Inference

Model: ethicalabs/Echo-DSRN-v0.1.4-Research-Intent-CLF — public repo, 6-class (Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable).

Install

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

Classify

from echo_dsrn import EchoForSequenceClassification
from transformers import AutoTokenizer

model     = EchoForSequenceClassification.from_pretrained(
    "ethicalabs/Echo-DSRN-v0.1.4-Research-Intent-CLF", trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "ethicalabs/Echo-DSRN-v0.1.4-Research-Intent-CLF", trust_remote_code=True
)

label, probs = model.classify(
    "Title: Attention Is All You Need\n"
    "Abstract: We propose a new simple network architecture, the Transformer...",
    tokenizer=tokenizer,
)
print(label, probs)
# → Methodology  tensor([0.87, 0.03, 0.02, 0.06, 0.02, 0.00])

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
Classes	6 (incl. Unclassifiable)
Inference	Fast on CPU

Run curl /api/model/card for live specs from the loaded model.