Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use czesty/ea-setfit-v1-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use czesty/ea-setfit-v1-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("czesty/ea-setfit-v1-classifier") 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 czesty/ea-setfit-v1-classifier with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("czesty/ea-setfit-v1-classifier") model = AutoModel.from_pretrained("czesty/ea-setfit-v1-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fef1e80fd5206cedbd97c457e2f2e893b80771537fb4cf5b0f8d0087fd6b7734
- Size of remote file:
- 6.99 kB
- SHA256:
- 5ec835f75f84afceca7b1f63510fe46ab64dceef0b2a437bf31b3fc157fb5f90
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