Инициализировал репозиторий с проектом punctuator_model

.gitignore

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+venv/

Files/tb.py

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+import time
+import logging
+import asyncio
+from aiogram import Bot, Dispatcher, types
+from aiogram.utils import executor
+from env import TOKEN
+from test_copy import process_text
+
+bot = Bot(TOKEN)
+dp = Dispatcher(bot=bot)
+
+@dp.message_handler(commands=['start'])
+async def start_handler(message: types.Message):
+    user_id = message.from_user.id
+    user_name = message.from_user.first_name
+    logging.info(f'{user_id} {time.asctime()}')
+
+    await message.reply(f"Hi, {user_name} !!!!!\nвведите текст:")
+
+@dp.message_handler(state=None)
+async def message_handler(message: types.Message):
+    processed_text = process_text(message.text)
+    await message.reply(f"Вот исправленный текст\n\n{processed_text}")
+
+async def on_startup(dp):
+    await bot.send_message(chat_id='your_chat_id', text='Bot has been started')
+
+async def on_shutdown(dp):
+    # Perform cleanup (if any) here
+    pass
+
+if __name__ == '__main__':
+    loop = asyncio.get_event_loop()
+    loop.run_until_complete(on_startup(dp))
+    try:
+        executor.start_polling(dp, skip_updates=True)
+    finally:
+        loop.run_until_complete(on_shutdown(dp))

Files/telegram_bot.py

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+import time
+import logging
+from aiogram import Bot, Dispatcher, types
+from aiogram import utils_executor
+from env import TOKEN
+from aiogram import Bot, types, Dispatcher, executor
+from test_copy import process_text
+bot = Bot(TOKEN)
+dp = Dispatcher(bot=bot)
+
+@dp.message_handler(commands=['start'])
+async def start_handler(message: types.Message):
+    user_id = message.from_user.id
+    user_name = message.from_user.first_name
+    logging.info(f'{user_id} {time.asctime()}')
+
+    await message.reply(f"Hi, {user_name} !!!!!\nвведите текст:")
+
+
+@dp.message_handler(state=None)
+async def message_handler(message: types.Message):
+    processed_text = process_text(message.text)
+    await message.reply(f"Вот исправленный текст\n\n{processed_text}")
+
+
+if __name__ == '__main__':
+    executor.start_polling(dp, skip_updates=True)

Files/test.py

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+import numpy as np
+import onnxruntime as ort
+from omegaconf import OmegaConf
+from sentencepiece import SentencePieceProcessor
+from typing import List
+
+def process_text(input_text: str) -> str:
+    spe_path = "sp.model"  # Путь к файлу SentencePieceProcessor
+    tokenizer: SentencePieceProcessor = SentencePieceProcessor(spe_path)
+
+    # Загрузка ONNX модели
+    onnx_path = "model.onnx"  # Путь к файлу ONNX модели
+    ort_session: ort.InferenceSession = ort.InferenceSession(onnx_path)
+
+    # Загрузка конфигурации модели с метками, параметрами и др.
+    config_path = "config.yaml"  # Путь к файлу конфигурации модели
+    config = OmegaConf.load(config_path)
+    # Возможные метки классификации перед каждым подтокеном
+    pre_labels: List[str] = config.pre_labels
+    # Возможные метки классификации после каждого подтокена
+    post_labels: List[str] = config.post_labels
+    # Специальный класс, который означает "ничего не предсказывать"
+    null_token = config.get("null_token", "<NULL>")
+    # Специальный класс, который означает "все символы в этом подтокене заканчиваются точкой", например, "am" -> "a.m."
+    acronym_token = config.get("acronym_token", "<ACRONYM>")
+    # Не используется в этом примере, но если ваша последовательность превышает это значение, вам нужно разделить ее на несколько входов
+    max_len = config.max_length
+    # Для справки: граф не имеет языковой специфики
+    languages: List[str] = config.languages
+
+    # Кодирование входного текста, добавление BOS + EOS
+    input_ids = [tokenizer.bos_id()] + tokenizer.EncodeAsIds(input_text) + [tokenizer.eos_id()]
+
+    # Создание массива numpy с формой [B, T], как ожидается входом графа.
+    input_ids_arr: np.array = np.array([input_ids])
+
+    # Запуск графа, получение результатов для всех аналитических данных
+    pre_preds, post_preds, cap_preds, sbd_preds = ort_session.run(None, {"input_ids": input_ids_arr})
+    # Убираем измерение пакета и преобразуем в списки
+    pre_preds = pre_preds[0].tolist()
+    post_preds = post_preds[0].tolist()
+    cap_preds = cap_preds[0].tolist()
+    sbd_preds = sbd_preds[0].tolist()
+
+    # Обработка текста как ранее
+    output_texts: List[str] = []
+    current_chars: List[str] = []
+
+    for token_idx in range(1, len(input_ids) - 1):
+        token = tokenizer.IdToPiece(input_ids[token_idx])
+        if token.startswith("▁") and current_chars:
+            current_chars.append(" ")
+        # Token-level predictions
+        pre_label = pre_labels[pre_preds[token_idx]]
+        post_label = post_labels[post_preds[token_idx]]
+        # If we predict "pre-punct", insert it before this token
+        if pre_label != null_token:
+            current_chars.append(pre_label)
+        # Iterate over each char. Skip SP's space token,
+        char_start = 1 if token.startswith("▁") else 0
+        for token_char_idx, char in enumerate(token[char_start:], start=char_start):
+            # If this char should be capitalized, apply upper case
+            if cap_preds[token_idx][token_char_idx]:
+                char = char.upper()
+            # Append char
+            current_chars.append(char)
+            # if this is an acronym, add a period after every char (p.m., a.m., etc.)
+            if post_label == acronym_token:
+                current_chars.append(".")
+        # Maybe this subtoken ends with punctuation
+        if post_label != null_token and post_label != acronym_token:
+            current_chars.append(post_label)
+
+        # If this token is a sentence boundary, finalize the current sentence and reset
+        if sbd_preds[token_idx]:
+            output_texts.append("".join(current_chars))
+            current_chars.clear()
+
+    # Добавляем последний токен
+    output_texts.append("".join(current_chars))
+
+    # Возвращаем обработанный текст
+    return "\n".join(output_texts)
+
+# Пример использования:
+input_text = "салам кандайсың"
+processed_text = process_text(input_text)
+print("Обработанный текст:")
+print(processed_text)

