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--- |
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license: mit |
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tags: |
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- textual-entailment |
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- logical-reasoning |
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- deberta |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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--- |
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# DELTA: Description Logics with Transformers |
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Fine-tuning a transformer model for textual entailment over expressive contexts generated from description logic knowledge bases. |
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Specifically, the model is given a context (a set of facts and rules) and a question. |
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The model should answer with "True" if the question is logically implied from the context, "False" if it contradicts the context, and "Unknown" if none of the two. |
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For more info please see our paper. |
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## Model Details |
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### Model Description |
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DELTA<sub>M</sub> is a DeBERTaV3 large model fine-tuned on the DELTA<sub>D</sub> dataset. |
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- **License:** MIT |
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- **Finetuned from model:** `microsoft/deberta-v3-large` |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/angelosps/DELTA |
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- **Paper:** [Transformers in the Service of Description Logic-based Contexts](https://arxiv.org/abs/2311.08941) |
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<!-- ### Downstream Use [optional] --> |
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### Training Data --> |
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### Training Procedure --> |
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## Evaluation --> |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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#### Testing Data |
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--> |
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#### Metrics --> |
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### Results |
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[More Information Needed] --> |
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<!-- #### Summary --> |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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``` |
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@misc{poulis2024transformers, |
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title={Transformers in the Service of Description Logic-based Contexts}, |
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author={Angelos Poulis and Eleni Tsalapati and Manolis Koubarakis}, |
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year={2024}, |
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eprint={2311.08941}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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<!-- ## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] --> |
