Sentence Similarity
sentence-transformers
PyTorch
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use hlyu/distilbert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hlyu/distilbert-base-uncased with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hlyu/distilbert-base-uncased") 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 hlyu/distilbert-base-uncased with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hlyu/distilbert-base-uncased") model = AutoModel.from_pretrained("hlyu/distilbert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a860190f8bc3e7f18943e6a5de96e4de6684547c0b7dae036b96e52583ebc0c
- Size of remote file:
- 265 MB
- SHA256:
- 9618887e09380b10a0667bd1b5a7b2948aacd8d85f90e132164d79aabd4c0abd
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