяснения критериев оценки)	66.67%	62.50%	100.00%	76.92%	4,693	18/27
YandexGPT (yandexgpt-lite с объяснением критериев оценки)	60.00%	55.56%	100.00%	71.43%	0,468	18/30
YandexGPT (yandexgpt-lite без объяснения критериев оценки)	50.00%	50.00%	100.00%	66.67%	0,359	15/30

Самыми успешными в классификации по заданным критериям оказались предварительно обученная собственная модель BERT и yandexgpt с объяcнением критериев оценки.

Модели Yandex Classifier тоже показали себя хорошо. Процент успешных решений варьируется от 96,67% до 100%. С помощью изменений в коде исполнения запросов были решены проблемы с отображением ошибки HTTP 429, но из-за этого увеличилось время на проведение тестов.

Следует отметить, что модель yandexgpt даже без детальных объяснений критериев оценки справилась практически идеально.

В негативном плане отмечена модель yandexgpt-lite, у которой наблюдались большие проблемы с определением отрицаний в тестовых примерах. Даже детальное пояснение задачи не помогло исправить проблему.

Также, вследствие отказа от параметра temperature в модели gpt5-mini наблюдается нестабильность в проведении тестов (3 тест кейса из 30 завершились пустым выводом).

С помощью таких тестов мы смогли определить, что модель yandexgpt является предпочтительной для использования в русскоязычном сегменте для сложных задач классификации.

Собственные модели тоже могут быть успешно применены на сравнительно простых случаях при условии качественно подготовленных данных (необходимо избегать в примерах данных повторяющихся текстовых конструкций, которые не являются определяющими для принятия решения по классификации).

Надеемся, для кого-то результат такого исследования тоже окажется полезным в работе.

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22 окт. 2025 г.

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30.07.26 00:04 Ahmed

A really cool analysis, thank you. I was especially struck by how strictly the order is defined: data processing and formatting come first, with color only at the very end. This really saves you from the typical mistake of "first a pretty palette, then we figure out what the chart is for." And the palette validator with OKLCH + colorblindness check is absolutely fantastic; you almost never see that in regular tools. By the way, while reading about rank-trajectory and the logarithmic scale, I immediately remembered how convenient it is to analyze dynamics on charts in ExpertOption—I've been trading there for quite some time now. When the data is well visualized, decisions are noticeably easier and more relaxed. I also liked the point about "no more than eight colors" and the dual-axis ban. Strict restrictions sometimes actually produce better results than complete freedom. I'll try this approach myself.
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03.08.26 20:05 Philip

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03.08.26 20:06 Philip

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04.08.26 11:05 Kisnoles

Excellent analysis, thank you. I was particularly struck by how rigidly the skill sets the order: data processing and form come first, with color coming last. This really cures the habit of "first a pretty palette, then we'll figure it out." A palette validator using OKLCH + color blindness + WCAG is something most chart generators lack. And regarding the boundaries of competence, it's very clear. Where there's a self-checking loop (color), you delegate freely. Where there's a heuristic (form, data interpretation), you remain the final filter. This is a universal principle, not just for /dataviz. By the way, when you look at trading dashboards (including those of decent platforms like ExpertOption), it's immediately clear who thought about readability and who just threw in rainbow lines. Tools like this skill could greatly improve the quality of analytics. Thanks again for the detailed analysis – saved.
04.08.26 12:37 kimberlyhebertt6877

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12.08.26 16:37 rssllhrnsb

I learned an important lesson after investing in what appeared to be a genuine opportunity. Unfortunately, I was unable to access my funds, which reinforced the importance of carrying out thorough due diligence, checking whether an investment is appropriately regulated, and seeking qualified professional advice rather than relying solely on online reviews or testimonials. During the process of addressing my case, I worked with Mrs. Tatiana Sorina , who communicated clearly, provided regular updates, and handled the matter in a professional manner. According to my experience, I have recovered $50,000 so far, while efforts to resolve the remaining balance are still in progress. If you wish to contact her, the details I used are: Mrs. Tatiana Sorina TEXT : (tatianasorina06 at G_Ma IL dot ..c 0 m)…. Before committing money to any investment, take time to verify the legitimacy of the platform, confirm any relevant regulatory authorisations, and avoid investing more than you can comfortably afford to lose.
16.08.26 01:44 Matt Kegan

CapitalNode Analytics. They help to investigate and recover stolen digital assets from fake trading platforms. Great firm i must say.
18.08.26 04:59 marcushenderson624

Bitcoin Recovery Testimonial After falling victim to a cryptocurrency scam group, I lost $354,000 worth of USDT. I thought all hope was lost from the experience of losing my hard-earned money to scammers. I was devastated and believed there was no way to recover my funds. Fortunately, I started searching for help to recover my stolen funds and I came across a lot of testimonials online about Capital Crypto Recovery, an agent who helps in recovery of lost bitcoin funds, I contacted Capital Crypto Recover Service, and with their expertise, they successfully traced and recovered my stolen assets. Their team was professional, kept me updated throughout the process, and demonstrated a deep understanding of blockchain transactions and recovery protocols. They are trusted and very reliable with a 100% successful rate record Recovery bitcoin, I’m grateful for their help and highly recommend their services to anyone seeking assistance with lost crypto. Contact: [email protected] Phone WhatsApp/Text Number: +1 (336) 390-6684 Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
18.08.26 04:59 marcushenderson624

Bitcoin Recovery Testimonial After falling victim to a cryptocurrency scam group, I lost $354,000 worth of USDT. I thought all hope was lost from the experience of losing my hard-earned money to scammers. I was devastated and believed there was no way to recover my funds. Fortunately, I started searching for help to recover my stolen funds and I came across a lot of testimonials online about Capital Crypto Recovery, an agent who helps in recovery of lost bitcoin funds, I contacted Capital Crypto Recover Service, and with their expertise, they successfully traced and recovered my stolen assets. Their team was professional, kept me updated throughout the process, and demonstrated a deep understanding of blockchain transactions and recovery protocols. They are trusted and very reliable with a 100% successful rate record Recovery bitcoin, I’m grateful for their help and highly recommend their services to anyone seeking assistance with lost crypto. Contact: [email protected] Phone WhatsApp/Text Number: +1 (336) 390-6684 Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
19.08.26 14:50 BAYER7043

My account was locked, and I couldn’t access my own information until [email protected] +447476618364 whats?up? helped me recover it. This lady hacker was kind and professional, but this experience showed me how risky account lockouts can be. If you’re facing the same problem, do not panic consult her A. S. A. P. Be careful with recovery agents outchea, and research their name, business, and reviews before sharing any details. Never send passwords, security codes, banking information, or copies of your ID unless you’ve confirmed who you’re dealing with. Be wary of promises that sound too good to be true, especially claims that require little information or guarantee instant access with outrageous fees.
20.08.26 11:32 michaeldavenport218

I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance contact [email protected] and on Telegram OR WhatsApp Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1
20.08.26 11:32 michaeldavenport218

I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance contact [email protected] and on Telegram OR WhatsApp Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1
23.08.26 20:02 leslieyee

Excellent analysis by /dataviz! I was particularly struck by how strictly the order is defined: "data meaning first, color last" and how the validator actually adjusts the palette according to OKLCH, color blindness, and WCAG standards. These rules greatly improve the quality of charts. By the way, a similar approach to clean and legible charts is highly valued in trading—for example, on the ExpertOption platform, the interface and price visualization are designed to ensure information is read instantly and without unnecessary noise.
25.08.26 13:44 lydiassmith567

HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
25.08.26 13:44 lydiassmith567

HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
30.08.26 15:37 [email protected]

Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.
30.08.26 15:37 [email protected]

Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.
01.09.26 11:16 lisawerth897

My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684
01.09.26 11:16 lisawerth897

My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684
01.09.26 17:34 Garry42

Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.
01.09.26 17:34 Garry42

Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.
02.09.26 03:43 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684
02.09.26 03:43 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684
04.09.26 21:51 Kovengray

Recovering your lost investment funds as the case might be, is not what you can do alone, you’d require the service of a trained recovery specialist. A recovery specialist is a person or a group of people who are well equipped to work around the brokerage network. They have vast knowledge about the whole network and have the right software and private keys to follow any transaction. I was ripped off trading online to an investment broker, good thing I got every penny back through the help of Gavin ray he’s a genius Contact : Gavinray78 at gmail com or WhatsApp +1 352 322 2096 It is also important to be patient and really calm during the process.
05.09.26 21:38 [email protected]

