đ Russian Chit-chat, Deductive and Common Sense reasoning model
This model serves as the core of a prototype dialogue system with two main functions.
đ Quick Start
The model has two main functions:
⨠Features
- Chat Replication Generation: It takes the dialogue history (1 - 10 previous utterances) as input to generate chat responses.
- Hi, how are you?
- Hi, not so good.
- <<< This is the response we expect from the model >>>
- Answer Deduction: It can deduce answers to given questions based on additional facts or common sense. Relevant facts are assumed to be retrieved from an external knowledge base using another model, such as sbert_pq. The model will construct a grammatical and concise answer using the provided facts and question text.
- Today is September 15th. What month is it now?
- September
The model can also perform syllogistic reasoning and solve simple arithmetic problems.
đĻ Installation
No specific installation steps are provided in the original document, so this section is skipped.
đģ Usage Examples
Basic Usage
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
device = "cuda" if torch.cuda.is_available() else "cpu"
model_name = "inkoziev/rugpt_chitchat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.add_special_tokens({'bos_token': '<s>', 'eos_token': '</s>', 'pad_token': '<pad>'})
model = AutoModelForCausalLM.from_pretrained(model_name)
model.to(device)
model.eval()
input_text = """<s>- Hi! What are you doing?
- Hi :) I'm in a taxi
-"""
encoded_prompt = tokenizer.encode(input_text, add_special_tokens=False, return_tensors="pt").to(device)
output_sequences = model.generate(input_ids=encoded_prompt, max_length=100, num_return_sequences=1, pad_token_id=tokenizer.pad_token_id)
text = tokenizer.decode(output_sequences[0].tolist(), clean_up_tokenization_spaces=True)[len(input_text)+1:]
text = text[: text.find('</s>')]
print(text)
đ Documentation
Model Variants and Metrics
The currently released model has 760 million parameters, similar to sberbank-ai/rugpt3large_based_on_gpt2. The following table shows the accuracy of solving arithmetic problems on a held - out test set:
Property |
Details |
Model Type |
The model has two main functions: chat response generation and answer deduction based on facts or common sense. It can also perform syllogistic reasoning and solve simple arithmetic problems. |
Training Data |
Not provided in the original document. |
Arithmetic Accuracy |
|
base model |
arith. accuracy |
--------------------------------------- |
--------------- |
sberbank-ai/rugpt3large_based_on_gpt2 |
0.91 |
sberbank-ai/rugpt3medium_based_on_gpt2 |
0.70 |
sberbank-ai/rugpt3small_based_on_gpt2 |
0.58 |
tinkoff-ai/ruDialoGPT-small |
0.44 |
tinkoff-ai/ruDialoGPT-medium |
0.69 |
đ License
The model is under the unlicense
license.
Contacts
If you have any questions about using this model or suggestions for its improvement, please email mentalcomputing@gmail.com
Citation:
@MISC{rugpt_chitchat,
author = {Ilya Koziev},
title = {Russian Chit-chat with Common sence Reasoning},
url = {https://huggingface.co/inkoziev/rugpt_chitchat},
year = 2022
}