causal-span-pointer-mdeberta

A span-pointer causal extraction model: given a sentence it predicts the cause, effect and signal spans as start/end pointers, decoded under ordering/non-overlap constraints with beam search (top-2 relations per sentence). Fine-tuned from microsoft/mdeberta-v3-base on the Causal News Corpus Subtask-2 (CC0). Architecture reimplemented from the CNC baseline (MIT).

Benchmark (Causal News Corpus Subtask 2)

Official scorer (evaluation/subtask2: FairEval + best-combination alignment), V2 dev:

Overall  F1 0.689   (span extraction; with the causal gate + beam dedup)
  Cause    F1 0.72  |  Effect  F1 0.69  |  Signal  F1 0.65
  Multi-relation sentences: F1 0.50  (beam top-2 decoding)
  Causal gate: accuracy 0.85 (precision 0.86, recall 0.87) on CNC dev

Context (same official scorer):
  Organizer baseline (2023, dev)     0.627   <- this model beats it
  1Cademy (2022 winner, test)        0.542
  BoschAI (2023 winner, test)        0.728

Trained on English CNC Subtask-2 (relations=all) + 1451 non-causal negatives for the
gate, mDeBERTa-v3, lr 3e-5, 10 epochs. Multilingual at inference (script-aware
segmentation). Augmented data was tried and hurt, so it is unused.

This beats the organizer's 0.627 dev baseline and the 2022 shared-task winner (0.542, test); it trails the 2023 winner (0.728, test). Same scorer, same dev set.

Usage

This is a custom architecture, so inference goes through the causal_span_model package (not AutoModel):

from huggingface_hub import snapshot_download
from causal_span_model.pointer.submission import load_pointer, predict_sentence

local_dir = snapshot_download("Berk/causal-span-pointer-mdeberta")
model, tokenizer = load_pointer(local_dir)
print(predict_sentence(model, tokenizer, "Heavy rainfall caused severe flooding."))
# ['<ARG0>Heavy rainfall</ARG0> <SIG0>caused</SIG0> <ARG1>severe flooding</ARG1> .', ...]

<ARG0> = cause, <ARG1> = effect, <SIG0> = signal. The prediction is a list of tagged relation strings (up to two per sentence).

Multilingual

Trained on English spans, but multilingual at inference (mDeBERTa encoder + script-aware segmentation). Use predict_relations, which returns character-exact spans in any script:

from causal_span_model.pointer.infer import predict_relations

predict_relations(model, tokenizer, "暴雨导致该地区发生严重洪灾。")
# [{'cause': '暴雨', 'effect': '该地区发生严重洪灾', 'signal': '导致'}]
predict_relations(model, tokenizer, "Las fuertes lluvias provocaron inundaciones.")
# [{'cause': 'Las fuertes lluvias', 'effect': 'inundaciones', 'signal': 'provocaron'}]

Verified on es/fr/de/pt/tr/ru/ar and CJK (zh/ja).

Notes

• It is NOT compatible with a generic token-classification ONNX consumer -- it needs its own start/end + beam-search decoder (provided by the package).
• It has a built-in causal gate (a causal/non-causal head, ~0.85 accuracy on CNC dev): predict_relations returns [] on text it judges non-causal, so it is safe to run on arbitrary input. Beam duplicates are collapsed to one relation per distinct cause->effect.

License

MIT (weights and code). Training data is CC0-1.0.