Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use rithwik-db/cleaned-e5-large-unsupervised with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rithwik-db/cleaned-e5-large-unsupervised with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rithwik-db/cleaned-e5-large-unsupervised") 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-large-unsupervised with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rithwik-db/cleaned-e5-large-unsupervised") model = AutoModel.from_pretrained("rithwik-db/cleaned-e5-large-unsupervised", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e832892c6900564f9a738c9e079a9e1bdd9eb1417773529a2291d4cc2cbf5fc
- Size of remote file:
- 1.34 GB
- SHA256:
- ccb637956d7f2d60e17a5ae1b4d6a1bc7464d04dfc0184fe433d4150fe418fe4
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