Instructions to use dbmdz/bert-tiny-historic-multilingual-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbmdz/bert-tiny-historic-multilingual-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dbmdz/bert-tiny-historic-multilingual-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-tiny-historic-multilingual-cased") model = AutoModelForMaskedLM.from_pretrained("dbmdz/bert-tiny-historic-multilingual-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#2)
- Adding `safetensors` variant of this model (009ae84988eb021a6a6a4aa5b7bdcd4f17cd3a24) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>9ddd9b4
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metrics: add TensorBoard logs
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model: add original TensorFlow checkpoints
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git: track original TensorFlow checkpoints
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config: update it
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model: add TensorFlow version
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config: add architecture information
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config: add model type
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model/tokenizer: add initial version
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readme: add metadata
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readme: add initial version
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initial commit
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