Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use basic-go/math-ru-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use basic-go/math-ru-sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("basic-go/math-ru-sbert") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use basic-go/math-ru-sbert with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("basic-go/math-ru-sbert") model = AutoModel.from_pretrained("basic-go/math-ru-sbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from basic-go/math-ru-sbert: direct link, hf CLI and curl.
- Browser
- Download file 1.36 kB
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https://huggingface.co/basic-go/math-ru-sbert/resolve/main/README.md
- Command line
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hf download hf://basic-go/math-ru-sbert/README.md
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curl -L -o README.md https://huggingface.co/basic-go/math-ru-sbert/resolve/main/README.md
1.36 kB
| library_name: sentence-transformers | |
| pipeline_tag: sentence-similarity | |
| tags: | |
| - sentence-transformers | |
| - feature-extraction | |
| - sentence-similarity | |
| - transformers | |
| # basic-go/math-ru-sbert | |
| Это [Sentence Transformer](https://www.SBERT.net), предназначенный для векторизации текстов с математическими выражениями. Полученные векторные представления могут использоваться для выявления дубликатов, оценки семантического сходства и информационного поиска. | |
| ## Использование | |
| Вместе с предустановленной библиотекой [sentence-transformers](https://www.SBERT.net) модель можно использовать следующим образом: | |
| ```python | |
| from sentence_transformers import SentenceTransformer | |
| sentences = [r"\(\int \rho(x) dx\)"] | |
| model = SentenceTransformer('basic-go/math-ru-sbert') | |
| embeddings = model.encode(sentences) | |
| print(embeddings) | |
| ``` | |
| Вместе с тем рекомендуется использовать модель в составе библиотеки [Emma](https://github.com/basic-go-ahead/emma) для актуальной пред- и постобработки данных. | |