SemEval 2026 Task 10 — Marker Extraction (Effect)
RoBERTa-large with LoRA for Effect span extraction (SemEval 2026 Task 10, marker extraction subtask).
Marker: Effect
Spans denoting consequences or results (e.g. collapse, death, economic harm) attributed in the text.
Model description
- Task: Token-level binary classification for Effect spans.
- Architecture:
roberta-large+ PEFT LoRA (RobertaForTokenClassification). - This repo: Single-marker model for Effect only.
Intended use
Research and evaluation on SemEval 2026 marker extraction.
How to load
from transformers import RobertaForTokenClassification, RobertaTokenizerFast
from peft import PeftModel
repo_id = "MMHusnain/semeval26-psysemev-extraction-Effect-2026-05-17-1834"
base_model = "roberta-large"
tokenizer = RobertaTokenizerFast.from_pretrained(repo_id, add_prefix_space=True)
model = RobertaForTokenClassification.from_pretrained(base_model, num_labels=2)
model = PeftModel.from_pretrained(model, repo_id)
Training
Trained under Markers-Extraction/ with LoRA.
Limitations
- English Reddit submission statements only.
- One marker type per model.
Citation
SemEval-2026 Task 10: Psycholinguistic Markers of Conspiracy Theories in Social Media Conversations.
Model metadata
| Field | Value |
|---|---|
| Hub repo | MMHusnain/semeval26-psysemev-extraction-Effect-2026-05-17-1834 |
| Marker type | Effect |
| Upload batch | 2026-05-17-1834 |
| Base model | roberta-large |
- Downloads last month
- 4
Model tree for MMHusnain/semeval26-psysemev-extraction-Effect-2026-05-17-1834
Base model
FacebookAI/roberta-large