12ml/e-CARE
Dataset of (Du et al., 2022) (Unofficial reupload) Abstract Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal fact to facilitate the causal reasoning process. However, such explanation information still remains absent in existing causal reasoning resources. In this paper, we fill this gap by… See the full description on the dataset page: https://huggingface.co/datasets/12ml/e-CARE.
| Author / organization | 12ml |
|---|---|
| Downloads | 5.2K (as reported by Hugging Face) |
| Likes | 3 |
| Created | 21 December 2022 |
| Last updated at source | 25 March 2024 |
Connected in the knowledge graph
- tasksource/deberta-base-long-nliModel · used to train
Recent changes
- 16 Oct 2026 Description: Dataset of (Du et al., 2022) (Unofficial reupload) Abstract Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can → Dataset of (Du et al., 2022) (Unofficial reupload) Abstract Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can
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