Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use rithwik-db/embedded-e5-large-500-correct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use rithwik-db/embedded-e5-large-500-correct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rithwik-db/embedded-e5-large-500-correct") 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/embedded-e5-large-500-correct with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("rithwik-db/embedded-e5-large-500-correct") model = AutoModel.from_pretrained("rithwik-db/embedded-e5-large-500-correct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fc8dde5af0d071306d087bd9595dbe72f3f5f6953df6fca4b46dabae6922c75d
- Size of remote file:
- 1.34 GB
- SHA256:
- 75c0122184bf5a852c100fa67097aadb916eba53d03a5615cbb9f8a17ae55a22
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