Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use rithwik-db/cleaned-e5-base-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-base-unsupervised with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rithwik-db/cleaned-e5-base-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-base-unsupervised with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rithwik-db/cleaned-e5-base-unsupervised") model = AutoModel.from_pretrained("rithwik-db/cleaned-e5-base-unsupervised", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a8af2c2e4073353df833a1083e13fb5cb71bffc157250310eeda4028e1d9069
- Size of remote file:
- 438 MB
- SHA256:
- f10c0c1606a4a1c8b6221cc4fb3bf7cc8a8b52150a208838b21c6d50ddc6c4f9
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