Sentence Similarity
sentence-transformers
PyTorch
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use Elise-hf/distilbert-base-pwc-task-multi-label-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Elise-hf/distilbert-base-pwc-task-multi-label-classification with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Elise-hf/distilbert-base-pwc-task-multi-label-classification") 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 Elise-hf/distilbert-base-pwc-task-multi-label-classification with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Elise-hf/distilbert-base-pwc-task-multi-label-classification") model = AutoModel.from_pretrained("Elise-hf/distilbert-base-pwc-task-multi-label-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5de1c1415e02b9e62c2f022032f05670650379c971a77dc9eb616716723fcf81
- Size of remote file:
- 265 MB
- SHA256:
- 60857d4caa48466a38f198ebdf723a5f0539930661f6862d6ae5a4d0b2a25798
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