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| import os | |
| import streamlit as st | |
| import transformers | |
| import torch | |
| import tokenizers | |
| from typing import List, Dict | |
| st.subheader('Эта демонстрация позволяет поэксперементировать с моделями, которые оценивают, насколько предлагаемый ответ подходит к контексту диалога.') | |
| model_name = st.selectbox( | |
| 'Выберите модель', | |
| ('tinkoff-ai/response-quality-classifier-tiny', 'tinkoff-ai/response-quality-classifier-base', 'tinkoff-ai/response-quality-classifier-large') | |
| ) | |
| auth_token = os.environ.get('TOKEN') or True | |
| def load_model(model_name: str): | |
| with st.spinner('Loading models...'): | |
| tokenizer = transformers.AutoTokenizer.from_pretrained(model_name, use_auth_token=auth_token) | |
| model = transformers.AutoModelForSequenceClassification.from_pretrained(model_name, use_auth_token=auth_token) | |
| if torch.cuda.is_available(): | |
| model = model.cuda() | |
| return tokenizer, model | |
| context_3 = 'привет' | |
| context_2 = 'привет!' | |
| context_1 = 'как дела?' | |
| st.markdown('👱🏻♀️ **Настя**: ' + context_3) | |
| st.markdown('🤖 **Диалоговый агент**: ' + context_2) | |
| st.markdown('👱🏻♀️ **Настя**: ' + context_1) | |
| response = st.text_input('🤖 Диалоговый агент:', 'норм') | |
| sample = { | |
| 'context_3': context_3, | |
| 'context_2': context_2, | |
| 'context_1': context_1, | |
| 'response': response | |
| } | |
| SEP_TOKEN = '[SEP]' | |
| CLS_TOKEN = '[CLS]' | |
| RESPONSE_TOKEN = '[RESPONSE_TOKEN]' | |
| MAX_SEQ_LENGTH = 128 | |
| sorted_dialog_columns = ['context_3', 'context_2', 'context_1', 'response'] | |
| def tokenize_dialog_data( | |
| tokenizer: transformers.PreTrainedTokenizer, | |
| sample: Dict, | |
| max_seq_length: int, | |
| sorted_dialog_columns: List, | |
| ): | |
| """ | |
| Tokenize both contexts and response of dialog data separately | |
| """ | |
| len_message_history = len(sorted_dialog_columns) | |
| max_seq_length = min(max_seq_length, tokenizer.model_max_length) | |
| max_each_message_length = max_seq_length // len_message_history - 1 | |
| messages = [sample[k] for k in sorted_dialog_columns] | |
| result = {model_input_name: [] for model_input_name in tokenizer.model_input_names} | |
| messages = [str(message) if message is not None else '' for message in messages] | |
| tokens = tokenizer( | |
| messages, padding=False, max_length=max_each_message_length, truncation=True, add_special_tokens=False | |
| ) | |
| for model_input_name in tokens.keys(): | |
| result[model_input_name].extend(tokens[model_input_name]) | |
| return result | |
| def merge_dialog_data( | |
| tokenizer: transformers.PreTrainedTokenizer, | |
| sample: Dict | |
| ): | |
| cls_token = tokenizer(CLS_TOKEN, add_special_tokens=False) | |
| sep_token = tokenizer(SEP_TOKEN, add_special_tokens=False) | |
| response_token = tokenizer(RESPONSE_TOKEN, add_special_tokens=False) | |
| model_input_names = tokenizer.model_input_names | |
| result = {} | |
| for model_input_name in model_input_names: | |
| tokens = [] | |
| tokens.extend(cls_token[model_input_name]) | |
| for i, message in enumerate(sample[model_input_name]): | |
| tokens.extend(message) | |
| if i < len(sample[model_input_name]) - 2: | |
| tokens.extend(sep_token[model_input_name]) | |
| elif i == len(sample[model_input_name]) - 2: | |
| tokens.extend(response_token[model_input_name]) | |
| result[model_input_name] = torch.tensor([tokens]) | |
| if torch.cuda.is_available(): | |
| result[model_input_name] = result[model_input_name].cuda() | |
| return result | |
| def inference(model_name: str, sample: dict): | |
| tokenizer, model = load_model(model_name) | |
| tokenized_dialog = tokenize_dialog_data(tokenizer, sample, MAX_SEQ_LENGTH, sorted_dialog_columns) | |
| tokens = merge_dialog_data(tokenizer, tokenized_dialog) | |
| with torch.inference_mode(): | |
| logits = model(**tokens).logits | |
| probas = torch.sigmoid(logits)[0].cpu().detach().numpy().tolist() | |
| return probas | |
| with st.spinner('Running inference...'): | |
| probas = inference(model_name, sample) | |
| st.metric( | |
| label='Вероятность того, что последний ответ диалогового агента релевантный', | |
| value=round(probas[0], 3) | |
| ) | |
| st.metric( | |
| label='Вероятность того, что последний ответ диалогового агента вовлечённый', | |
| value=round(probas[1], 3) | |
| ) | |