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:
- 8aeeacf9ea1dba10d67798cf6490d834882338526985e32857945fd96e4e9f48
- Size of remote file:
- 3.26 kB
- SHA256:
- 126cb774085794dce8bc4fad0ca3f3d6b821be011cf4b1b3669ba50028557e9f
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