Sentence Similarity
sentence-transformers
ONNX
Safetensors
OpenVINO
modernbert
feature-extraction
Generated from Trainer
dataset_size:36864
loss:MatryoshkaLoss
loss:CachedMultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use redis/langcache-embed-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/langcache-embed-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/langcache-embed-v2") sentences = [ "What are civil cases and what are some examples?", "What are criminal cases and what are no examples?", "Civil cases involve disputes between individuals or organizations, typically seeking monetary compensation or specific performance, and *do not* include criminal prosecutions by the government.", "Criminal cases involve disputes between individuals or organizations, seeking monetary damages or specific performance, while civil cases concern offenses against the state punishable by imprisonment.", "What are some examples of civil cases?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add new SentenceTransformer model with an onnx backend
Verified signatureHello! *This pull request has been automatically generated from the [`push_to_hub`](https://sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html#sentence_transformers.SentenceTransformer.push_to_hub) method from the Sentence Transformers library.* ## Full Model Architecture: ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ORTModelForFeatureExtraction (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Tip: Consider testing this pull request before merging by loading the model from this PR with the `revision` argument: ```python from sentence_transformers import SentenceTransformer # TODO: Fill in the PR number pr_number = 2 model = Sentenc…a883118