Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use math-similarity/Bert-MLM_arXiv-MP-class_zbMath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use math-similarity/Bert-MLM_arXiv-MP-class_zbMath with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("math-similarity/Bert-MLM_arXiv-MP-class_zbMath") 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 math-similarity/Bert-MLM_arXiv-MP-class_zbMath with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("math-similarity/Bert-MLM_arXiv-MP-class_zbMath") model = AutoModel.from_pretrained("math-similarity/Bert-MLM_arXiv-MP-class_zbMath") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ff8e39ec95d3fba56533acb6000daf7ca803445aa3982fd254cfe8618334fb5
- Size of remote file:
- 438 MB
- SHA256:
- ce4e913cdb60061bd6fcddcff8a51ce53d708537c2d0834ae7436180d4219198
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