Instructions to use lmvasque/readability-es-benchmark-bertin-es-sentences-2class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmvasque/readability-es-benchmark-bertin-es-sentences-2class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lmvasque/readability-es-benchmark-bertin-es-sentences-2class")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lmvasque/readability-es-benchmark-bertin-es-sentences-2class") model = AutoModelForSequenceClassification.from_pretrained("lmvasque/readability-es-benchmark-bertin-es-sentences-2class") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -56,7 +56,7 @@ If you use our results and scripts in your research, please cite our work: "[A B
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```
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@inproceedings{vasquez-rodriguez-etal-2022-benchmarking,
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title = "
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author = "V{\'a}squez-Rodr{\'\i}guez, Laura and
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Cuenca-Jim{\'\e}nez, Pedro-Manuel and
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Morales-Esquivel, Sergio Esteban and
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```
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@inproceedings{vasquez-rodriguez-etal-2022-benchmarking,
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title = "A Benchmark for Neural Readability Assessment of Texts in Spanish",
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author = "V{\'a}squez-Rodr{\'\i}guez, Laura and
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Cuenca-Jim{\'\e}nez, Pedro-Manuel and
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Morales-Esquivel, Sergio Esteban and
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