Instructions to use stefan-it/bort with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stefan-it/bort with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="stefan-it/bort")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("stefan-it/bort") model = AutoModelForMaskedLM.from_pretrained("stefan-it/bort", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.8a3fc67
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upload flax model
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allow flax
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config: fix tokenizer class
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config: change model_type and tokenizer_class
It uses BERT for model and RoBERTa for tokenizer now8fbdb7f
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bort: add tokenizer-related files
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bort: add initial version of TensorFlow BORT model
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bort: add initial model version
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bort: add initial configuration
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initial commit
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