Sentence Similarity
sentence-transformers
Safetensors
English
bert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
loss:ArcFaceInBatchLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/langcache-embed-v3-mini-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/langcache-embed-v3-mini-experimental with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/langcache-embed-v3-mini-experimental") 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] - Notebooks
- Google Colab
- Kaggle
| { | |
| "test_cosine_accuracy@1": 0.5474394601032155, | |
| "test_cosine_precision@1": 0.5474394601032155, | |
| "test_cosine_recall@1": 0.5284894589479743, | |
| "test_cosine_ndcg@10": 0.7464605932142311, | |
| "test_cosine_mrr@1": 0.5474394601032155, | |
| "test_cosine_map@100": 0.6905991135837425, | |
| "test_cosine_auc_precision_cache_hit_ratio": 0.3154094226830552, | |
| "test_cosine_auc_similarity_distribution": 0.16081146552358963 | |
| } |