Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use Consensus/e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Consensus/e5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Consensus/e5-base") 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 Consensus/e5-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Consensus/e5-base") model = AutoModel.from_pretrained("Consensus/e5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c093f1c1ea97ffa1ad48de650992366e4c1c04c39158bfa80fe6509c89b6b73b
- Size of remote file:
- 438 MB
- SHA256:
- 85aa75c49fb1652062186472b0d0c920823bc84c4b6973d37b251256040d5de4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.