Transformers in the Service of Description Logic-based Contexts
Paper • 2311.08941 • Published
How to use angelosps/delta_m with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="angelosps/delta_m") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("angelosps/delta_m")
model = AutoModelForSequenceClassification.from_pretrained("angelosps/delta_m")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("angelosps/delta_m")
model = AutoModelForSequenceClassification.from_pretrained("angelosps/delta_m")Fine-tuning a transformer model for textual entailment over expressive contexts generated from description logic knowledge bases. Specifically, the model is given a context (a set of facts and rules) and a question. 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.
For more info please see our paper.
DELTAM is a DeBERTaV3 large model fine-tuned on the DELTAD dataset.
microsoft/deberta-v3-largeBibTeX:
@misc{poulis2024transformers,
title={Transformers in the Service of Description Logic-based Contexts},
author={Angelos Poulis and Eleni Tsalapati and Manolis Koubarakis},
year={2024},
eprint={2311.08941},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="angelosps/delta_m")