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import os
import streamlit as st
import transformers
import torch
from typing import List, Dict
model_name = st.selectbox(
'Выберите модель',
('tinkoff-ai/crossencoder-tiny', 'tinkoff-ai/crossencoder-medium', 'tinkoff-ai/crossencoder-large')
)
auth_token = os.environ.get('TOKEN') or True
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()
context_3 = st.text_input('Настя', 'Привет')
context_2 = st.text_input('Диалоговый агент', 'Здарова')
context_1 = st.text_input('Настя', 'Как жизнь?')
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
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()
st.metric(
label='Вероятность того, что последний ответ диалогового агента релевантный',
value=probas[0]
)
st.metric(
label='Вероятность того, что последний ответ диалогового агента специфичный',
value=probas[1]
)
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