by ai-forever
Open source · 33k downloads · 78 likes
The ru-en-RoSBERTa model is a text embedding model designed for Russian, based on a fine-tuned RoBERTa architecture trained on millions of Russian-English text pairs. It generates vector representations of texts suitable for various tasks such as information retrieval, classification, or thematic clustering, using specific prefixes. Its capabilities include retrieving relevant documents, detecting paraphrases, and analyzing semantic similarity between texts. This model stands out for its versatility and ability to handle bilingual texts, though its performance in English is not guaranteed. It is optimized for Russian texts, with a limit of 512 tokens per input.
The ru-en-RoSBERTa is a general text embedding model for Russian. The model is based on ruRoBERTa and fine-tuned with ~4M pairs of supervised, synthetic and unsupervised data in Russian and English. Tokenizer supports some English tokens from RoBERTa tokenizer.
For more model details please refer to our article.
The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task.
We use the following basic rules to choose a prefix:
"search_query: " and "search_document: " prefixes are for answer or relevant paragraph retrieval"classification: " prefix is for symmetric paraphrasing related tasks (STS, NLI, Bitext Mining)"clustering: " prefix is for any tasks that rely on thematic features (topic classification, title-body retrieval)To better tailor the model to your needs, you can fine-tune it with relevant high-quality Russian and English datasets.
Below are examples of texts encoding using the Transformers and SentenceTransformers libraries.
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def pool(hidden_state, mask, pooling_method="cls"):
if pooling_method == "mean":
s = torch.sum(hidden_state * mask.unsqueeze(-1).float(), dim=1)
d = mask.sum(axis=1, keepdim=True).float()
return s / d
elif pooling_method == "cls":
return hidden_state[:, 0]
inputs = [
#
"classification: Он нам и <unk> не нужон ваш Интернет!",
"clustering: В Ярославской области разрешили работу бань, но без посетителей",
"search_query: Сколько программистов нужно, чтобы вкрутить лампочку?",
#
"classification: What a time to be alive!",
"clustering: Ярославским баням разрешили работать без посетителей",
"search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.",
]
tokenizer = AutoTokenizer.from_pretrained("ai-forever/ru-en-RoSBERTa")
model = AutoModel.from_pretrained("ai-forever/ru-en-RoSBERTa")
tokenized_inputs = tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
outputs = model(**tokenized_inputs)
embeddings = pool(
outputs.last_hidden_state,
tokenized_inputs["attention_mask"],
pooling_method="cls" # or try "mean"
)
embeddings = F.normalize(embeddings, p=2, dim=1)
sim_scores = embeddings[:3] @ embeddings[3:].T
print(sim_scores.diag().tolist())
# [0.4796873927116394, 0.9409002065658569, 0.7761015892028809]
from sentence_transformers import SentenceTransformer
inputs = [
#
"classification: Он нам и <unk> не нужон ваш Интернет!",
"clustering: В Ярославской области разрешили работу бань, но без посетителей",
"search_query: Сколько программистов нужно, чтобы вкрутить лампочку?",
#
"classification: What a time to be alive!",
"clustering: Ярославским баням разрешили работать без посетителей",
"search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.",
]
# loads model with CLS pooling
model = SentenceTransformer("ai-forever/ru-en-RoSBERTa")
# embeddings are normalized by default
embeddings = model.encode(inputs, convert_to_tensor=True)
sim_scores = embeddings[:3] @ embeddings[3:].T
print(sim_scores.diag().tolist())
# [0.47968706488609314, 0.940900444984436, 0.7761018872261047]
or using prompts (sentence-transformers>=2.4.0):
from sentence_transformers import SentenceTransformer
# loads model with CLS pooling
model = SentenceTransformer("ai-forever/ru-en-RoSBERTa")
classification = model.encode(["Он нам и <unk> не нужон ваш Интернет!", "What a time to be alive!"], prompt_name="classification")
print(classification[0] @ classification[1].T) # 0.47968706488609314
clustering = model.encode(["В Ярославской области разрешили работу бань, но без посетителей", "Ярославским баням разрешили работать без посетителей"], prompt_name="clustering")
print(clustering[0] @ clustering[1].T) # 0.940900444984436
query_embedding = model.encode("Сколько программистов нужно, чтобы вкрутить лампочку?", prompt_name="search_query")
document_embedding = model.encode("Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.", prompt_name="search_document")
print(query_embedding @ document_embedding.T) # 0.7761018872261047
@misc{snegirev2024russianfocusedembeddersexplorationrumteb,
title={The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design},
author={Artem Snegirev and Maria Tikhonova and Anna Maksimova and Alena Fenogenova and Alexander Abramov},
year={2024},
eprint={2408.12503},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2408.12503},
}
The model is designed to process texts in Russian, the quality in English is unknown. Maximum input text length is limited to 512 tokens.