Instructions to use aajrami/bert-sr-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aajrami/bert-sr-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aajrami/bert-sr-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aajrami/bert-sr-base") model = AutoModel.from_pretrained("aajrami/bert-sr-base") - Notebooks
- Google Colab
- Kaggle
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license: cc-by-4.0
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## bert-sr-base
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## License
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CC BY 4.0
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license: cc-by-4.0
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## bert-sr-base
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A BERT base Language Model with a **shuffle + random** pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties?](https://aclanthology.org/2022.acl-short.16/)
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## License
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CC BY 4.0
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