Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use rithwik-db/e5-base_arguana_10000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rithwik-db/e5-base_arguana_10000 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rithwik-db/e5-base_arguana_10000") 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/e5-base_arguana_10000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rithwik-db/e5-base_arguana_10000") model = AutoModel.from_pretrained("rithwik-db/e5-base_arguana_10000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13fe5f7207939ec7a61e417b7c98697bc3e976dd23d7006185e8ae1034a6727f
- Size of remote file:
- 438 MB
- SHA256:
- 651a48435752be55c164d13ae30c5cc74f82c3f9b773d75bdc4178cdecbd4e05
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.