Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use rithwik-db/cleaned-e5-base-unsupervised-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rithwik-db/cleaned-e5-base-unsupervised-16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rithwik-db/cleaned-e5-base-unsupervised-16") 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/cleaned-e5-base-unsupervised-16 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rithwik-db/cleaned-e5-base-unsupervised-16") model = AutoModel.from_pretrained("rithwik-db/cleaned-e5-base-unsupervised-16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aaab3b7a69e44ae38bc25a5a8b189d935200f539cfe395fa9b156bd0f952f624
- Size of remote file:
- 438 MB
- SHA256:
- b7c4a49012f4eaba7c26c3f1f8406c4a1e74a7738c3b459344aaced61390db80
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