├── .gitignore ├── weights └── weights.txt ├── .github └── FUNDING.yml ├── images ├── 0l1k6xm0e55j.png ├── 0rulgz9m75fc.png ├── 2iw73365ipkj.png ├── b9m7plcblp71.png ├── bx8pwm8j2njf.png ├── dz749dknry6v.png ├── e096csal1tbl.png ├── hywpq6yb16no.png ├── jbz7uc83s0j1.png ├── k5e1vy36y49i.png ├── khrxc9atjbni.png ├── lidikqfnw71o.png ├── qb8hwuno3q4l.png ├── re2ypiiv5rg7.png ├── t6co1mos6p6p.png ├── ty7546sn8nrx.png ├── xhsnyv7dzi3v.png └── zt9ylu1hceqq.png ├── vocab └── vocab_full_10k_ru.pickle ├── requirements.txt ├── requirements_gpu.txt ├── utils └── tprint.py ├── core ├── checking_client.py ├── yadisk.py ├── predictor.py ├── main_client.py ├── tokenizer.py └── tf_transformer.py ├── bot.py ├── why_prewarm.txt ├── config.py ├── README.md └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | /.idea 2 | __pycache__ 3 | *.h5 4 | -------------------------------------------------------------------------------- /weights/weights.txt: -------------------------------------------------------------------------------- 1 | https://yadi.sk/d/shcawRomGx2seA 2 | -------------------------------------------------------------------------------- /.github/FUNDING.yml: -------------------------------------------------------------------------------- 1 | custom: 'https://boosty.to/sergree' 2 | -------------------------------------------------------------------------------- /images/0l1k6xm0e55j.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sergree/DolboNet/HEAD/images/0l1k6xm0e55j.png -------------------------------------------------------------------------------- /images/0rulgz9m75fc.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sergree/DolboNet/HEAD/images/0rulgz9m75fc.png -------------------------------------------------------------------------------- /images/2iw73365ipkj.png: 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-------------------------------------------------------------------------------- 1 | cyrtranslit>=0.4 2 | discord.py>=1.5.1 3 | numpy>=1.18.5 4 | scipy>=1.5.4 5 | tensorflow-gpu>=2.3.1 6 | tqdm>=4.51.0 7 | requests>=2.24.0 8 | -------------------------------------------------------------------------------- /utils/tprint.py: -------------------------------------------------------------------------------- 1 | # Микромодуль для вывода информации на консоль с датой и временем 2 | # by Sergree 3 | # https://github.com/sergree 4 | 5 | import datetime 6 | 7 | 8 | def current_time(): 9 | return str(datetime.datetime.now()) + ": " 10 | 11 | 12 | def log(*msg): 13 | print(current_time() + " ".join([str(x) for x in msg])) 14 | -------------------------------------------------------------------------------- /core/checking_client.py: -------------------------------------------------------------------------------- 1 | # Тестовый Discord клиент для проверки валидности Discord токена 2 | # by Sergree 3 | # https://github.com/sergree 4 | 5 | import discord 6 | 7 | 8 | class CheckingClient(discord.Client): 9 | def __init__(self, **options): 10 | super().__init__(**options) 11 | 12 | async def on_ready(self): 13 | await self.close() 14 | -------------------------------------------------------------------------------- /bot.py: -------------------------------------------------------------------------------- 1 | # Основной модуль DolboNet 2 | # by Sergree 3 | # https://github.com/sergree 4 | 5 | import discord 6 | from core.checking_client import CheckingClient 7 | import config 8 | from utils.tprint import log 9 | import asyncio 10 | 11 | log("Проверяю Discord токен...") 12 | checking_client = CheckingClient(intents=discord.Intents.none()) 13 | login_successful = False 14 | try: 15 | checking_client.run(config.token) 16 | login_successful = True 17 | except discord.errors.LoginFailure: 18 | log("НЕВЕРНЫЙ DISCORD ТОКЕН! Необходимо отредактировать файл config.py!") 19 | 20 | if login_successful: 21 | log("Discord токен проверен.") 22 | asyncio.set_event_loop(asyncio.new_event_loop()) 23 | from core.main_client import MainClient 24 | 25 | intents = discord.Intents.none() 26 | intents.guilds = True 27 | intents.guild_messages = True 28 | intents.emojis = True 29 | intents.message_content = True 30 | main_client = MainClient(intents=intents) 31 | main_client.run(config.token) 32 | -------------------------------------------------------------------------------- /core/yadisk.py: -------------------------------------------------------------------------------- 1 | # https://lowvoltage.github.io/2017/07/29/Yadisk-Direct-Download-Python 2 | 3 | import requests 4 | 5 | from utils.tprint import log 6 | 7 | API_ENDPOINT = ( 8 | "https://cloud-api.yandex.net/v1/disk/public/resources/download?public_key={}" 9 | ) 10 | 11 | 12 | def _get_real_direct_link(sharing_link): 13 | pk_request = requests.get(API_ENDPOINT.format(sharing_link)) 14 | 15 | # Returns None if the link cannot be "converted" 16 | return pk_request.json().get("href") 17 | 18 | 19 | def _extract_filename(direct_link): 20 | for chunk in direct_link.strip().split("&"): 21 | if chunk.startswith("filename="): 22 | return chunk.split("=")[1] 23 | return None 24 | 25 | 26 | def download_yadisk_link(sharing_link, filename=None): 27 | direct_link = _get_real_direct_link(sharing_link) 28 | if direct_link: 29 | # Try to recover the filename from the link 30 | filename = filename or _extract_filename(direct_link) 31 | 32 | download = requests.get(direct_link) 33 | with open(filename, "wb") as out_file: 34 | out_file.write(download.content) 35 | log('Успешно скачал "{}" в "{}"'.format(sharing_link, filename)) 36 | else: 37 | log('Не удалось скачать "{}"'.format(sharing_link)) 38 | -------------------------------------------------------------------------------- /why_prewarm.txt: -------------------------------------------------------------------------------- 1 | В модуле predictor.py после непосредственной загрузки трансформера с весами выполняется подготовительный прогон пустыми данными для всей длины config.max_len (64). 