Den4ikAI / ruBert-tiny-replicas-classifier

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text-classification

Introduction of ruBert-tiny-replicas-classifier

Model Details of ruBert-tiny-replicas-classifier

Den4ikAI/ruBert-tiny-replicas-classifier

Описание классов:

  1. about_user - реагирует, когда пользователь говорит о себе. Например, "меня зовут андрей"
  2. question - реагирует на вопросы
  3. instruct - реагирует на вопросы, ответ на которые представляет собой инструкцию. Например, "как установить windows, как приготовить борщ"
  4. about_system - реагирует на вопросы о личности ассистента. Например, "как тебя зовут, ты кто такая"
  5. problem - реагирует на реплики, где пользователь рассказывает о своих проблемах. Например, "у меня болит зуб, мне проткнули колесо"
  6. dialogue - реагирует на диалоговые реплики. Например, "привет"

Примечание: модель обучалась без знаков '?'

Использование

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained('Den4ikAI/ruBert-tiny-replicas-classifier')
model = AutoModelForSequenceClassification.from_pretrained('Den4ikAI/ruBert-tiny-replicas-classifier')
model.to(device)
model.eval()

classes = ['instruct', 'question', 'dialogue', 'problem', 'about_system', 'about_user']


def get_sentence_type(text):
    inputs = tokenizer(text, max_length=512, add_special_tokens=False, return_tensors='pt').to(device)
    with torch.no_grad():
        logits = model(**inputs).logits
        probas = list(torch.sigmoid(logits)[0].cpu().detach().numpy())
    out = classes[probas.index(max(probas))]
    return out

while 1:
  print(get_sentence_type(input(":> ")))

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