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This generation model is based on sberbank-ai/rugpt3small_based_on_gpt2. It's trained on large corpus of dialog data and can be used for buildning generative conversational agents

The model was trained with context size 3

On a private validation set we calculated metrics introduced in this paper:

  • Sensibleness: Crowdsourcers were asked whether model's response makes sense given the context
  • Specificity: Crowdsourcers were asked whether model's response is specific for given context, in other words we don't want our model to give general and boring responses
  • SSA which is the average of two metrics above (Sensibleness Specificity Average)
sensibleness specificity SSA
tinkoff-ai/ruDialoGPT-small 0.64 0.5 0.57
tinkoff-ai/ruDialoGPT-medium 0.78 0.69 0.735

How to use:

import torch
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained('tinkoff-ai/ruDialoGPT-small')
model = AutoModelWithLMHead.from_pretrained('tinkoff-ai/ruDialoGPT-small')
inputs = tokenizer('@@ПЕРВЫЙ@@ привет @@ВТОРОЙ@@ привет @@ПЕРВЫЙ@@ как дела? @@ВТОРОЙ@@', return_tensors='pt')
generated_token_ids = model.generate(
    **inputs,
    top_k=10,
    top_p=0.95,
    num_beams=3,
    num_return_sequences=3,
    do_sample=True,
    no_repeat_ngram_size=2,
    temperature=1.2,
    repetition_penalty=1.2,
    length_penalty=1.0,
    eos_token_id=50257,
    max_new_tokens=40
)
context_with_response = [tokenizer.decode(sample_token_ids) for sample_token_ids in generated_token_ids]
context_with_response
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