econecpe-qwen3.5-9b-lora

EconECPE student: Qwen3.5-9B LoRA

The distilled emotion-cause pair extractor from "EconECPE: A Span-Level Emotion-Cause Dataset for Financial Social Media" (Simone Adobati, Federico Lorenzo Bruno, Erik Cambria; IEEE ICDM 2026 Workshops, SENTIRE). This is the labeler of record: it produced the full-corpus labels behind the paper's market study.

Given a Reddit comment and its thread context (post title, parent comment), it returns JSON with every emotion-cause pair the author expresses: a typed emotion (7 classes + neutral), verbatim emotion and cause spans, one of 16 economic cause categories, a target asset, intensity and a sarcasm flag.

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

Results (gold set, 300 comments / 336 pairs)

Nested micro-F1 (%): E-span = emotion span matched (IoU ≥ 0.5); +E also the emotion; +CS also the cause span; +C also the cause category. Neut. = accuracy on the emits-no-pairs decision.

It is statistically indistinguishable from the multi-LLM consensus it was distilled from (61.8 E-span), at about $5 per million comments on one RTX 5090 (≈22 comments/s) against about $1,700 for the consensus.

How to use

The adapter was trained on one prompt contract: the frozen specification as the system prompt, and the comment plus its context as the user turn, with no few-shot examples and greedy decoding. Use the release code, which reproduces that contract and validates the output:

# 1. merge the adapter into the base model (Unsloth; ~18 GB of fp16 weights)
python -m pipeline.merge_adapter --adapter EconECPE/econecpe-qwen3.5-9b-lora --out merged/   # or a local download of this repo
# 2. label comments with vLLM: same prompt as training, JSON validated + one repair turn
#    (reads comments + context from the corpus index built by pipeline.context)
python -m pipeline.label_corpus --model merged/ --limit 2000

⚠ Merge first. vLLM's runtime-LoRA path cannot parse this adapter's regex-string target_modules and silently serves the un-adapted base model.

Training details: LoRA rank 16, α 16, no dropout, on all attention and MLP projections of the text model; bf16 (not QLoRA); AdamW-8bit, cosine schedule, peak LR 1e-4, effective batch 16; checkpoint 1300 of the recall run, whose targets are every silver pair with support ≥ 1. Split: 18,187 train / 1,500 dev silver comments; the 300 gold comments were never trained on.

Limitations

• The training labels are model-generated (multi-LLM consensus over DeepSeek-V4-Flash, MiMo-V2.5 and MiniMax-M3, with a DeepSeek-V4-Pro judge). It inherits their shared errors and their framing of politically charged topics.
• Cause localization is the weak point: requiring the cause span to match costs ~22 F1 points (+E → +CS).
• Domain: r/economics, 2018–2026, English. The emotion marginal is dominated by anger/disgust (68.9% of pairs), so the minority emotions rest on few training examples.
• The sarcasm flag is auxiliary metadata, not an evaluated field.
• Not investment advice, and not suitable for judging individual users.

License

The adapter follows its base model's license (Apache 2.0). 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}
}