This model is a fine-tuned version of roberta-base for causal span extraction (token classification). It identifies cause and effect text spans in sentences.

Model Description

Base Model: roberta-base
Task: Token classification (BIO tagging)
Labels: O, B-CAUSE, I-CAUSE, B-EFFECT, I-EFFECT
Training Data: CausalNewsCorpus V2 (sentences with exactly 1 causal pair)
Training Samples: 1105
Dev Samples: 133

Training Results

See the training notebook for detailed metrics.

Usage

python

from transformers import RobertaTokenizerFast, RobertaForTokenClassification
import torch

model_name = "causal-narrative/roberta-causal-span-extractor"
tokenizer = RobertaTokenizerFast.from_pretrained(model_name, add_prefix_space=True)
model = RobertaForTokenClassification.from_pretrained(model_name)

text = "The heavy rain caused flooding in the city."
words = text.split()
inputs = tokenizer(words, is_split_into_words=True, return_tensors="pt",
                   truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    preds = torch.argmax(outputs.logits, dim=2)[0]

id2label = model.config.id2label
word_ids = tokenizer(words, is_split_into_words=True).word_ids()
prev = None
for wid in word_ids:
    if wid is not None and wid != prev:
        print(f"{words[wid]:20s} {id2label[preds[word_ids.index(wid)].item()]}")
        prev = wid

Labels

Label	Description
O	Non-causal token
B-CAUSE	Beginning of cause span
I-CAUSE	Inside cause span
B-EFFECT	Beginning of effect span
I-EFFECT	Inside effect span