2 | Если убрать этот прогон, то бот загрузится намного быстрее, но сообщения будут генерироваться (иногда) долго. 3 | А именно: при генерации первого сообщения ботом, на выхлоп одной буквы (токена) уйдет около 6 секунд. 4 | Из-за того, что TF2 не дружит с asyncio, Discord клиент скорее всего вылетит. 5 | Если бот сгенерировал 10 букв (токенов), то следующие сообщения длиной <= 10 токенов будут генерироваться быстро. 6 | Но при попытке сгенерировать сообщение большей длины, каждая следующая буква (11-я, 12-я и т.п.) первый раз будет генерироваться также по 6 секунд. 7 | А Discord клиент продолжит вылетать. 8 | Такие тормоза будут продолжаться, пока не сгенерируется сообщение длиной в config.max_len (64) буквы (токена). 9 | Чтобы избежать такой медленной работы, мы решили добавить подготовительный прогон пустыми данными (преварм), чтобы трансформер разметил всю свою схему заранее. 10 | Скорее всего, этого можно было бы избежать изменением какого-либо специального флага для TensorFlow 2. Но мы такого пока не нашли. 11 | В оригинальном туториале информация об этой особенности отсутствует: https://github.com/tensorflow/examples/blob/master/community/en/transformer_chatbot.ipynb 12 | -------------------------------------------------------------------------------- /config.py: -------------------------------------------------------------------------------- 1 | # Токен бота в Discord 2 | # Как получить: https://www.writebots.com/discord-bot-token/ 3 | token = "ВСТАВЬТЕ_ТОКЕН_СЮДА" 4 | 5 | # С какой вероятностью бот отправит сообщение, если обнаружит сообщение с его упоминанием (от 0 до 1) 6 | mention_prob = 1 # 100% 7 | 8 | # С какой вероятностью бот отправит сообщение, если обнаружит сообщение без его упоминания (от 0 до 1) 9 | no_mention_prob = 0.2 # 20% 10 | 11 | # Температура семплирования - регулирует характер и разнообразие генерируемого текста 12 | # Примеры значений: 13 | # 0.3 - пресет "попугай-повторюшка" 14 | # 0.65 - пресет "по-умолчанию" 15 | # 1.3 - пресет "пьяный поэт" 16 | # 3 - пресет "уснул на клавиатуре" 17 | temperature = 0.65 18 | 19 | # Команда изменения температуры во время работы бота (могут использовать только администраторы) 20 | command_temperature_change = "!temp" 21 | 22 | # --- 23 | # ! Следующие параметры лучше оставить как есть ! 24 | # --- 25 | 26 | # Максимальная длина хранимой очереди сообщений на канал 27 | deque_max_len = 10 28 | 29 | # Предобученные веса модели 30 | weights_file = "weights/dolbonet_004_100_0.1485_0.4306.h5" 31 | 32 | # Файл хранящий словарь 33 | vocab_file = "vocab/vocab_full_10k_ru.pickle" 34 | 35 | # Статус бота в Discord 36 | discord_game_name = "github.com/sergree" 37 | 38 | # Величина словаря 39 | vocab_size = 10000 40 | 41 | # Максимальная длина входного и выходного тензоров 42 | max_len = 64 43 | 44 | # Использовать подготовительный прогон трансформера (читайте why_prewarm.txt) 45 | use_prewarm = True 46 | 47 | # Использовать задержку в печати или нет (симуляция скорости печати 300-600 символов в минуту) 48 | use_delay = True 49 | -------------------------------------------------------------------------------- /core/predictor.py: -------------------------------------------------------------------------------- 1 | # Модуль загрузки и семплирования из Transformer 2 | # by Sergree 3 | # https://github.com/sergree 4 | 5 | import numpy as np 6 | from scipy.special import softmax 7 | from core.tf_transformer import transformer 8 | import config 9 | from core.yadisk import download_yadisk_link 10 | from utils.tprint import log 11 | from tqdm import tqdm 12 | 13 | # Параментры Transformer взяты из оригинальной публикации: 14 | # https://arxiv.org/abs/1706.03762 (стр. 9 - base) 15 | 16 | NUM_LAYERS = 6 17 | D_MODEL = 512 18 | NUM_HEADS = 8 19 | UNITS = 2048 20 | DROPOUT = 0.1 21 | 22 | log(f"Загружаю {config.weights_file}...") 23 | 24 | model = transformer( 25 | vocab_size=config.vocab_size, 26 | num_layers=NUM_LAYERS, 27 | units=UNITS, 28 | d_model=D_MODEL, 29 | num_heads=NUM_HEADS, 30 | dropout=DROPOUT, 31 | ) 32 | try: 33 | model.load_weights(config.weights_file) 34 | except OSError: 35 | log(f"Похоже весов нет! Попробую скачать с Яндекс.Диска, подождите 2 минуты...") 36 | with open("weights/weights.txt") as f: 37 | url = f.readline().strip() 38 | download_yadisk_link(url, filename=config.weights_file) 39 | model.load_weights(config.weights_file) 40 | 41 | model.compile( 42 | optimizer="rmsprop", loss="sparse_categorical_crossentropy", metrics=["accuracy"] 43 | ) 44 | 45 | log(f"{config.weights_file} загружен.") 