Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use rithwik-db/cleaned-e5-base-unsupervised-test 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-test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rithwik-db/cleaned-e5-base-unsupervised-test") 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-test with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rithwik-db/cleaned-e5-base-unsupervised-test") model = AutoModel.from_pretrained("rithwik-db/cleaned-e5-base-unsupervised-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70996dd40687450723c0ffbe86a81dab5b3cbb14b954d1fc1fa487d258d73bcd
- Size of remote file:
- 438 MB
- SHA256:
- f4cf96629ad8349f8ca4f41b11aa95e052ed1dc8f6047827cae0b87e6a27035c
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