sileod/discovery
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How to use sileod/roberta-base-discourse-marker-prediction with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="sileod/roberta-base-discourse-marker-prediction") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sileod/roberta-base-discourse-marker-prediction")
model = AutoModelForSequenceClassification.from_pretrained("sileod/roberta-base-discourse-marker-prediction")roberta-base pretrained on discourse marker prediction on the Discovery dataset with a validation accuracy of 30.93% (majority class is 0.57%)
https://github.com/sileod/discovery
https://huggingface.co/datasets/discovery
This model can also be used as a pretrained model for NLU, pragmatics and discourse tasks
@inproceedings{sileo-etal-2019-mining,
title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
author = "Sileo, Damien and
Van De Cruys, Tim and
Pradel, Camille and
Muller, Philippe",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1351",
doi = "10.18653/v1/N19-1351",
pages = "3477--3486",
}