Instructions to use ixa-ehu/roberta-eus-euscrawl-large-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ixa-ehu/roberta-eus-euscrawl-large-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ixa-ehu/roberta-eus-euscrawl-large-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ixa-ehu/roberta-eus-euscrawl-large-cased") model = AutoModelForMaskedLM.from_pretrained("ixa-ehu/roberta-eus-euscrawl-large-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ixa-ehu/roberta-eus-euscrawl-large-cased: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/ixa-ehu/roberta-eus-euscrawl-large-cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ixa-ehu/roberta-eus-euscrawl-large-cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ixa-ehu/roberta-eus-euscrawl-large-cased/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 26eb702e610a732020c5789d6f6af448c32ebd33fee8c2b9973d444242160039
- Size of remote file:
- 1.42 GB
- SHA256:
- ec448e7b727cf46f21c734fcf4e3b56898cf1b20c5017e7f322a8d014ba4acdc
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