Model Card for ethicalabs/Echo-DSRN-v0.1.3-Research-Intent-CLF

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This is a ~98 million parameters sequence classification model based on the Echo-DSRN architecture, trained on a single AMD GPU using ROCm 7.2.

OpenAIRE AI Hackathon 2026 🇪🇺

This model is part of ethicalabs.ai's entry in the OpenAIRE AI Hackathon 2026, co-organised by OpenAIRE and Alien Intelligence — a 12-week open science build challenge.

🔴 Live Demo → openaire-2026.ethicalabs.ai

The live application classifies research papers in real time using this model, with publication metadata streamed from the OpenAIRE Graph API under CC BY 4.0.

What this model does

Echo-DSRN-v0.1.3-Research-Intent-CLF is a research paper intent classifier — it reads a paper's title and abstract and predicts one of five categories:

Label	Meaning
Methodology	Introduces a new method, model, or algorithm
Dataset	Introduces or documents a dataset or benchmark
Review	Surveys or synthesises existing work
Applied	Applies existing methods to a domain problem
Theoretical	Mathematical or formal analysis without empirical evaluation

Inference runs in on CPU, making it suitable for streaming applications where a GPU is unavailable.

How to use

pip install git+https://github.com/ethicalabs-ai/Echo-DSRN.git

import torch
from echo_dsrn import EchoForSequenceClassification
from transformers import AutoTokenizer

# 1. Load sequence classification model and tokenizer
model_path = "ethicalabs/Echo-DSRN-v0.1.3-Research-Intent-CLF"
model = EchoForSequenceClassification.from_pretrained(
    model_path,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(
    model_path,
    trust_remote_code=True
)

# 2. Run inference
text = "Title: ... \nAbstract: ..."
label, probs = model.classify(text, tokenizer=tokenizer)
print(f"Intent: {label}")

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	bfloat16
Base model	Echo-DSRN-114M-v0.1.2
GPU	AMD Radeon AI Pro R9700 (ROCm 7.2)

Building a better dataset — we need you

This model is actively powering the live classifier at openaire-2026.ethicalabs.ai.

Every paper classified on the platform becomes part of a growing annotation dataset stored in the system.

We need human contributors, not just LLM judges. If you're a researcher, librarian, or domain expert, you can:

Visit the Collab Hub and vote on papers
Flag misclassified papers
Save papers to your private history
Log in with your HuggingFace account

The multi-model LLM-as-Judge pipeline (11 LLMs across Qwen, Gemma, GPT-OSS, and other families) validates Echo-DSRN predictions.

But LLM consensus is no substitute for human expertise — your domain knowledge helps us catch edge cases the models miss.

After the hackathon

The current model will be fine-tuned on the collected annotation dataset and re-released as v0.1.4 after the hackathon concludes (submission deadline: 20 August 2026).

The fine-tuned model will benefit from:

Thousands of LLM + Human annotations from the OpenAIRE Graph stream.
Multi-model consensus labels from LLM judges.
Community feedback from the Collab Hub.

Data provenance

Training data for the current version includes curated records from PubMed, Semantic Scholar, Papers With Code, and arXiv.

The live application streams additional publication metadata from the OpenAIRE Graph API, licensed under CC BY 4.0.

License

Apache 2.0 — see LICENSE.

OpenAIRE Graph API data: CC-BY 4.0.

Citation

If you use this model or the annotation dataset, please cite:

@software{echo_dsrn_research_intent_clf,
  author = {Massimo Roberto Scamarcia},
  title = {OpenAIRE Graph Evaluator: Research paper classifier and multi-model LLM-as-Judge pipeline},
  year = {2026},
  url = {https://huggingface.co/collections/ethicalabs/openaire-ai-hackathon-2026}
}

Made in 🇪🇺 with 💗 for Open Science 🤗 - github.com/ethicalabs-ai/OpenAIRE-AI-Research-Evaluator

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