Files/word.py

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+import re
+from collections import Counter
+
+def read_sentences(file_path):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        sentences = file.readlines()
+    return sentences
+
+def preprocess_sentences(sentences):
+    # Удаление символов новой строки и лишних пробелов
+    sentences = [sentence.strip() for sentence in sentences]
+
+    # Добавление маркеров начала и конца предложения
+    sentences = ['<start> ' + sentence + ' <end>' for sentence in sentences]
+
+    # Разделение предложений на токены с сохранением информации о знаках препинания
+    tokenized_sentences = [re.findall(r"[\w']+|[.,!?;]", sentence) for sentence in sentences]
+
+    return tokenized_sentences
+
+def create_vocab(tokenized_sentences):
+    # Создание словаря с частотой слов (токенов) в корпусе
+    word_freq = Counter([word for sentence in tokenized_sentences for word in sentence])
+
+    # Сортировка слов по убыванию частоты
+    sorted_words = sorted(word_freq, key=word_freq.get, reverse=True)
+
+    # Добавление специальных маркеров начала и конца предложения
+    special_tokens = ['<pad>', '<start>', '<end>', '<unk>']
+    sorted_words = special_tokens + sorted_words
+
+    # Создание словаря, преобразующего слова (токены) в числовые идентификаторы
+    word_to_id = {word: idx for idx, word in enumerate(sorted_words)}
+
+    # Создание обратного словаря для декодирования
+    id_to_word = {idx: word for word, idx in word_to_id.items()}
+
+    return word_to_id, id_to_word
+
+
+file_path = "Files/merged.txt"
+sentences = read_sentences(file_path)
+tokenized_sentences = preprocess_sentences(sentences)
+
+word_to_id, id_to_word = create_vocab(tokenized_sentences)
+# print(word_to_id)

README.md

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+# hackaton_generative_ai

config.yaml

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+
+languages:  [
+  "af", "am", "ar", "bg", "bn", "de", "el", "en", "es", "et",
+  "fa", "fi", "fr", "gu", "hi", "hr", "hu", "id", "is", "it",
+  "ja", "kk", "kn", "ko", "ky", "lt", "lv", "mk", "ml", "mr",
+  "nl", "or", "pa", "pl", "ps", "pt", "ro", "ru", "rw", "so",
+  "sr", "sw", "ta", "te", "tr", "uk", "zh"
+]
+
+max_length: 256
+
+pre_labels: [
+  "<NULL>",
+  "¿",
+]
+
+post_labels: [
+  "<NULL>",
+  "<ACRONYM>",
+  ".",
+  ",",
+  "?",
+  "？",
+  "，",
+  "。",
+  "、",
+  "・",
+  "।",
+  "؟",
+  "،",
+  ";",
+  "።",
+  "፣",
+  "፧",
+]

env.py

@@ -0,0 +1 @@
+TOKEN = "7073499256:AAHovU_AQdpLAqg5qINRiYBUMKZdba-QTuc"

