Sentence Similarity
sentence-transformers
Safetensors
English
bert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:8000000
loss:ArcFaceInBatchLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/langcache-embed-v3-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/langcache-embed-v3-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/langcache-embed-v3-small") sentences = [ "\"How much would I need to narrate a \"\"Let's Play\"\" video in order to make money from it on YouTube?\"", "How much money do people make from YouTube videos with 1 million views?", "\"How much would I need to narrate a \"\"Let's Play\"\" video in order to make money from it on YouTube?\"", "\"Does the sentence, \"\"I expect to be disappointed,\"\" make sense?\"" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download final_metrics.json from redis/langcache-embed-v3-small: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/redis/langcache-embed-v3-small/resolve/main/final_metrics.json
- Command line
-
hf download hf://redis/langcache-embed-v3-small/final_metrics.json
-
curl -L -o final_metrics.json https://huggingface.co/redis/langcache-embed-v3-small/resolve/main/final_metrics.json
296 Bytes
| { | |
| "train_cosine_accuracy@1": 0.5949339683914268, | |
| "train_cosine_precision@1": 0.5949339683914268, | |
| "train_cosine_recall@1": 0.5736236134628644, | |
| "train_cosine_ndcg@10": 0.7884594230085583, | |
| "train_cosine_mrr@1": 0.5949339683914268, | |
| "train_cosine_map@100": 0.7351414640689793 | |
| } |