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_", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 480a9c5b408f3a5f51e21ca8381c8237cf55b59bb6db02be4f59e33eceee7cf0
- Size of remote file:
- 17.1 MB
- SHA256:
- 40e4a12784b3e7ee5c5c373c28a22f02497b30a4ea39d9548ee0f3c6a31e9ca9
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