---
license: mit
---
# roberta-temporal-predictor
A RoBERTa-base model that is fine-tuned on the [The New York Times Annotated Corpus](https://catalog.ldc.upenn.edu/LDC2008T19)
to predict temporal precedence of two events. This is used as the ``temporality prediction'' component
in our ROCK framework for reasoning about commonsense causality. See our [paper](https://arxiv.org/abs/2202.00436) for more details.

# Usage

For simplicity, we implement the following TempPredictor class. Example usage using the ``TempPredictor`` class:
```python
from transformers import (RobertaForMaskedLM, RobertaTokenizer)
from src.temp_predictor import TempPredictor

TORCH_DEV = "cuda:0" # change as needed

tp_roberta_ft = src.TempPredictor(
    model=RobertaForMaskedLM.from_pretrained("CogComp/roberta-temporal-predictor"),
    tokenizer=RobertaTokenizer.from_pretrained("CogComp/roberta-temporal-predictor"),
    device=TORCH_DEV
)

E1 = "The man turned on the faucet."
E2 = "Water flows out."
t12 = tp_roberta_ft(E1, E2, top_k=5)
print(f"P('{E1}' before '{E2}'): {t12}")
```

# BibTeX entry and citation info

```bib
@misc{zhang2022causal,
      title={Causal Inference Principles for Reasoning about Commonsense Causality}, 
      author={Jiayao Zhang and Hongming Zhang and Dan Roth and Weijie J. Su},
      year={2022},
      eprint={2202.00436},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```