Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use Bruno/intent-classification-pt-setfit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Bruno/intent-classification-pt-setfit-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Bruno/intent-classification-pt-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/intent-classification-pt-setfit-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Bruno/intent-classification-pt-setfit-model") model = AutoModel.from_pretrained("Bruno/intent-classification-pt-setfit-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 386a68f7ec2b27f60f092ae74deb048ca18a1ceab077eeaa2ded49646c9e1283
- Size of remote file:
- 471 MB
- SHA256:
- 5846e8670fe8f067477123992e0b3e36a273b486bb847f26d18a484aec06ff33
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