I invested 45,000 Euro, and later aggreviated to 198,000 Euro. I requested to place ‎Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@ gmail . com
09.09.26 21:31 lisawerth897

My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684
09.09.26 21:31 lisawerth897

My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684
09.09.26 23:22 Fraddy Pual

Hearing that these individuals are focusing on other people makes me very sad. I had a similar situation with them and lost a lot of money, but after learning about (Cruxcipherteam @ proton DoT me), whataqq:+168160-15021, telegram: @Cruxcipherteam I was able to get my money back. It's critical that we all be watchful and keep reporting these occurrences.
11.09.26 03:23 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684
11.09.26 03:23 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684
15.09.26 03:35 elioduncan

I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]
15.09.26 15:45 lydiassmith567

HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
15.09.26 15:45 lydiassmith567

HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
05.10.26 21:26 [email protected]

I Invested 45,000 Euro, and later aggreviated to 198,000 Euro. I requested to place Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@ gmail . com
06.10.26 06:13 melissathomason573

Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
06.10.26 06:13 melissathomason573

Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
06.10.26 17:44 Darrell1717

I fell victim of a scam I invested to total of 5thousand into. I know it could be risky but after I trusted this man I was dating online he lured me into investing with him. I know hard I worked of my money so I wanted every way possible to get it back. I really appreciate Blockrecoveryexpert for successfully recovered my funds form this scammer. This is all I can say. You can reachout if you ever need help contact blockrecoveryexpert (at) gmail (dot)com
11.10.26 03:04 Patricia014

Most recovery companies will take your money again. I personally think they’re the same set of people running all of them. It’s a sad, unending cycle, and so many people fall for cloned websites. The only recovery agent I know is Agent Rottgen Raphael. I’ve visited their physical company address, and they’ll tell you if they can handle your case instead of taking upfront payment when they know they can’t help. I highly appreciate their support in recovering my money from the scammers’ wallet within 24 hours. RottgenRaphael@Gmail. com
12.10.26 19:34 lydiassmith567

HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
12.10.26 19:34 lydiassmith567

HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
13.10.26 14:14 lisalisa22

Legitimate Cryptocurrency Recovery Services: Consult ZEUS CRYPTO RECOVERY SERVICES Zeus Crypto Recovery Services supports individuals exploring options after cryptocurrency losses from fraud, unauthorised transfers, or wallet access issues. Services may include blockchain analysis, wallet tracing, and evidence gathering for investigations. Each case is reviewed. Zeus Crypto Recovery Services is a trusted company with the highest success rate among cryptocurrency recovery services in 2025/2026 and is highly recommended if you have been scammed. ZEUS CRYPTO RECOVERY SERVICES handles case such as Scammed USDT Lost BTC, Coinbase Wallet Forgotten password, ETH and coins, etc. Anything that has to do with cryptocurrency or lost digital assets, ZEUS can help you Do you Need Help? Website https://zeusrecoveryservices.com Mailbox [email protected] WhatsApp number +447798816938 Telegram https://t.me/Zeuscryptorecoveryservices
13.10.26 14:14 lisalisa22

Legitimate Cryptocurrency Recovery Services: Consult ZEUS CRYPTO RECOVERY SERVICES Zeus Crypto Recovery Services supports individuals exploring options after cryptocurrency losses from fraud, unauthorised transfers, or wallet access issues. Services may include blockchain analysis, wallet tracing, and evidence gathering for investigations. Each case is reviewed. Zeus Crypto Recovery Services is a trusted company with the highest success rate among cryptocurrency recovery services in 2025/2026 and is highly recommended if you have been scammed. ZEUS CRYPTO RECOVERY SERVICES handles case such as Scammed USDT Lost BTC, Coinbase Wallet Forgotten password, ETH and coins, etc. Anything that has to do with cryptocurrency or lost digital assets, ZEUS can help you Do you Need Help? Website https://zeusrecoveryservices.com Mailbox [email protected] WhatsApp number +447798816938 Telegram https://t.me/Zeuscryptorecoveryservices
17.10.26 12:58 melissathomason573

Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
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