Instructions to use classla/xlm-r-bertic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use classla/xlm-r-bertic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="classla/xlm-r-bertic")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("classla/xlm-r-bertic") model = AutoModelForMaskedLM.from_pretrained("classla/xlm-r-bertic") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70fc2eabec8b26aec99dd8f5c9a7c1d704f5fcd8611fa0b1ea9e821945d6acb7
- Size of remote file:
- 3.2 kB
- SHA256:
- 247c3bdd68d09cc83f152dd6a5f6c55e0b749831f92bc9b4114c74256b2e9633
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