Post
1587
Hi Hugging Face community! 👋 we just submitted our project, OpenAIRE Graph Evaluator, to the OpenAIRE AI Hackathon, and community voting is open until September 11th!
We developed a new classifier model and an LLM-as-judge pipeline to automatically classify millions of unlabelled research papers within the OpenAIRE Graph.
The goal is to save researchers hundreds of hours spent manually sorting datasets, methodologies, and reviews, while creating a self-improving pipeline to generate datasets for training future classifiers!
We would love your support: please vote for "02-OpenAIRE Graph Evaluator" (Theme B) here: https://innovation.openaire.eu/component/content/article/hackathon-community-vote.html?catid=8
We built a custom model, Echo-DSRN, a hybrid recurrent architecture designed for resource-constrained tasks. It features an internal surprise gate and decouples computation into a fast GRU, sliding-window attention, and a gated slow state.
It comes in 4 variants:
- Hybrid RNN: Lightweight generation and speculative drafting.
- Transformers Hybrid: DSRN memory blocks injected into backbones like Qwen 2.5.
- Embedding: Dense sentence embeddings compatible with sentence-transformers.
- Classifier: The specialized engine powering our hackathon submission!
Thanks for your support! Let us know if you have any questions about the architecture!
---
Collab Hub: https://openaire-2026.ethicalabs.ai/collab
GitHub: https://github.com/ethicalabs-ai/Echo-DSRN
HF Hackathon Collection: https://huggingface.co/collections/ethicalabs/openaire-ai-hackathon-2026
We developed a new classifier model and an LLM-as-judge pipeline to automatically classify millions of unlabelled research papers within the OpenAIRE Graph.
The goal is to save researchers hundreds of hours spent manually sorting datasets, methodologies, and reviews, while creating a self-improving pipeline to generate datasets for training future classifiers!
We would love your support: please vote for "02-OpenAIRE Graph Evaluator" (Theme B) here: https://innovation.openaire.eu/component/content/article/hackathon-community-vote.html?catid=8
We built a custom model, Echo-DSRN, a hybrid recurrent architecture designed for resource-constrained tasks. It features an internal surprise gate and decouples computation into a fast GRU, sliding-window attention, and a gated slow state.
It comes in 4 variants:
- Hybrid RNN: Lightweight generation and speculative drafting.
- Transformers Hybrid: DSRN memory blocks injected into backbones like Qwen 2.5.
- Embedding: Dense sentence embeddings compatible with sentence-transformers.
- Classifier: The specialized engine powering our hackathon submission!
Thanks for your support! Let us know if you have any questions about the architecture!
---
Collab Hub: https://openaire-2026.ethicalabs.ai/collab
GitHub: https://github.com/ethicalabs-ai/Echo-DSRN
HF Hackathon Collection: https://huggingface.co/collections/ethicalabs/openaire-ai-hackathon-2026