46 | 47 | 48 | def sample(preds, temperature=1.0): 49 | preds = np.asarray(preds).astype("float64") 50 | preds = preds / temperature 51 | preds = softmax(preds) 52 | probas = np.random.multinomial(1, preds, 1) 53 | return np.argmax(probas) 54 | 55 | 56 | def decode_sequence(input_seq, temperature, prewarm=False): 57 | target_seq = np.zeros((1, 1), dtype="uint16") 58 | target_seq[0, 0] = 2 59 | stop_condition = False 60 | decoded_sentence = [] 61 | if prewarm: 62 | pbar = tqdm(total=config.max_len) 63 | while not stop_condition: 64 | output_tokens = model.predict([input_seq, target_seq]) 65 | sampled_token_index = sample(output_tokens[0, -1, :], temperature=temperature) 66 | decoded_sentence.append(sampled_token_index) 67 | if len(decoded_sentence) > config.max_len: 68 | stop_condition = True 69 | elif sampled_token_index == 4 and not prewarm: 70 | stop_condition = True 71 | packed_sampled_token_index = np.zeros((1, 1)) 72 | packed_sampled_token_index[0, 0] = sampled_token_index if not prewarm else 1 73 | target_seq = np.append(target_seq, packed_sampled_token_index, axis=-1) 74 | if prewarm: 75 | pbar.update(1) 76 | if stop_condition: 77 | pbar.close() 78 | return decoded_sentence 79 | 80 | 81 | if config.use_prewarm: 82 | log( 83 | "Подготовительный прогон трансформера пустыми данными (читайте why_prewarm.txt)..." 84 | ) 85 | decode_sequence( 86 | np.ones((1, config.max_len), dtype="uint16"), config.temperature, prewarm=True 87 | ) 88 | log("Прогон трансформера завершен.") 89 | -------------------------------------------------------------------------------- /core/main_client.py: -------------------------------------------------------------------------------- 1 | # Основной Discord клиент для работы 2 | # by Sergree 3 | # https://github.com/sergree 4 | 5 | import discord 6 | import collections 7 | import random 8 | import asyncio 9 | import config 10 | from core.tokenizer import Tokenizer 11 | from utils.tprint import log 12 | from core import predictor 13 | 14 | 15 | class MainClient(discord.Client): 16 | def __init__(self, **options): 17 | super().__init__(**options) 18 | self.temperature = config.temperature 19 | self.tokenizer = Tokenizer() 20 | self.tokenizer.load_vocab_from_file(config.vocab_file) 21 | self.channel_deques = {} 22 | self.custom_emoji_collection = [] 23 | 24 | @staticmethod 25 | def decision(probability): 26 | return random.random() < probability 27 | 28 | def load_custom_emoji_collection(self): 29 | self.custom_emoji_collection.clear() 30 | guilds = list(self.guilds) 31 | for guild in guilds: 32 | self.custom_emoji_collection.extend(guild.emojis) 33 | log("Коллекция кастомных emoji обновлена.") 34 | 35 | def random_emoji(self): 36 | return ( 37 | str(random.choice(self.custom_emoji_collection)) 38 | if self.custom_emoji_collection 39 | else "" 40 | ) 41 | 42 | async def on_ready(self): 43 | log(f"Подключение к Discord успешно под пользователем @{self.user}.") 44 | self.load_custom_emoji_collection() 45 | game = discord.Game(config.discord_game_name) 46 | await self.change_presence(activity=game) 47 | 48 | async def on_guild_join(self, guild): 49 | await self.wait_until_ready() 50 | log(f"Зашел на сервер {guild.name}.") 51 | self.load_custom_emoji_collection() 52 | 53 | async def on_guild_remove(self, guild): 54 | await self.wait_until_ready() 55 | log(f"Вышел с сервера {guild.name}.") 56 | self.load_custom_emoji_collection() 57 | 58 | async def on_guild_emojis_update(self, guild, before, after): 59 | await self.wait_until_ready() 60 | log(f"На сервере {guild.name} изменилась коллекция emoji.") 61 | if len(before) != len(after): 62 | self.load_custom_emoji_collection() 63 | 64 | async def handle_command(self, message): 65 | # Команда изменения температуры семплирования 66 | # Не стали использовать discord.ext.commands, т.к. это единственная команда на данный момент 67 | # Потом добавим, если потребуется 68 | if ( 69 | message.author.guild_permissions.administrator 70 | and message.content.startswith(config.command_temperature_change.lower()) 71 | ): 72 | mc_splitted = message.content.split() 73 | if len(mc_splitted) > 1: 74 | set_ = self.set_temperature(mc_splitted[1]) 75 | if set_: 76 | await message.channel.send(f"`temperature` ➡️ `{mc_splitted[1]}`") 77 | return True 78 | return False 79 | 80 | def set_temperature(self, value): 81 | try: 82 | temperature = float(value) 83 | except ValueError: 84 | return False 85 | if temperature <= 0: 86 | return False 87 | self.temperature = temperature 88 | return True 89 | 90 | async def on_message(self, message): 91 | await self.wait_until_ready() 92 | if ( 93 | not isinstance(message.channel, discord.TextChannel) 94 | or (message.author.bot and message.author != self.user) 95 | or message.type != discord.MessageType.default 96 | ): 97 | return 98 | if not message.channel.permissions_for(message.guild.me).send_messages: 99 | return 100 | if message.channel.id not in self.channel_deques: 101 | self.channel_deques[message.channel.id] = collections.deque( 102 | maxlen=config.deque_max_len 103 | ) 104 | self.channel_deques[message.channel.id].append(message) 105 | command_used = await self.handle_command(message) 106 | if command_used: 107 | return 108 | if message.author == self.user: 109 | return 110 | my_mention = self.user in message.mentions 111 | if self.decision(config.no_mention_prob) or ( 112 | my_mention and self.decision(config.mention_prob) 113 | ): 114 | async with message.channel.typing(): 115 | input_messages = self.channel_deques[message.channel.id] 116 | input_tensor = self.tokenizer.encode_input(input_messages, self.user) 117 | output_tensor = predictor.decode_sequence( 118 | input_tensor, self.temperature 119 | ) 120 | output_message, token_count = self.tokenizer.decode_output( 121 | self, input_messages, output_tensor 122 | ) 123 | if config.use_delay: 124 | await asyncio.sleep(random.uniform(0.1, 0.2) * token_count) 125 | if output_message: 126 | await message.channel.send(output_message[:2000]) 