Sentence Similarity
sentence-transformers
Safetensors
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use nanalysenko/model_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nanalysenko/model_4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nanalysenko/model_4") 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 nanalysenko/model_4 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nanalysenko/model_4") model = AutoModel.from_pretrained("nanalysenko/model_4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b8dc2bc4df20ba2de4c13450010c80afdd1f77892cfc9748db43cc2f6acedd6
- Size of remote file:
- 328 MB
- SHA256:
- c1fab41261923ffd54442096d1b7ea7eb8486fea83fb61cb6c4f842854686105
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