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
Download pytorch_model.bin from dbmdz/bert-tiny-historic-multilingual-cased: direct link, hf CLI and curl.
- Browser
- Download file 18.5 MB
-
https://huggingface.co/dbmdz/bert-tiny-historic-multilingual-cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dbmdz/bert-tiny-historic-multilingual-cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dbmdz/bert-tiny-historic-multilingual-cased/resolve/main/pytorch_model.bin
18.5 MB
- Xet hash:
- ce4f7479d5fda22becd6a05e27de4d41bb1c44a00fe4e1c2aa970576cee50e63
- Size of remote file:
- 18.5 MB
- SHA256:
- d6894cf575006b4305e36be437e4a1759da79eba58adb8bcdb459a7d6f9a7695
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