Instructions to use Fredr0id/proquolm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fredr0id/proquolm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fredr0id/proquolm")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fredr0id/proquolm") model = AutoModelForSequenceClassification.from_pretrained("Fredr0id/proquolm") - Notebooks
- Google Colab
- Kaggle
Model Summary
This model is a finetuned bert-base-german-uncased for binary classification of short quotations between
literary works and scholarly works. The model is intended to be used as part of our tool ProQuoLM. More information can be found in the corresponding
repository and our paper
A Novel Approach for Identification and Linking of Short Quotations in Scholarly Texts and Literary Works.
Citation
@article{arnold2023,
author = {Frederik Arnold, Robert Jäschke},
title = {A Novel Approach for Identification and Linking of Short Quotations in Scholarly Texts and Literary Works},
volume = {2},
year = {2023},
url = {https://jcls.io/article/id/3590/},
issue = {1},
doi = {10.48694/jcls.3590},
month = {1},
publisher={Universitäts- und Landesbibliothek Darmstadt},
journal = {Journal of Computational Literary Studies}
}
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Model tree for Fredr0id/proquolm
Base model
dbmdz/bert-base-german-uncased