Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use clp/setfit-ethos-multilabel-example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use clp/setfit-ethos-multilabel-example with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("clp/setfit-ethos-multilabel-example") 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 clp/setfit-ethos-multilabel-example with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("clp/setfit-ethos-multilabel-example") model = AutoModel.from_pretrained("clp/setfit-ethos-multilabel-example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 089c7c180d370c7e7d3a478fca2177244c77467bad0a32ac99b86b0a15e44357
- Size of remote file:
- 438 MB
- SHA256:
- 19c44bd392ea79a8ffb77767950c4134ae9beb45e168af02e66a999ad7262080
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