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:
- 7eae9a814fd420bdb5ae1463fded7a5157cadfb2ff7b099398277f2cbe2ec6b0
- Size of remote file:
- 2.24 GB
- SHA256:
- fb7a1f7a1369da1bf12d799211d25d3cffeee985c6cfb39a63cf9ba8ebb598e5
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