Instructions to use nikotang/span-nli-bert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikotang/span-nli-bert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nikotang/span-nli-bert-large")# Load model directly from transformers import AutoTokenizer, BertForIdentificationClassification tokenizer = AutoTokenizer.from_pretrained("nikotang/span-nli-bert-large") model = BertForIdentificationClassification.from_pretrained("nikotang/span-nli-bert-large") - Notebooks
- Google Colab
- Kaggle
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README.md
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---
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# Span NLI BERT (large)
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This model is fine-tuned according to the recommended hyperparameters in the Appendix of the paper,
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some of which differ from the hyperparameters in `data/conf_large.yml` in their repo.
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[1]: https://aclanthology.org/2021.findings-emnlp.164/
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[2]: https://huggingface.co/bert-large-uncased-whole-word-masking
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language:
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pipeline_tag: text-classification
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---
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# Span NLI BERT (large)
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This model is fine-tuned according to the recommended hyperparameters in the Appendix of the paper,
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some of which differ from the hyperparameters in `data/conf_large.yml` in their repo.
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ArXiv: <https://arxiv.org/abs/2110.01799>
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[1]: https://aclanthology.org/2021.findings-emnlp.164/
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[2]: https://huggingface.co/bert-large-uncased-whole-word-masking
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