README.md

metadata

language:
  - ru
pipeline_tag: sentence-similarity
tags:
  - russian
  - pretraining
  - embeddings
  - tiny
  - feature-extraction
  - sentence-similarity
  - sentence-transformers
  - transformers
datasets:
  - IlyaGusev/gazeta
  - zloelias/lenta-ru
license: mit
base_model: cointegrated/rubert-tiny2

Базовый Bert для Semantic text similarity (STS) на CPU

Базовая модель BERT для расчетов компактных эмбеддингов предложений на русском языке. Модель основана на cointegrated/rubert-tiny2 - имеет аналогичные размеры контекста (2048) и ембеддинга (312), количество слоев увеличено с 3 до 7.

Использование модели с библиотекой transformers:

# pip install transformers sentencepiece
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("sergeyzh/rubert-mini-sts")
model = AutoModel.from_pretrained("sergeyzh/rubert-mini-sts")
# model.cuda()  # uncomment it if you have a GPU

def embed_bert_cls(text, model, tokenizer):
    t = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
    with torch.no_grad():
        model_output = model(**{k: v.to(model.device) for k, v in t.items()})
    embeddings = model_output.last_hidden_state[:, 0, :]
    embeddings = torch.nn.functional.normalize(embeddings)
    return embeddings[0].cpu().numpy()

print(embed_bert_cls('привет мир', model, tokenizer).shape)
# (312,)

Использование с sentence_transformers:

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer('sergeyzh/rubert-mini-sts')

sentences = ["привет мир", "hello world", "здравствуй вселенная"]
embeddings = model.encode(sentences)
print(util.dot_score(embeddings, embeddings))

Метрики

Оценки модели на бенчмарке encodechka:

Модель	STS	PI	NLI	SA	TI
intfloat/multilingual-e5-large	0.862	0.727	0.473	0.810	0.979
sergeyzh/LaBSE-ru-sts	0.845	0.737	0.481	0.805	0.957
sergeyzh/rubert-mini-sts	0.815	0.723	0.477	0.791	0.949
sergeyzh/rubert-tiny-sts	0.797	0.702	0.453	0.778	0.946
Tochka-AI/ruRoPEBert-e5-base-512	0.793	0.704	0.457	0.803	0.970
cointegrated/LaBSE-en-ru	0.794	0.659	0.431	0.761	0.946
cointegrated/rubert-tiny2	0.750	0.651	0.417	0.737	0.937

Задачи:

• Semantic text similarity (STS);
• Paraphrase identification (PI);
• Natural language inference (NLI);
• Sentiment analysis (SA);
• Toxicity identification (TI).

Быстродействие и размеры

На бенчмарке encodechka:

Модель	CPU	GPU	size	dim	n_ctx	n_vocab
intfloat/multilingual-e5-large	149.026	15.629	2136	1024	514	250002
sergeyzh/LaBSE-ru-sts	42.835	8.561	490	768	512	55083
sergeyzh/rubert-mini-sts	6.417	5.517	123	312	2048	83828
sergeyzh/rubert-tiny-sts	3.208	3.379	111	312	2048	83828
Tochka-AI/ruRoPEBert-e5-base-512	43.314	9.338	532	768	512	69382
cointegrated/LaBSE-en-ru	42.867	8.549	490	768	512	55083
cointegrated/rubert-tiny2	3.212	3.384	111	312	2048	83828