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/summary.png from RikkaBotan/NexteraBERT-Mezzoforte-220M-en: direct link, hf CLI and curl.
- Browser
- Download file 195 kB
-
https://huggingface.co/RikkaBotan/NexteraBERT-Mezzoforte-220M-en/resolve/main/assets/summary.png
- Command line
-
hf download hf://RikkaBotan/NexteraBERT-Mezzoforte-220M-en/assets/summary.png
-
curl -L -o summary.png https://huggingface.co/RikkaBotan/NexteraBERT-Mezzoforte-220M-en/resolve/main/assets/summary.png
195 kB

- Xet hash:
- db0a44de47009e899d3117794de73da7c3f1595862cb56523039e83b0447f891
- Size of remote file:
- 195 kB
- SHA256:
- 74d7ddc14ffc8ccf81e70646dfb3e97ca74e99b06087508fd36d682bea4256b1
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