Echo-DSRN-v0.1.3-Embed-Intent

98M-parameter multilingual intent classification embedding model based on the Echo-DSRN architecture (Dual-State Recurrent Neural Network) ◦ Recurrent Hybrid.

Fine-tuned from ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp on Amazon MASSIVE across all 51 languages using MultipleNegativesRankingLoss (MNRL).

Model specs

Property	Value
Architecture	Echo-DSRN (Recurrent Neural Network)
Parameters	98,266,629 (~98M)
Layers	8 DSRN blocks
Hidden dim	512
Attention heads	4
Vocab size	32,017 tokens
Precision	fp32
Base model	ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp
GPU	AMD Radeon AI Pro R9700 (ROCm 7.2)

MTEB Results

MassiveIntentClassification (60 intents, 51 languages)

Metric	Score
Accuracy (mean)	72.42%
Accuracy (min)	62.98%
Accuracy (max)	78.33%
F1 (mean)	66.27%
F1 (min)	56.52%
F1 (max)	71.94%

MassiveScenarioClassification (17 scenarios, 51 languages)

Metric	Score
Accuracy (mean)	79.00%
Accuracy (min)	71.62%
Accuracy (max)	84.30%
F1 (mean)	78.28%
F1 (min)	70.05%
F1 (max)	84.08%

Evaluated via MTEB v2.12.30 logistic regression protocol on frozen embeddings. Per-language scores available in the model-index metadata.

Training

• Base model: ethicalabs/Echo-DSRN-v0.1.3-Embed-Exp (STS-pretrained, 0.753 avg Spearman on MTEB STS)
• Dataset: Amazon MASSIVE, all 51 locales (~1M training utterances)
• Loss: MultipleNegativesRankingLoss with intent-grouped positive pairs
• Pooling: mean_c_all (2048-dim recurrent slow state)
• Convergence: Early stopping at epoch 1.2; linear accuracy gain (+2 pts/1k steps), no grokking plateau
• Random baseline: ~1.7% (60-class 1-NN)

Usage

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("ethicalabs/Echo-DSRN-v0.1.3-Embed-Intent", trust_remote_code=True)
embeddings = model.encode(["What's the weather today?", "Will it rain?"])

A reproducible test script is available at scripts/test_intent_clf.py.

Citation

@software{echo_dsrn_embed_intent,
  author = {Massimo Roberto Scamarcia},
  title = {Echo-DSRN-v0.1.3-Embed-Intent: Multilingual Intent Classification Embeddings},
  year = {2026},
  url = {https://huggingface.co/Echo-DSRN-v0.1.3-Embed-Intent}
}