Instructions to use JakobLindscheid/moral_foundations_loyalty-bert-DANN-eu_docs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JakobLindscheid/moral_foundations_loyalty-bert-DANN-eu_docs with Transformers:
# Load model directly from transformers import AutoTokenizer, BertForDomainAdversarialMoralityDetection tokenizer = AutoTokenizer.from_pretrained("JakobLindscheid/moral_foundations_loyalty-bert-DANN-eu_docs") model = BertForDomainAdversarialMoralityDetection.from_pretrained("JakobLindscheid/moral_foundations_loyalty-bert-DANN-eu_docs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Card for JakobLindscheid/moral_foundations_loyalty-bert-DANN-eu_docs
This model is part of the Master Thesis "Domain-adversarial moral foundation prediction for long EU legal documents". All related code is published on GitHub.
The models are finetuned for classifying moral foundations. We use domain-adversarial training to transfer knowledge from a labeled source domain to an unlabeled target domain.
This specific model is a finetuned google-bert/bert-base-uncased model that identifies the loyalty foundation, uses the MFRC, MFNC and MFNC as source domain and EU law documents as target domain.
Refer to JakobLindscheid/moral_foundations_ensemble-bert-DANN-eu_docs, which wraps all single label models in an ensemble model.
Overview of ensemble models
| Base model | Target domain | Model |
|---|---|---|
google-bert/bert-base-uncased |
MFNC | JakobLindscheid/moral_foundations_ensemble-bert-DANN-mfnc |
google-bert/bert-base-uncased |
EU docs | JakobLindscheid/moral_foundations_ensemble-bert-DANN-eu_docs |
FacebookAI/roberta-base |
MFNC | JakobLindscheid/moral_foundations_ensemble-roberta-DANN-mfnc |
FacebookAI/roberta-base |
EU docs | JakobLindscheid/moral_foundations_ensemble-roberta-DANN-eu_docs |
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Model tree for JakobLindscheid/moral_foundations_loyalty-bert-DANN-eu_docs
Base model
google-bert/bert-base-uncased