127 | -------------------------------------------------------------------------------- /core/tokenizer.py: -------------------------------------------------------------------------------- 1 | # Токенизатор текста для Discord 2 | # by Sergree 3 | # https://github.com/sergree 4 | 5 | import numpy as np 6 | import re 7 | import pickle 8 | import cyrtranslit 9 | 10 | 11 | class Tokenizer: 12 | 13 | entity_to_word = { 14 | 0: "_URL_", 15 | 1: "_MY_MENTION_", 16 | 2: "_MEMBER_MENTION_", 17 | 3: "_CHANNEL_MENTION_", 18 | 4: "_ROLE_MENTION_", 19 | 5: "_CUSTOM_EMOJI_", 20 | 6: "_ANIMATED_CUSTOM_EMOJI_", 21 | } 22 | 23 | def __init__(self): 24 | self.index_to_word = {} 25 | self.word_to_index = {} 26 | 27 | def fill_index_to_word(self): 28 | self.index_to_word = {value: key for key, value in self.word_to_index.items()} 29 | 30 | def load_vocab_from_file(self, fname): 31 | with open(fname, "rb") as file: 32 | self.word_to_index = pickle.load(file) 33 | self.fill_index_to_word() 34 | 35 | @staticmethod 36 | def trigramize(word): 37 | trigrams = [] 38 | for idx, char in enumerate(word): 39 | if idx == 0: 40 | first = "*" 41 | else: 42 | first = word[idx - 1] 43 | second = char 44 | if idx == len(word) - 1: 45 | third = "*" 46 | else: 47 | third = word[idx + 1] 48 | trigrams.append(first + second + third) 49 | return trigrams 50 | 51 | def tokenize(self, content, author_id, my_id=0): 52 | tuples = re.findall( 53 | r"(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*(),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)|" 54 | r"(<@!?" + str(my_id) + r">)|" 55 | r"(<@!?\d{16,20}>)|" 56 | r"(<#\d{16,20}>)|" 57 | r"(<@&\d{16,20}>)|" 58 | r"(<:\w{1,32}:\d{16,20}>)|" 59 | r"(<[a]:\w{1,32}:\d{16,20}>)|" 60 | r"(@everyone|@here)|" 61 | r"([^\d\W]+)|" 62 | r"(.)", 63 | content, 64 | re.UNICODE, 65 | ) 66 | result = [] 67 | if author_id == my_id: 68 | result.append("_MY_MESSAGE_BEGIN_") 69 | else: 70 | result.append("_NOT_MY_MESSAGE_BEGIN_") 71 | for tup in tuples: 72 | for idx, item in enumerate(tup): 73 | if item: 74 | if idx <= 6: 75 | result.append(self.entity_to_word[idx]) 76 | elif idx == 7: 77 | result.append(item) 78 | elif idx == 8: 79 | if item.isupper(): 80 | result.append("_CAPS_") 81 | elif item[0].isupper(): 82 | result.append("_SHIFT_") 83 | trigrams = self.trigramize( 84 | cyrtranslit.to_cyrillic(item.lower(), "ru") 85 | ) 86 | result.extend(trigrams) 87 | else: 88 | result.append(item) 89 | result.append("_MESSAGE_END_") 90 | return result 91 | 92 | def get_index_by_word(self, word): 93 | if word in self.word_to_index: 94 | return self.word_to_index[word] 95 | else: 96 | return self.word_to_index["_UNK_"] 97 | 98 | def encode_input(self, messages, me, max_len=64): 99 | encoder_input_data = np.zeros((1, max_len), dtype="uint16") 100 | my_id = me.id 101 | tokenized_input = [] 102 | for message in messages: 103 | tokenized_input.extend( 104 | self.tokenize(message.content, message.author.id, my_id=my_id) 105 | ) 106 | if len(tokenized_input) > max_len: 107 | tokenized_input = tokenized_input[-max_len:] 108 | for idx, token in enumerate(tokenized_input): 109 | encoder_input_data[0, idx] = self.get_index_by_word(token) 110 | return encoder_input_data 111 | 112 | def decode_output(self, discord_client, input_messages, tensor): 113 | tokens = [] 114 | for idx in tensor: 115 | tokens.append(self.index_to_word[idx]) 116 | message = "" 117 | caps_active = False 118 | shift_active = False 119 | last_token = None 120 | for token in tokens: 121 | reset_shift_and_caps = True 122 | if token == " ": 123 | if last_token != " ": 124 | message += token 125 | elif len(token) < 3: 126 | message += token 127 | elif len(token) == 3: 128 | if shift_active or caps_active: 129 | message += token[1].upper() 130 | shift_active = False 131 | reset_shift_and_caps = False 132 | else: 133 | message += token[1] 134 | reset_shift_and_caps = False 135 | elif token == "_SHIFT_": 136 | shift_active = True 137 | reset_shift_and_caps = False 138 | elif token == "_CAPS_": 139 | caps_active = True 140 | reset_shift_and_caps = False 141 | elif token in ["_CUSTOM_EMOJI_", "_ANIMATED_CUSTOM_EMOJI_"]: 142 | message += discord_client.random_emoji() 143 | elif token == "_MY_MENTION_": 144 | message += discord_client.user.mention 145 | elif token == "_MEMBER_MENTION_": 146 | other_members = [] 147 | for input_message in input_messages: 148 | if input_message.author != discord_client.user: 149 | other_members.append(input_message.author.mention) 150 | if len(other_members) > 0: 151 | message += other_members[-1] 152 | last_token = token 153 | if reset_shift_and_caps: 154 | caps_active = False 155 | shift_active = False 156 | return message, len(tokens) 157 | -------------------------------------------------------------------------------- /core/tf_transformer.py: -------------------------------------------------------------------------------- 1 | # Copyright 2019 The TensorFlow Authors 2 | # https://github.com/tensorflow/examples/blob/master/community/en/transformer_chatbot.ipynb 3 | 4 | import tensorflow as tf 5 | 6 | 7 | # Scaled dot product attention 8 | def scaled_dot_product_attention(query, key, value, mask): 9 | """Calculate the attention weights. """ 10 | matmul_qk = tf.matmul(query, key, transpose_b=True) 11 | 12 | # scale matmul_qk 13 | depth = tf.cast(tf.shape(key)[-1], tf.float32) 14 | logits = matmul_qk / tf.math.sqrt(depth) 15 | 16 | # add the mask to zero out padding tokens 17 | if mask is not None: 18 | logits += mask * -1e9 19 | 20 | # softmax is normalized on the last axis (seq_len_k) 21 | attention_weights = tf.nn.softmax(logits, axis=-1) 22 | 23 | output = tf.matmul(attention_weights, value) 24 | 25 | return output 26 | 27 | 28 | # Multi-head attention 29 | class MultiHeadAttention(tf.keras.layers.Layer): 30 | def __init__(self, d_model, num_heads, name="multi_head_attention"): 31 | super(MultiHeadAttention, self).__init__(name=name) 32 | self.num_heads = num_heads 33 | self.d_model = d_model 34 | 35 | assert d_model % self.num_heads == 0 36 | 37 | self.depth = d_model // self.num_heads 38 | 39 | self.query_dense = tf.keras.layers.Dense(units=d_model) 40 | self.key_dense = tf.keras.layers.Dense(units=d_model) 41 | self.value_dense = tf.keras.layers.Dense(units=d_model) 42 | 43 | self.dense = tf.keras.layers.Dense(units=d_model) 44 | 45 | def split_heads(self, inputs, batch_size): 46 | inputs = tf.reshape(inputs, shape=(batch_size, -1, self.num_heads, self.depth)) 47 | return tf.transpose(inputs, perm=[0, 2, 1, 3]) 48 | 49 | def call(self, inputs): 50 | query, key, value, mask = ( 51 | inputs["query"], 52 | inputs["key"], 53 | inputs["value"], 54 | inputs["mask"], 55 | ) 56 | batch_size = tf.shape(query)[0] 57 | 58 | # linear layers 59 | query = self.query_dense(query) 60 | key = self.key_dense(key) 61 | value = self.value_dense(value) 62 | 63 | # split heads 64 | query = self.split_heads(query, batch_size) 65 | key = self.split_heads(key, batch_size) 66 | value = self.split_heads(value, batch_size) 67 | 68 | # scaled dot-product attention 69 | scaled_attention = scaled_dot_product_attention(query, key, value, mask) 70 | 71 | scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3]) 72 | 73 | # concatenation of heads 74 | concat_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model)) 75 | 76 | # final linear layer 77 | outputs = self.dense(concat_attention) 78 | 79 | return outputs 80 | 81 | 82 | # Padding mask 83 | def create_padding_mask(x): 84 | mask = tf.cast(tf.math.equal(x, 0), tf.float32) 85 | # (batch_size, 1, 1, sequence length) 86 | return mask[:, tf.newaxis, tf.newaxis, :] 87 | 88 | 89 | # Look-ahead mask 90 | def create_look_ahead_mask(x): 91 | seq_len = tf.shape(x)[1] 92 | look_ahead_mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0) 93 | padding_mask = create_padding_mask(x) 94 | return tf.maximum(look_ahead_mask, padding_mask) 95 | 96 | 97 | # Positional encoding 98 | class PositionalEncoding(tf.keras.layers.Layer): 99 | def __init__(self, position, d_model): 100 | super(PositionalEncoding, self).__init__() 101 | self.pos_encoding = self.positional_encoding(position, d_model) 102 | 103 | def get_angles(self, position, i, d_model): 104 | angles = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(d_model, tf.float32)) 105 | return position * angles 106 | 107 | def positional_encoding(self, position, d_model): 108 | angle_rads = self.get_angles( 109 | position=tf.range(position, dtype=tf.float32)[:, tf.newaxis], 110 | i=tf.range(d_model, dtype=tf.float32)[tf.newaxis, :], 111 | d_model=d_model, 112 | ) 113 | # apply sin to even index in the array 114 | sines = tf.math.sin(angle_rads[:, 0::2]) 115 | # apply cos to odd index in the array 116 | cosines = tf.math.cos(angle_rads[:, 1::2]) 117 | 118 | pos_encoding = tf.concat([sines, cosines], axis=-1) 119 | pos_encoding = pos_encoding[tf.newaxis, ...] 120 | return tf.cast(pos_encoding, tf.float32) 121 | 122 | def call(self, inputs): 123 | return inputs + self.pos_encoding[:, : tf.shape(inputs)[1], :] 124 | 125 | 126 | # Encoder layer 127 | def encoder_layer(units, d_model, num_heads, dropout, name="encoder_layer"): 128 | inputs = tf.keras.Input(shape=(None, d_model), name="inputs") 129 | padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask") 130 | 131 | attention = MultiHeadAttention(d_model, num_heads, name="attention")( 132 | {"query": inputs, "key": inputs, "value": inputs, "mask": padding_mask} 133 | ) 134 | attention = tf.keras.layers.Dropout(rate=dropout)(attention) 135 | attention = tf.keras.layers.LayerNormalization(epsilon=1e-6)(inputs + attention) 136 | 137 | outputs = tf.keras.layers.Dense(units=units, activation="relu")(attention) 138 | outputs = tf.keras.layers.Dense(units=d_model)(outputs) 139 | outputs = tf.keras.layers.Dropout(rate=dropout)(outputs) 140 | outputs = tf.keras.layers.LayerNormalization(epsilon=1e-6)(attention + outputs) 141 | 142 | return tf.keras.Model(inputs=[inputs, padding_mask], outputs=outputs, name=name) 143 | 144 | 145 | # Encoder 146 | def encoder(vocab_size, num_layers, units, d_model, num_heads, dropout, name="encoder"): 147 | inputs = tf.keras.Input(shape=(None,), name="inputs") 148 | padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask") 149 | 150 | embeddings = tf.keras.layers.Embedding(vocab_size, d_model)(inputs) 151 | embeddings *= tf.math.sqrt(tf.cast(d_model, tf.float32)) 152 | embeddings = PositionalEncoding(vocab_size, d_model)(embeddings) 153 | 154 | outputs = tf.keras.layers.Dropout(rate=dropout)(embeddings) 155 | 156 | for i in range(num_layers): 157 | outputs = encoder_layer( 158 | units=units, 159 | d_model=d_model, 160 | num_heads=num_heads, 161 | dropout=dropout, 162 | name="encoder_layer_{}".format(i), 163 | )([outputs, padding_mask]) 164 | 165 | return tf.keras.Model(inputs=[inputs, padding_mask], outputs=outputs, name=name) 166 | 167 | 168 | # Decoder layer 169 | def decoder_layer(units, d_model, num_heads, dropout, name="decoder_layer"): 170 | inputs = tf.keras.Input(shape=(None, d_model), name="inputs") 171 | enc_outputs = tf.keras.Input(shape=(None, d_model), name="encoder_outputs") 172 | look_ahead_mask = tf.keras.Input(shape=(1, None, None), name="look_ahead_mask") 173 | padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask") 174 | 175 | attention1 = MultiHeadAttention(d_model, num_heads, name="attention_1")( 176 | inputs={ 177 | "query": inputs, 178 | "key": inputs, 179 | "value": inputs, 180 | "mask": look_ahead_mask, 181 | } 182 | ) 183 | attention1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)(attention1 + inputs) 184 | 185 | attention2 = MultiHeadAttention(d_model, num_heads, name="attention_2")( 186 | inputs={ 187 | "query": attention1, 188 | "key": enc_outputs, 189 | "value": enc_outputs, 190 | "mask": padding_mask, 191 | } 192 | ) 193 | attention2 = tf.keras.layers.Dropout(rate=dropout)(attention2) 194 | attention2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)( 195 | attention2 + attention1 196 | ) 197 | 198 | outputs = tf.keras.layers.Dense(units=units, activation="relu")(attention2) 199 | outputs = tf.keras.layers.Dense(units=d_model)(outputs) 200 | outputs = tf.keras.layers.Dropout(rate=dropout)(outputs) 201 | outputs = tf.keras.layers.LayerNormalization(epsilon=1e-6)(outputs + attention2) 202 | 203 | return tf.keras.Model( 204 | inputs=[inputs, enc_outputs, look_ahead_mask, padding_mask], 205 | outputs=outputs, 206 | name=name, 207 | ) 208 | 209 | 210 | # Decoder 211 | def decoder(vocab_size, num_layers, units, d_model, num_heads, dropout, name="decoder"): 212 | inputs = tf.keras.Input(shape=(None,), name="inputs") 213 | enc_outputs = tf.keras.Input(shape=(None, d_model), name="encoder_outputs") 214 | look_ahead_mask = tf.keras.Input(shape=(1, None, None), name="look_ahead_mask") 215 | padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask") 216 | 217 | embeddings = tf.keras.layers.Embedding(vocab_size, d_model)(inputs) 218 | embeddings *= tf.math.sqrt(tf.cast(d_model, tf.float32)) 219 | embeddings = PositionalEncoding(vocab_size, d_model)(embeddings) 220 | 221 | outputs = tf.keras.layers.Dropout(rate=dropout)(embeddings) 222 | 223 | for i in range(num_layers): 224 | outputs = decoder_layer( 225 | units=units, 226 | d_model=d_model, 227 | num_heads=num_heads, 228 | dropout=dropout, 229 | name="decoder_layer_{}".format(i), 230 | )(inputs=[outputs, enc_outputs, look_ahead_mask, padding_mask]) 231 | 232 | return tf.keras.Model( 233 | inputs=[inputs, enc_outputs, look_ahead_mask, padding_mask], 234 | outputs=outputs, 235 | name=name, 236 | ) 237 | 238 | 239 | # Transformer 240 | def transformer( 241 | vocab_size, num_layers, units, d_model, num_heads, dropout, name="transformer" 242 | ): 243 | inputs = tf.keras.Input(shape=(None,), name="inputs") 244 | dec_inputs = tf.keras.Input(shape=(None,), name="dec_inputs") 245 | 246 | enc_padding_mask = tf.keras.layers.Lambda( 247 | create_padding_mask, output_shape=(1, 1, None), name="enc_padding_mask" 248 | )(inputs) 249 | # mask the future tokens for decoder inputs at the 1st attention block 250 | look_ahead_mask = tf.keras.layers.Lambda( 251 | create_look_ahead_mask, output_shape=(1, None, None), name="look_ahead_mask" 252 | )(dec_inputs) 253 | # mask the encoder outputs for the 2nd attention block 254 | dec_padding_mask = tf.keras.layers.Lambda( 255 | create_padding_mask, output_shape=(1, 1, None), name="dec_padding_mask" 256 | )(inputs) 257 | 258 | enc_outputs = encoder( 259 | vocab_size=vocab_size, 260 | num_layers=num_layers, 261 | units=units, 262 | d_model=d_model, 263 | num_heads=num_heads, 264 | dropout=dropout, 265 | )(inputs=[inputs, enc_padding_mask]) 266 | 267 | dec_outputs = decoder( 268 | vocab_size=vocab_size, 269 | num_layers=num_layers, 270 | units=units, 271 | d_model=d_model, 272 | num_heads=num_heads, 273 | dropout=dropout, 274 | )(inputs=[dec_inputs, enc_outputs, look_ahead_mask, dec_padding_mask]) 275 | 276 | outputs = tf.keras.layers.Dense(units=vocab_size, name="outputs")(dec_outputs) 277 | 278 | return tf.keras.Model(inputs=[inputs, dec_inputs], outputs=outputs, name=name) 279 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ###### Dear English speaking users, unfortunately this project is only in Russian at the moment. Sorry for the inconvenience. 2 | 3 | # DolboNet 4 | ![Привет!](images/re2ypiiv5rg7.png) 5 | 6 | Мы рады представить Вам нашу разработку - **Русскоязычный чат-бот для [Discord] на архитектуре [Transformer]**. 7 | 8 | Нейронная сеть обучена на **36M+** публично доступных сообщениях [наиболее популярных русскоязычных серверов **Discord**][PopularServers] в течение одной эпохи *(5 суток на **GTX 1080**)*. Обучение проходило по принципу: ***какое сообщение вероятнее всего будет отправлено после 10-ти предыдущих*** на уровне [**character trigram embeddings**][N-grams]. 9 | 10 | ![Дота?](images/0l1k6xm0e55j.png) 11 | 12 | Данный бот **не использует** готовую базу данных сообщений, а генерирует новые уникальные сообщения, реализуя концепцию **seq2seq на архитектуре [Transformer]**. Основа сети взята из [этого][Tutorial] руководства по **[TensorFlow 2]**. 13 | 14 | ![Дурачок](images/jbz7uc83s0j1.png) 15 | 16 | > Эта модель была актуальна в 2019 году, но довольно быстро устарела. Вы можете найти что-то более качественное и современное, проследовав [сюда][SOTA]. 17 | 18 | **Поехали!** 🚀 19 | 20 | # Установка 21 | 22 | ![Илон](images/k5e1vy36y49i.png) 23 | 24 | ## Windows 10 64-bit 25 | Протестировано на **2 x 2.6 ГГц CPU + 4 ГБ RAM**. 