Echo-DSRN-v0.1.4-Research-Intent-CLF

Model Card for ethicalabs/Echo-DSRN-v0.1.4-Research-Intent-CLF

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6-class research paper intent classifier built on the Echo-DSRN architecture (98.3M params), fine-tuned from ethicalabs/Echo-DSRN-v0.1.3-Research-Intent-CLF (5-class).

Labels: Methodology · Dataset · Review · Applied · Theoretical · Unclassifiable

The Unclassifiable class is new in v0.1.4 — papers flagged by >1 LLM judge as garbled / non-English / non-academic.

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.

Training

• Data: ethicalabs/Research-Intent-Collab (10,001 papers, LLM-judge majority consensus + human curation). Official train (7,999) / validation (1,001) / test (1,001) splits.
• Recipe: head swap 5→6, differential AdamW (backbone 2e-5, head 2e-4), batch 16, 3 epochs (1,500 steps), bf16, cosine LR + 5% warmup, early stopping on the official validation split.

Results (official test split, 1,001 rows)

Accuracy: 0.7353 (736/1001).

Comparison with v0.1.3 (same official test split)

v0.1.3 (5-class) evaluates on 899 rows only — its head cannot produce Unclassifiable, so all 102 such test rows would be misclassified.

v0.1.4 covers all 1,001 rows and catches 60 of the 102 with 91% precision.

Per-class F1 (test split):

v0.1.4 gains accuracy on the original classes (+3.2 pts on the 5-class subset) and adds the Unclassifiable safety class.

Review remains the weakest class for both models (mostly confused with Applied).

Usage

from echo_dsrn import pipeline

# Requires echo_dsrn >= 0.1.6 (chat-template support for
# text-classification pipelines). Tiny model — CPU load + inference
# is faster than GPU for this size.
pipe = pipeline(
    "text-classification",
    model="ethicalabs/Echo-DSRN-v0.1.4-Research-Intent-CLF",
    trust_remote_code=True,
    device="cpu",
)

# Raw title+abstract string — the pipeline applies the model's chat
# template (system prompt + user phrasing) automatically.
pipe(
    "Title: Attention Is All You Need\n"
    "Abstract: The dominant sequence transduction models are based on complex "
    "recurrent or convolutional neural networks that include an encoder and a "
    "decoder. The best performing models also connect the encoder and decoder "
    "through an attention mechanism. We propose a new simple network "
    "architecture, the Transformer, based solely on attention mechanisms, "
    "dispensing with recurrence and convolutions entirely. Experiments on two "
    "machine translation tasks show these models to be superior in quality "
    "while being more parallelizable and requiring significantly less time to "
    "train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German "
    "translation task, improving over the existing best results, including "
    "ensembles, by over 2 BLEU."
)
# [{'label': 'Methodology', 'score': 0.9518}]

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