Instructions to use google/fnet-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/fnet-large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("google/fnet-large") model = AutoModelForPreTraining.from_pretrained("google/fnet-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# FNet large model
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Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was
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introduced in [this paper](https://arxiv.org/abs/2105.03824) and first released in [this repository](https://github.com/google-research/f_net).
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This model is uncased: it does not make a difference between english and English. The model achieves 0.63 accuracy on MLM objective and 0.82 on NSP objective.
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Disclaimer: This model card has been written by [gchhablani](https://huggingface.co/gchhablani).
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# FNet large model
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Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was
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+
introduced in [this paper](https://arxiv.org/abs/2105.03824) and first released in [this repository](https://github.com/google-research/google-research/f_net).
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This model is uncased: it does not make a difference between english and English. The model achieves 0.63 accuracy on MLM objective and 0.82 on NSP objective.
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Disclaimer: This model card has been written by [gchhablani](https://huggingface.co/gchhablani).
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