Instructions to use UMCU/RobBERT_NegationDetection_32xTokenWindow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMCU/RobBERT_NegationDetection_32xTokenWindow with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="UMCU/RobBERT_NegationDetection_32xTokenWindow")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("UMCU/RobBERT_NegationDetection_32xTokenWindow") model = AutoModelForTokenClassification.from_pretrained("UMCU/RobBERT_NegationDetection_32xTokenWindow") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Usage
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If you use the model in your work please
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https://doi.org/10.5281/zenodo.6980076
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## References
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Paper: Pieter Delobelle, Thomas Winters, Bettina Berendt (2020), RobBERT: a Dutch RoBERTa-based Language Model, Findings of the Association for Computational Linguistics: EMNLP 2020
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## Usage
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If you use the model in your work please use the following referrals;
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(model) https://doi.org/10.5281/zenodo.6980076 and (paper) https://doi.org/10.1186/s12859-022-05130-x
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## References
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Paper: Pieter Delobelle, Thomas Winters, Bettina Berendt (2020), RobBERT: a Dutch RoBERTa-based Language Model, Findings of the Association for Computational Linguistics: EMNLP 2020
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