Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
echo
feature-extraction
echo-dsrn
linear-complexity
recurrent-hybrid
custom_code
Instructions to use ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp", trust_remote_code=True) 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 ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Benchmarks, Resources Footprints and Tasks
#1
by mrs83 - opened
Benchmarks
This experimental variant (Echo-DSRN-v0.1.3-Embed-Exp) reaches an average 0.753 Spearman correlation across 7 STS tasks, competitive with dedicated bidirectional encoders (like MiniLM variants) despite its causal recurrent heritage.
Resources Footprint
Because of the O(1) memory core and bounded 128-token local attention, it runs at ~220 sentences/sec on pure CPU with zero GPU dependency and near-instant load times.
Tasks & Active Testing
- Multilingual RAG (pgvector): Intensive validation in PostgreSQL over English, Italian, and German corpora, leveraging the dense 2048-dim slow-state vectors (mean_c_all).
- Semantic Search: Asymmetric retrieval and cross-lingual passage matching