Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use rithwik-db/embedded-e5-base-500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rithwik-db/embedded-e5-base-500 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rithwik-db/embedded-e5-base-500") 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 rithwik-db/embedded-e5-base-500 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rithwik-db/embedded-e5-base-500") model = AutoModel.from_pretrained("rithwik-db/embedded-e5-base-500", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ac35ccd7fd2b63cb3bc56ba8271b4ac5748bb718b729ec826273e506b0ef755
- Size of remote file:
- 438 MB
- SHA256:
- 2953c7653772e99cd745a91f66a619ea3944ecd233dea171deb5fb7058ce4cf2
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