Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use rithwik-db/embedded-e5-base-50 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-50 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rithwik-db/embedded-e5-base-50") 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-50 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rithwik-db/embedded-e5-base-50") model = AutoModel.from_pretrained("rithwik-db/embedded-e5-base-50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0b3064a5f876d69b18b372ae9469b448ae6856526d419cbf04306c643842f8c
- Size of remote file:
- 438 MB
- SHA256:
- a25a5e12cbd2aa33d9bf4a7ae6ac55e6da5bf9d5e087f3896ed08ec8507c832c
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