Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Bruno/my-awesome-setfit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Bruno/my-awesome-setfit-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Bruno/my-awesome-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 Bruno/my-awesome-setfit-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Bruno/my-awesome-setfit-model") model = AutoModel.from_pretrained("Bruno/my-awesome-setfit-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0736cc54fc95893e29830bb81936d3f1c21faeee0018e46144f44fc66a5e301
- Size of remote file:
- 438 MB
- SHA256:
- e22ef19ad19462ef13caffa6938cdbe5f2372064f797283ac0af659d8ede9c07
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