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

This repository contains experimental models designed strictly for academic evaluation and research purposes.

Critical Constraints:

No Production Deployment: Experimental models must not be deployed in commercial, enterprise, or mission-critical environments under any circumstances.

No Liability: Experimental models are provided "as-is" without warranties of any kind. The developers assume zero liability for downstream consequences, system integration failures, or regulatory non-compliance resulting from unauthorized deployment.

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

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}")

The model is gated — request access before downloading.

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: