Instructions to use ltg/norbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ltg/norbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ltg/norbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ltg/norbert") model = AutoModelForMaskedLM.from_pretrained("ltg/norbert", 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.d9b9f96
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Link to NorBERT 2
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Automatic correction of README.md metadata. Contact website@huggingface.co for any question
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upload flax model
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allow flax
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Paper link
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Tokenizer config back
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Discarded basic tokenization to better fit our vocabulary
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New NLPL URL
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Removed duplicates from vocabulary
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Masked LM
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Special tokens
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Dummy special tokens file
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Added tokens placeholder
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Logo
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Tags
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Pipeline tag
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Added license
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Test
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README
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Header
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Pipeline
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Even more links
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Additional links
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TF checkpoint
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Original NorBERT commit
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initial commit
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