metadata
license: mit
pipeline_tag: text-generation
widget:
- text: '@@ПЕРВЫЙ@@ привет @@ВТОРОЙ@@ привет @@ПЕРВЫЙ@@ как дела? @@ВТОРОЙ@@'
example_title: how r u
- text: '@@ПЕРВЫЙ@@ что ты делал на выходных? @@ВТОРОЙ@@'
example_title: wyd
language:
- ru
tags:
- conversational
This generation model is based on sberbank-ai/rugpt3medium_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-medium')
model = AutoModelWithLMHead.from_pretrained('tinkoff-ai/ruDialoGPT-medium')
inputs = tokenizer('@@ПЕРВЫЙ@@ привет @@ВТОРОЙ@@ привет @@ПЕРВЫЙ@@ как дела? @@ВТОРОЙ@@', return_tensors='pt')
with torch.inference_mode():
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
)
context_with_response = [tokenizer.decode(sample_token_ids) for sample_token_ids in generated_token_ids]
context_with_response