Hugging Face's logo

sergeyzh
/
rubert-mini-sts

Sentence Similarity
sentence-transformers
Safetensors
Transformers
Russian
bert
pretraining
russian
embeddings
tiny
feature-extraction
text-embeddings-inference
Inference Endpoints
Model card Files Community
rubert-mini-sts
File size: 5,070 Bytes 
c70dbfd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
 
---

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](https://huggingface.co/cointegrated/rubert-tiny2) - имеет аналогичные размеры контекста (2048) и ембеддинга (312), количество слоев увеличено с 3 до 7.


## Использование модели с библиотекой `transformers`:

```python

# 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`:

```Python

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](https://github.com/avidale/encodechka):



| Модель                           | STS       | PI        | NLI       | SA        | TI        |

|:---------------------------------|:---------:|:---------:|:---------:|:---------:|:---------:|

| [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)   |   0.862   |   0.727   |   0.473   |   0.810   |   0.979   |

| [sergeyzh/LaBSE-ru-sts](https://huggingface.co/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](https://huggingface.co/sergeyzh/rubert-tiny-sts)     |   0.797   |   0.702   |   0.453   |   0.778   |   0.946   |

| [Tochka-AI/ruRoPEBert-e5-base-512](https://huggingface.co/Tochka-AI/ruRoPEBert-e5-base-512) |   0.793   |   0.704   |   0.457   |   0.803   |   0.970   |

| [cointegrated/LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru)         |   0.794   |   0.659   |   0.431   |   0.761   |   0.946   |

| [cointegrated/rubert-tiny2](https://huggingface.co/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](https://github.com/avidale/encodechka):



| Модель                           | CPU       | GPU       | size      | dim       | n_ctx     | n_vocab   |

|:---------------------------------|----------:|----------:|----------:|----------:|----------:|----------:|

| [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)   | 149.026   |  15.629   |   2136    |   1024    |    514    |  250002   |

| [sergeyzh/LaBSE-ru-sts](https://huggingface.co/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](https://huggingface.co/sergeyzh/rubert-tiny-sts)     |   3.208   |   3.379   |    111    |    312    |    2048   |   83828   |

| [Tochka-AI/ruRoPEBert-e5-base-512](https://huggingface.co/Tochka-AI/ruRoPEBert-e5-base-512) |  43.314   |   9.338   |    532    |    768    |    512    |   69382   |

| [cointegrated/LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru)         |  42.867   |   8.549   |    490    |    768    |    512    |   55083   |

| [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2)        |   3.212   |   3.384   |    111    |    312    |    2048   |   83828   |