26 | - Установите [**Python 3.8 64-bit**][Python] 27 | - При установке [**Python 3.8 64-bit**][Python] должны стоять галочки `Install launcher for all users (recommended)` и `Add Python 3.8 to PATH` 28 | - Установите **[Git]** 29 | - Установите [**Visual C++ Redistributable**][Vcredist] - необходим для `scipy` 30 | - Откройте **Git Bash**, щелкнув правой кнопкой мыши по пустому месту внутри папки *(например, **Мои документы**)* и выбрав `Git Bash Here` 31 | - Скачайте репозиторий: `git clone https://github.com/sergree/DolboNet` 32 | - Перейдите в папку: `cd DolboNet` 33 | - Выполните `pip install -r requirements.txt` в появившемся окне 34 | - [Получите][DiscordDevelopers] токен вашего бота - [инструкция][HowToGetToken] 35 | - Отредактируйте файл конфигурации `config.py`, вставив токен бота в `token = "..."` 36 | - ⚠️ Включите **Message Content Intent** в настройках аккаунта бота 37 | ![Message Content Intent](images/bx8pwm8j2njf.png) 38 | - И наконец, запустите бота: `python bot.py` 39 | - *Бот будет загружаться 5-10 минут* 40 | 41 | Бот заработает **только** на **64-разрядной** версии **Windows** и **Python**. 42 | 43 | ## Ubuntu 20.04 LTS 44 | Протестировано на **2 x 2.6 ГГц CPU + 2 ГБ RAM**. 45 | - Скачайте репозиторий: `git clone https://github.com/sergree/DolboNet` 46 | - Перейдите в папку: `cd DolboNet` 47 | - Если ещё не установлен `pip3`, то установите его: `sudo apt install python3-pip` 48 | - Установите зависимости: `pip3 install -r requirements.txt` 49 | - [Получите][DiscordDevelopers] токен вашего бота - [инструкция][HowToGetToken] 50 | - Отредактируйте файл конфигурации: `nano config.py`, вставив токен бота в `token = "..."` 51 | - ⚠️ Включите **Message Content Intent** в настройках аккаунта бота 52 | ![Message Content Intent](images/bx8pwm8j2njf.png) 53 | - И наконец, запустите бота: `python3 bot.py` 54 | - *Бот будет загружаться 5-10 минут* 55 | 56 | ## Поддержка GPU 57 | Если на машине присутствует видеокарта **NVIDIA**, то Вы можете запустить бота используя **CUDA**, что даст прирост в скорости работы. 58 | - Удалите `tensorflow`, если успели установить зависимости: `pip uninstall tensorflow` 59 | - Установите **CUDA® Toolkit** и **cuDNN SDK** - [инструкция][HowToGPU] 60 | - Установите `pip install tensorflow-gpu>=2.3.1` или `pip install -r requirements_gpu.txt` 61 | 62 | ![Хз](images/hywpq6yb16no.png) 63 | 64 | ⚠️ Не советуем настраивать **поддержку GPU**, если Вы делаете это впервые и у Вас нет желания потратить на процесс установки весь вечер ⚠️ 65 | 66 | # Дополнительные настройки 67 | В файле `config.py` можно отредактировать некоторые параметры, чтобы изменить характер и поведение бота: 68 | - `temperature` - [температура семплирования][Temperature] - регулирует характер и разнообразие генерируемого текста 69 | 70 | | Значение | Описание | 71 | |----------|--------------------------------| 72 | | 0.01 | Я знаю только слово **Привет** | 73 | | 0.3 | Попугай-повторюшка | 74 | | 0.65 | По-умолчанию | 75 | | 1.3 | Пьяный поэт | 76 | | 3 | Уснул на клавиатуре | 77 | 78 | Для удобства экспериментирования присутствует команда `!temp значение`, которую можно отправлять в **[Discord]**, чтобы редактировать это значение *на ходу*. Команда работает только у пользователей с привилегией **Администратор**. 79 | 80 | - `mention_prob` - вероятность того, что бот ответит на сообщение, в котором его упомянули. Может принимать значения от `0` до `1`. По умолчанию: `1`, т.е. **100%** 81 | - `no_mention_prob` - вероятность того, что бот ответит на сообщение, в котором его не упоминали. Может принимать значения от `0` до `1`. По умолчанию: `0.2`, т.е. **20%** 82 | - `command_temperature_change` - команда изменения температуры, если не нравится `!temp значение` 😛 83 | - `use_delay` - эмуляция человеческой скорости печати на клавиатуре, по-умолчанию `False`, т.к. на **CPU** процесс генерации и так не быстрый 84 | - `discord_game_name` - статус бота в **[Discord]** 85 | 86 | Остальные параметры лучше не редактировать. 87 | 88 | # Кофе 89 | ![Кофе](images/khrxc9atjbni.png) 90 | 91 | ☕ Если Вы заинтересованы в развитии проекта, Вы можете [купить мне кофе][BMC]. ☕ 92 | 93 | **Спасибо!** 🙏 94 | 95 | # FAQ 96 | 97 | > _У меня половина сервера таких долбонетов, зачем нужен ещё один?_ 98 | 99 | - Он может помогать в модерации: 100 | 101 | ![Плохо](images/b9m7plcblp71.png) 102 | 103 | - Иногда даже очень хорошо: 104 | 105 | ![ПоФорме](images/e096csal1tbl.png) 106 | 107 | - Любит аниме: 108 | 109 | ![Anime](images/dz749dknry6v.png) 110 | 111 | - Делится свежими новостями: 112 | 113 | ![News](images/zt9ylu1hceqq.png) 114 | 115 | - А ещё он не даст заскучать: 116 | 117 | ![Pings](images/2iw73365ipkj.png) 118 | 119 | - Ну и, конечно же, у него отличный вкус: 120 | 121 | ![Blind](images/ty7546sn8nrx.png) 122 | 123 | А если серьёзно, причина только [одна][Tweet]. 124 | 125 | > _Хостите ли вы этого бота? Могу ли я обойтись публичной версией? Дайте ссылку!_ 126 | 127 | 128 | ~~[Ссылка][BotInvite]. Бот доступен не всегда и иногда отвечает медленно.~~ 129 | Мы не хостим публичную версию бота. Чтобы он появился на Вашем **[Discord]** сервере, его необходимо [установить][Setup]. 130 | 131 | > _На каких серверах этот бот уже есть?_ 132 | 133 | Мы знаем, что бот уже хостится здесь: 134 | - **[! REPUBLIC OF PEPESTAN & ITS CITIZENS][Server1]** 135 | - **[FUNCLUB][Server4]** 136 | - **[LeviaFun][Server3]** 137 | - **[! Molecular Dream World ☘][Server2]** 138 | 139 | [Напишите нам], чтобы попасть в этот список. 