Fill-Mask
Transformers
PyTorch
Safetensors
English
nexterabert
encoder
masked-lm
long-context
custom_code
Instructions to use RikkaBotan/NexteraBERT-Mezzoforte-220M-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RikkaBotan/NexteraBERT-Mezzoforte-220M-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="RikkaBotan/NexteraBERT-Mezzoforte-220M-en", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RikkaBotan/NexteraBERT-Mezzoforte-220M-en", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/NexteraBERT_layers.png from RikkaBotan/NexteraBERT-Mezzoforte-220M-en: direct link, hf CLI and curl.
- Browser
- Download file 846 kB
-
https://huggingface.co/RikkaBotan/NexteraBERT-Mezzoforte-220M-en/resolve/main/assets/NexteraBERT_layers.png
- Command line
-
hf download hf://RikkaBotan/NexteraBERT-Mezzoforte-220M-en/assets/NexteraBERT_layers.png
-
curl -L -o NexteraBERT_layers.png https://huggingface.co/RikkaBotan/NexteraBERT-Mezzoforte-220M-en/resolve/main/assets/NexteraBERT_layers.png
846 kB

- Xet hash:
- 1a2906a7f9a648b6aa4a6d72128b229102e3937dbe1b1cdb12274f78d86c54e5
- Size of remote file:
- 846 kB
- SHA256:
- 30cb9b37f5d839fb178070914426439c1c4bb3a39b8acf9037d914e052bf79e1
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