Instructions to use ixa-ehu/roberta-eus-mc4-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ixa-ehu/roberta-eus-mc4-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ixa-ehu/roberta-eus-mc4-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ixa-ehu/roberta-eus-mc4-base-cased") model = AutoModelForMaskedLM.from_pretrained("ixa-ehu/roberta-eus-mc4-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f61603f481743ce8a7fb6c3ba1ba5e98c23dee231102ae39b5774ba80893801b
- Size of remote file:
- 652 MB
- SHA256:
- 79f0d38a703743006c582e05c2cf373336b00bedcf1c111dbfbe8756e8df9a32
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