Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use peter2000/bmz_topics_ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use peter2000/bmz_topics_ with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("peter2000/bmz_topics_") 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 peter2000/bmz_topics_ with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("peter2000/bmz_topics_") model = AutoModel.from_pretrained("peter2000/bmz_topics_") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94c6a41259a9e456ed498450d3810e79e7f3e4542ecb36b3cbb3d8d04f8afb13
- Size of remote file:
- 1.11 GB
- SHA256:
- fcc9f7479d86ec633a0c7180a99e5334625c2c60fa63d3c32469f14086d93271
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