main.py

@@ -0,0 +1,25 @@
+import time
+import logging
+from aiogram import Bot, types, Dispatcher
+from aiogram.utils import executor
+from env import TOKEN
+from test_copy import process_text
+
+bot = Bot(TOKEN)
+dp = Dispatcher(bot=bot)
+
+@dp.message_handler(commands=['start'])
+async def start_handler(message: types.Message):
+    user_id = message.from_user.id
+    user_name = message.from_user.first_name
+    logging.info(f'{user_id} {time.asctime()}')
+
+    await message.reply(f"Салам, {user_name} !!!!!\nТекст киргизиңиз:")
+
+@dp.message_handler(state=None)
+async def message_handler(message: types.Message):
+    processed_text = process_text(message.text)
+    await message.reply(f"Оңдолгон текст:\n\n{processed_text}")
+
+if __name__ == '__main__':
+    executor.start_polling(dp, skip_updates=True)

model.onnx

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+oid sha256:c43ca686dabc237c3b06be834b9423c07580fef7e2b1a6c09976f7d60caa5d89
+size 1112481438

requirements.txt

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sp.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7f944d0be93b275f62e1913fd409f378ddbba108e57fe4a9cb47e8c047f6bef1
+size 5069059

test_copy.py

@@ -0,0 +1,95 @@
+import numpy as np
+import onnxruntime as ort
+from huggingface_hub import hf_hub_download
+from omegaconf import OmegaConf
+from sentencepiece import SentencePieceProcessor
+from typing import List
+
+def process_text(input_text: str) -> str:
+    spe_path = "sp.model"  # Путь к файлу SentencePieceProcessor
+    tokenizer: SentencePieceProcessor = SentencePieceProcessor(spe_path)
+
+    # Загрузка ONNX модели
+    onnx_path = "model.onnx"  # Путь к файлу ONNX модели
+    ort_session: ort.InferenceSession = ort.InferenceSession(onnx_path)
+
+    # Загрузка конфигурации модели с метками, параметрами и др.
+    config_path = "config.yaml"  # Путь к файлу конфигурации модели
+    config = OmegaConf.load(config_path)
+    # Возможные метки классификации перед каждым подтокеном
+    pre_labels: List[str] = config.pre_labels
+    # Возможные метки классификации после каждого подтокена
+    post_labels: List[str] = config.post_labels
+    # Специальный класс, который означает "ничего не предсказывать"
+    null_token = config.get("null_token", "<NULL>")
+    # Специальный класс, который означает "все символы в этом подтокене заканчиваются точкой", например, "am" -> "a.m."
+    acronym_token = config.get("acronym_token", "<ACRONYM>")
+    # Не используется в этом примере, но если ваша последовательность превышает это значение, вам нужно разделить ее на несколько входов
+    max_len = config.max_length
+    # Для справки: граф не имеет языковой специфики
+    languages: List[str] = config.languages
+
+    # Кодирование входного текста, добавление BOS + EOS
+    input_ids = [tokenizer.bos_id()] + tokenizer.EncodeAsIds(input_text) + [tokenizer.eos_id()]
+
+    # Создание массива numpy с формой [B, T], как ожидается входом графа.
+    input_ids_arr: np.array = np.array([input_ids])
+
+    # Запуск графа, получение результатов для всех аналитических данных
+    pre_preds, post_preds, cap_preds, sbd_preds = ort_session.run(None, {"input_ids": input_ids_arr})
+    # Убираем измерение пакета и преобразуем в списки
+    pre_preds = pre_preds[0].tolist()
+    post_preds = post_preds[0].tolist()
+    cap_preds = cap_preds[0].tolist()
+    sbd_preds = sbd_preds[0].tolist()
+
+    # Обработка текста как ранее
+    output_texts: List[str] = []
+    current_chars: List[str] = []
+
+    for token_idx in range(1, len(input_ids) - 1):
+        token = tokenizer.IdToPiece(input_ids[token_idx])
+        if token.startswith("▁") and current_chars:
+            current_chars.append(" ")
+        # Token-level predictions
+        pre_label = pre_labels[pre_preds[token_idx]]
+        post_label = post_labels[post_preds[token_idx]]
+        # If we predict "pre-punct", insert it before this token
+        if pre_label != null_token:
+            current_chars.append(pre_label)
+        # Iterate over each char. Skip SP's space token,
+        char_start = 1 if token.startswith("▁") else 0
+        for token_char_idx, char in enumerate(token[char_start:], start=char_start):
+            # If this char should be capitalized, apply upper case
+            if cap_preds[token_idx][token_char_idx]:
+                char = char.upper()
+            # Append char
+            current_chars.append(char)
+            # if this is an acronym, add a period after every char (p.m., a.m., etc.)
+            if post_label == acronym_token:
+                current_chars.append(".")
+        # Maybe this subtoken ends with punctuation
+        if post_label != null_token and post_label != acronym_token:
+            current_chars.append(post_label)
+
+        # If this token is a sentence boundary, finalize the current sentence and reset
+        if sbd_preds[token_idx]:
+            output_texts.append("".join(current_chars))
+            current_chars.clear()
+
+    # Добавляем последний токен
+    output_texts.append("".join(current_chars))
+
+    # Возвращаем обработанный текст
+    return "\n".join(output_texts)
+
+
+
+
+
+
+
+
+
+
+