140 | 141 | > _Что-то он в основном бессвязный бред отправляет._ 🤔 142 | 143 | ![Бред](images/xhsnyv7dzi3v.png) 144 | 145 | Да, есть такое. Но иногда получается забавно. 146 | 147 | > _Это же бесполезная фигня, вы понимаете?_ 148 | 149 | Конечно. Как и [многое другое в нашем современном мире][Trends]. 150 | 151 | > _Бот отправил мне оскорбление или угрозу! Беспредел!_ 😠 152 | 153 | ![Юра](images/0rulgz9m75fc.png) 154 | 155 | Нейронная сеть бота лишь отражает публичные данные, на которых проходило обучение. Возможно, это тревожный звоночек о том, [что стало с нашим обществом][Rebyata]. В любом случае, мы не хотели. 156 | 157 | > _Что насчёт английского языка?_ 158 | 159 | ![Транслит](images/t6co1mos6p6p.png) 160 | 161 | На данном этапе мы решили не расходовать ёмкость сети на латинские триграммы. Латиница автоматически транслитерируется в кириллицу с помощью **[opendatakosovo/cyrillic-transliteration]**. *Мы тестировали много подобных библиотек, [эта][opendatakosovo/cyrillic-transliteration] - самая быстрая.* 162 | 163 | > _Почему триграммы?_ 164 | 165 | Потому-что [великий и могучий]. Идея, конечно же, не наша, а взята из [этой книги][Book]. 166 | 167 | > _Может было бы лучше использовать [стемминг]?_ 168 | 169 | В данном кейсе нет. Так как лдюи в чатах пиушт с очепятками, а инагда с ашебками. A inogda translitom, ile fse vmesti. 🤪 170 | 171 | *Другое дело википедию или новостные ленты разбирать.* 172 | 173 | > _Он и эмодзи умеет отправлять?_ 174 | 175 | ![Emoji](images/lidikqfnw71o.png) 176 | 177 | Да. Только пока рандомно. *Всем кастомным эмодзи присвоен единый токен в словаре.* В будущем есть планы привязать **[CNN]** с классификатором. 178 | 179 | > _Вы просто скопировали гайд для **[TensorFlow 2]**, что вы сделали сами?_ 180 | 181 | - Алгоритм токенизации русского текста и разбора сущностей **[Discord]**: упоминания пользователей / ролей / каналов, ссылки, эмодзи и т.д. 182 | - Перелопатили уйму доступных реализаций **[Transformer]** 183 | - Нашли [подходящую реализацию][Tutorial] и связали её с нашим токенизатором и **[Discord API]** 184 | - Спарсили **36M+** публичных сообщений русскоязычного **[Discord]** комьюнити и обучили [трансформер][Transformer] на нём 185 | - Напечатали *этот текст* 186 | 187 | > _А как же **[LSTM]**?_ 188 | 189 | Мы просто оставим [это здесь][TransformerExplained]. 190 | 191 | > _Что дальше?_ 192 | 193 | - Больше парсить, дольше обучать 194 | - **[CNN]** для классификации эмодзи без учителя 195 | - Пробуем **[Universal Transformer]** 196 | - И **[Transformer XL]** 197 | - Если не закончится [кофе][BMC] 198 | 199 | ![Пошла](images/qb8hwuno3q4l.png) 200 | 201 | 🤖 202 | 203 | [Discord]: https://discordapp.com/ 204 | [DiscordDevelopers]: https://discordapp.com/developers/applications/ 205 | [Tutorial]: https://github.com/tensorflow/examples/blob/master/community/en/transformer_chatbot.ipynb 206 | [TensorFlow 2]: https://www.tensorflow.org 207 | [HowToGetToken]: https://www.writebots.com/discord-bot-token/ 208 | [HowToGPU]: https://www.tensorflow.org/install/gpu#software_requirements 209 | [Transformer]: https://arxiv.org/abs/1706.03762 210 | [PopularServers]: https://discord-server.com/?language=ru&page=1&rowsOnPage=50&sort=count_desc 211 | [N-grams]: https://ru.wikipedia.org/wiki/N-%D0%B3%D1%80%D0%B0%D0%BC%D0%BC%D0%B0 212 | [Python]: https://www.python.org/ftp/python/3.8.6/python-3.8.6-amd64.exe 213 | [Git]: https://gitforwindows.org/ 214 | [Vcredist]: https://www.microsoft.com/en-us/download/details.aspx?id=48145 215 | [BMC]: https://boosty.to/sergree 216 | [opendatakosovo/cyrillic-transliteration]: https://github.com/opendatakosovo/cyrillic-transliteration 217 | [Великий и могучий]: https://ru.wikipedia.org/wiki/%D0%A0%D1%83%D1%81%D1%81%D0%BA%D0%B8%D0%B9_%D1%8F%D0%B7%D1%8B%D0%BA 218 | [стемминг]: https://ru.wikipedia.org/wiki/%D0%A1%D1%82%D0%B5%D0%BC%D0%BC%D0%B8%D0%BD%D0%B3 219 | [Book]: https://www.piter.com/product/glubokoe-obuchenie 220 | [Rebyata]: http://lurkmore.to/%D0%91%D1%8B%D0%B4%D0%BB%D0%BE 221 | [CNN]: https://ru.wikipedia.org/wiki/%D0%A1%D0%B2%D1%91%D1%80%D1%82%D0%BE%D1%87%D0%BD%D0%B0%D1%8F_%D0%BD%D0%B5%D0%B9%D1%80%D0%BE%D0%BD%D0%BD%D0%B0%D1%8F_%D1%81%D0%B5%D1%82%D1%8C 222 | [Discord API]: https://github.com/Rapptz/discord.py 223 | [Tweet]: https://twitter.com/elonmusk/status/1095574487104315392 224 | [Temperature]: https://cs.stackexchange.com/questions/79241/what-is-temperature-in-lstm-and-neural-networks-generally 225 | [Trends]: http://lurkmore.to/%D0%9C%D0%BE%D0%B4%D0%B0 226 | [LSTM]: https://ru.wikipedia.org/wiki/%D0%94%D0%BE%D0%BB%D0%B3%D0%B0%D1%8F_%D0%BA%D1%80%D0%B0%D1%82%D0%BA%D0%BE%D1%81%D1%80%D0%BE%D1%87%D0%BD%D0%B0%D1%8F_%D0%BF%D0%B0%D0%BC%D1%8F%D1%82%D1%8C 227 | [TransformerExplained]: https://habr.com/ru/post/341240/ 228 | [Universal Transformer]: https://arxiv.org/abs/1807.03819 229 | [Transformer XL]: https://arxiv.org/abs/1901.02860 230 | [Git LFS]: https://git-lfs.github.com/ 231 | [Server1]: https://discordapp.com/invite/TVw8NKv 232 | [Server2]: https://discordapp.com/invite/HmK6xsS 233 | [Server3]: https://discordapp.com/invite/XUJq5WR 234 | [Server4]: https://discordapp.com/invite/mUsxsST 235 | [BotInvite]: https://discordapp.com/api/oauth2/authorize?client_id=584636018125176834&permissions=3072&scope=bot 236 | [Setup]: https://github.com/sergree/DolboNet#%D1%83%D1%81%D1%82%D0%B0%D0%BD%D0%BE%D0%B2%D0%BA%D0%B0 237 | [Напишите нам]: mailto:sergree@vk.com 238 | [SOTA]: https://paperswithcode.com/sota 239 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------