Fill-Mask
Transformers
PyTorch
Quechua
roberta
quechua
masked-language-modeling
nlp
low-resource
indigenous-language
Instructions to use rjzevallos/quebert_qu_bpe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rjzevallos/quebert_qu_bpe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rjzevallos/quebert_qu_bpe")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rjzevallos/quebert_qu_bpe") model = AutoModelForMaskedLM.from_pretrained("rjzevallos/quebert_qu_bpe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rjzevallos/quebert_qu_bpe: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/rjzevallos/quebert_qu_bpe/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rjzevallos/quebert_qu_bpe/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rjzevallos/quebert_qu_bpe/resolve/main/pytorch_model.bin
334 MB
- Xet hash:
- bafd7b59ce99438d71c9be79c16102ff3f11155a866644dac331e367f3c59182
- Size of remote file:
- 334 MB
- SHA256:
- 0eb6ff321bb732598c2f5bee8ffab7209c7dd36562e3a26f96e72562bf8b3542
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