README.md

---
license: mit
language:
- en
metrics:
- accuracy
library_name: transformers
tags:
- logical-reasoning
- logical-equivalence
- constrastive-learning
---

# AMR-LE
This is a branch which includes the model weight for AMR-LE. AMR-LE is a model that been fine-tuned on AMR-based logic-driven augmented data. The data is formed as (original sentence, logical equivalence sentence, logical inequivalence sentence). We use Abstract Meaning Representation (AMR) to automatically construct logical equivalence and logical inequivalence sentences. We use constrastive learning to train the model to learn to identify whether two sentences are logically equivalent or logically inequivalent. You are welcome to fine-tune the model weights on the dowstream tasks as logical reasoning reading comprehension tasks (ReClor and LogiQA) and natural language inference tasks (MNLI, MRPC, QNLI, RTE and QQP). We achieved #2 on the ReClor Leaderboard.

Here is the original links for AMR-LE including paper, project and leaderboard.

Paper: https://arxiv.org/abs/2305.12599

Project: https://github.com/Strong-AI-Lab/Logical-Equivalence-driven-AMR-Data-Augmentation-for-Representation-Learning

Leaderboard: https://eval.ai/web/challenges/challenge-page/503/leaderboard/1347

In this repository, we upload the model weight which has been trained on the dataset that has the ratio of positive sample and negative sample as 1 and 3.
## How to load the model weight?
```
from transformers import AutoModel

model = AutoModel.from_pretrained("qbao775/AMR-LE-DeBERTa-V2-XXLarge-Contraposition-Double-Negation-Implication-Commutative-Pos-Neg-1-3")

```

## Citation
```
@article{bao2023contrastive,
  title={Contrastive Learning with Logic-driven Data Augmentation for Logical Reasoning over Text},
  author={Bao, Qiming and Peng, Alex Yuxuan and Deng, Zhenyun and Zhong, Wanjun and Tan, Neset and Young, Nathan and Chen, Yang and Zhu, Yonghua and Witbrock, Michael and Liu, Jiamou},
  journal={arXiv preprint arXiv:2305.12599},
  year={2023}
}
```