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Sponsored by
iMean AI Trip Planner
ethicalabs / Echo-DSRN-v0.1.3-Embed-Intent
Model's Last Updated: August 23 2026
sentence-similarity
Introduction of Echo-DSRN-v0.1.3-Embed-Intent
Model Details of Echo-DSRN-v0.1.3-Embed-Intent
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:
MultipleNegativesRankingLosswith 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}
}
Runs of ethicalabs Echo-DSRN-v0.1.3-Embed-Intent on huggingface.co
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More Information About Echo-DSRN-v0.1.3-Embed-Intent huggingface.co Model
Url of Echo-DSRN-v0.1.3-Embed-Intent
Echo-DSRN-v0.1.3-Embed-Intent huggingface.co Url
Provider of Echo-DSRN-v0.1.3-Embed-Intent huggingface.co
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Updated:August 23 2026
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