---
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

```python
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