Text Classification
Transformers
Safetensors
English
deberta-v2
deberta-v3
nli
political-science
trade-policy
intellectual-property
text-embeddings-inference
Instructions to use jacqpark/nte-deberta-ipr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jacqpark/nte-deberta-ipr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jacqpark/nte-deberta-ipr")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jacqpark/nte-deberta-ipr") model = AutoModelForSequenceClassification.from_pretrained("jacqpark/nte-deberta-ipr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add Zenodo DOI to citation
Browse files
README.md
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## Citation
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```bibtex
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@unpublished{park_nteipr,
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author = {Park, Jihye},
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title = {
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note = {Working paper, University of Geneva},
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year = {2026}
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}
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## Citation
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To cite the model and companion package directly.
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```bibtex
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@software{park_nteText_2026,
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author = {Park, Jihye},
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title = {nteText: USTR National Trade Estimate Corpus and IPR Hypothesis Scores},
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year = {2026},
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publisher = {Zenodo},
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version = {v0.1.0},
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doi = {10.5281/zenodo.20028790},
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url = {https://github.com/jacqpark/nteText}
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}
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```
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To cite the working paper that introduces the measure.
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```bibtex
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@unpublished{park_nteipr,
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author = {Park, Jihye},
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title = {Aid, Lending, and TRIPS},
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note = {Working paper, University of Geneva},
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year = {2026}
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}
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