Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
mathematics
scientific-papers
retrieval
matryoshka
text-embeddings-inference
Instructions to use RobBobin/math-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RobBobin/math-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RobBobin/math-embed") 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] - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -92,4 +92,4 @@ This is a form of **knowledge distillation** — a large language model's unders
|
|
| 92 |
|
| 93 |
## Citation
|
| 94 |
|
| 95 |
-
See the accompanying paper: *Knowledge-Graph-Guided Fine-Tuning of Embedding Models for Mathematical Document Retrieval*
|
|
|
|
| 92 |
|
| 93 |
## Citation
|
| 94 |
|
| 95 |
+
See the accompanying paper: [*Knowledge-Graph-Guided Fine-Tuning of Embedding Models for Mathematical Document Retrieval*](https://huggingface.co/RobBobin/math-embed/blob/main/paper/math_embeddings.pdf)
|