Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use deepfile/embedder-distilroberta-base-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use deepfile/embedder-distilroberta-base-512 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("deepfile/embedder-distilroberta-base-512") 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 deepfile/embedder-distilroberta-base-512 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("deepfile/embedder-distilroberta-base-512") model = AutoModel.from_pretrained("deepfile/embedder-distilroberta-base-512") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4258c26a29fd5a4218dc6695ee7a86ae1444ea6a2e03ade9bc511dc8f6386db4
- Size of remote file:
- 329 MB
- SHA256:
- 4ae34d3d621d1c362def3cee1e206e6923b843a1ede41d0fe91e4a00cb0314d0
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