metadata
datasets:
- CausalNewsCorpus
language: en
library_name: transformers
license: mit
metrics:
- accuracy
- f1
- precision
- recall
tags:
- text-classification
- roberta
- causal-narrative
- sequence-classification
RoBERTa Causal Narrative Classifier
This model is a fine-tuned version of roberta-base for causal narrative sentence classification.
Model Description
- Base Model: roberta-base
- Task: Binary classification (causal vs non-causal sentences)
- Training Data: CausalNewsCorpus V2
Training Results
- Accuracy: 83.82%
- Precision: 84.31%
- Recall: 83.20%
- F1 Score: 83.48%
Usage
from transformers import RobertaTokenizer, RobertaForSequenceClassification
import torch
# Load model and tokenizer
model_name = "causal-narrative/roberta-causal-narrative-classifier"
tokenizer = RobertaTokenizer.from_pretrained(model_name)
model = RobertaForSequenceClassification.from_pretrained(model_name)
# Predict
text = "The heavy rain caused flooding in the city."
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits, dim=-1).item()
print(f"Is causal: {prediction == 1}")
Labels
- 0: Non-causal sentence
- 1: Causal narrative sentence