Instructions to use classla/xlm-r-parla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use classla/xlm-r-parla with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="classla/xlm-r-parla")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("classla/xlm-r-parla") model = AutoModelForMaskedLM.from_pretrained("classla/xlm-r-parla", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7477c444291e6fa4d160caf95cb898ca4ef9a378d261df0a31d3045f5f87270
- Size of remote file:
- 2.24 GB
- SHA256:
- 10aa9629c3feedb52f1d7ebff1caf760df9c47e1b3f063a5fe695d9b6e0e8d60
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