Create README.md

README.md

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+---
+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.*
+-->
+
+<!--
+### 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 |