Instructions to use lgrobol/xlm-r-CreoleEval_msa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lgrobol/xlm-r-CreoleEval_msa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lgrobol/xlm-r-CreoleEval_msa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lgrobol/xlm-r-CreoleEval_msa") model = AutoModelForMaskedLM.from_pretrained("lgrobol/xlm-r-CreoleEval_msa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68fb6d4d9eaf476f0c31f0c69881987604a2d14b68b60fbe613c97ce843ab062
- Size of remote file:
- 1.11 GB
- SHA256:
- 144de4a9df83262bdc46c1ce46c08e897f6b25aada58e7309c27cbd96498c39f
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