Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:21470
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/unified-negatives with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/unified-negatives with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/unified-negatives") sentences = [ "This positive resistance model is a different way of analyzing feedback oscillator operation.", "This positive resistance model is a different way of analyzing feedback oscillator operation.", "This negative resistance model is an alternate way of analyzing feedback oscillator operation.", "I am BE 8th sem. CSE student. Which path should I choose as a career or which course I should do to get a good job in future within my country?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5941547ce40e090b7aefbdaffda51eed11676ba2adda9a8c1613a64f97e2f33a
- Size of remote file:
- 6.16 kB
- SHA256:
- 0fc2286c99f44cbe5eb914eebd6c378a90fddc36567ca2bdb5e7afe2813cac8c
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