Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use menbom/test-setfit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use menbom/test-setfit-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("menbom/test-setfit-model") 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 menbom/test-setfit-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("menbom/test-setfit-model") model = AutoModel.from_pretrained("menbom/test-setfit-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a32c602b119f56f8f81218aef6f24a3b1f344d8763b6c1796fb232c3537448b
- Size of remote file:
- 6.99 kB
- SHA256:
- 03c1bb8f0141f5e1f4321285a7c4a8405154a98471c4c9e4f108c0f610b1ba4c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.