econecpe-deberta-v3-tagger

EconECPE tagger: DeBERTaV3-large

The encoder efficiency variant from "EconECPE: A Span-Level Emotion-Cause Dataset for Financial Social Media" (Simone Adobati, Federico Lorenzo Bruno, Erik Cambria; IEEE ICDM 2026 Workshops, SENTIRE). It tags typed emotion spans and typed cause spans in one forward pass (≈81 comments/s, 3.7× the generative student), but it is not the paper's labeler of record. For that, see EconECPE/econecpe-qwen3.5-9b-lora.

Data, code and specification: doi.org/10.5281/zenodo.22913761 · code on GitHub: EconECPE/EconECPE

Architecture

DeBERTaV3-large over the concatenated context and comment (512 tokens), with three heads on the shared token representations:

• a typed emotion-span head (BIO, 15 labels = 7 emotions × B/I + O),
• a typed cause-span head (BIO, 35 labels = 17 cause categories × B/I + O),
• an intensity-regression head on emotion-span tokens.

Pairs are formed by a proximity heuristic: nearest cause span, backing off to a self-span. Decoding drops spans shorter than 2 tokens and spans whose mean confidence is below 0.5 (emotion and cause). The label lists and decode settings are in econecpe_tagger_config.json.

Results (gold set, 300 comments / 336 pairs)

Nested micro-F1 (%), as in the paper's Table IV:

The weak cause-span score is structural: 29.7% of its cause spans are copies of its own emotion span (61.7% under transfer), the cost of proximity pairing standing in for multi-pair decoding.

How to use

model.safetensors is the state dict of MultiHeadTagger from the release's pipeline/deberta_train.py:

from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from pipeline.deberta_train import build_model   # from the EconECPE release

model = build_model("microsoft/deberta-v3-large", from_config=True)
model.load_state_dict(load_file(hf_hub_download(
    "EconECPE/econecpe-deberta-v3-tagger", "model.safetensors")))

or score it on the gold set with the release's evaluator, which accepts the .safetensors file directly: python -m pipeline.deberta_eval --checkpoint model.safetensors ...

Limitations

• Trained on model-generated labels (the same multi-LLM silver consensus as the generative student), so it inherits their shared errors.
• Degrades sharply out of domain unless re-tuned on target-domain labels.
• Domain: r/economics, 2018–2026, English. Not investment advice, and not suitable for judging individual users.

License

The weights follow the base model's license (MIT). The annotations it was trained on are released under CC BY-NC 4.0.

Citation

@inproceedings{adobati2026econecpe,
  title     = {{EconECPE}: A Span-Level Emotion-Cause Dataset for Financial Social Media},
  author    = {Adobati, Simone and Bruno, Federico Lorenzo and Cambria, Erik},
  booktitle = {2026 IEEE International Conference on Data Mining Workshops (ICDMW)},
  note      = {SENTIRE 2026 workshop},
  year      = {2026}
}