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---
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license: mit
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widget:
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- text: "The man turned on the faucet <mask> water flows out."
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- text: "The woman received her pension <mask> she retired."
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---
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# roberta-temporal-predictor
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A RoBERTa-base model that is fine-tuned on the [The New York Times Annotated Corpus](https://catalog.ldc.upenn.edu/LDC2008T19)
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to predict temporal precedence of two events. This is used as the ``temporality prediction'' component
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in our ROCK framework for reasoning about commonsense causality. See our [paper](https://arxiv.org/abs/2202.00436) for more details.
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# Usage
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You can directly use this model for filling-mask tasks, as shown in the example widget.
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However, for better temporal inference, it is recommended to symmetrize the outputs as
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$$
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P(E_1 \prec E_2) = \frac{1}{2} (f(E_1,E_2) + f(E_2,E_1))
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$$
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where ``f(E_1,E_2)`` denotes the predicted probability for ``E_1`` to occur preceding ``E_2``.
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For simplicity, we implement the following TempPredictor class that incorporate this symmetrization automatically.
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Below is an example usage for the ``TempPredictor`` class:
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```python
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from transformers import (RobertaForMaskedLM, RobertaTokenizer)
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from src.temp_predictor import TempPredictor
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TORCH_DEV = "cuda:0" # change as needed
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tp_roberta_ft = src.TempPredictor(
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model=RobertaForMaskedLM.from_pretrained("CogComp/roberta-temporal-predictor"),
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tokenizer=RobertaTokenizer.from_pretrained("CogComp/roberta-temporal-predictor"),
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device=TORCH_DEV
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)
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E1 = "The man turned on the faucet."
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E2 = "Water flows out."
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t12 = tp_roberta_ft(E1, E2, top_k=5)
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print(f"P('{E1}' before '{E2}'): {t12}")
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```
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# BibTeX entry and citation info
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```bib
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@misc{zhang2022causal,
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title={Causal Inference Principles for Reasoning about Commonsense Causality},
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author={Jiayao Zhang and Hongming Zhang and Dan Roth and Weijie J. Su},
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year={2022},
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eprint={2202.00436},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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``` |
