---
language:
- ru
- en
- zh
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
widget: []
license: mit
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 312-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 312 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 312, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("mlsa-iai-msu-lab/sci-rus-tiny3.1")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 312]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

### Metrics

RuSciBench few shoot

| model name                          |    ru |    en |   total avg |
|:------------------------------------|------:|------:|------------:|
| mlsa-iai-msu-lab/sci-rus-tiny       | 28.37 | 27.87 |       60.79 |
| mlsa-iai-msu-lab/sci-rus-small-cite | 38.4  | 38.68 |       67.34 |
| mlsa-iai-msu-lab/sci-rus-tiny3-cite | 39.36 | 39.5  |       67.58 |
| **mlsa-iai-msu-lab/sci-rus-tiny3.5**    | 38.93 | 39.27 |       69.17 |
| mlsa-iai-msu-lab/sci-rus-tiny3.1    | 39    | 39.92 |       69.36 |

RuSciBench

| model_name                          |    ru |    en |   total avg |
|:------------------------------------|------:|------:|------------:|
| mlsa-iai-msu-lab/sci-rus-tiny       | 35.77 | 35.21 |       64.48 |
| mlsa-iai-msu-lab/sci-rus-tiny3-cite | 44.55 | 44.79 |       70.2  |
| mlsa-iai-msu-lab/sci-rus-small-cite | 44.53 | 44.81 |       70.4  |
| mlsa-iai-msu-lab/sci-rus-tiny3.1    | 44.52 | 45.15 |       72.05 |
| **mlsa-iai-msu-lab/sci-rus-tiny3.5**    | 44.48 | 45.4  |       72.09 |