Fill-Mask
Transformers
PyTorch
Safetensors
xlm-roberta
roberta
icelandic
norwegian
faroese
danish
swedish
masked-lm
Instructions to use vesteinn/ScandiBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vesteinn/ScandiBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vesteinn/ScandiBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vesteinn/ScandiBERT") model = AutoModelForMaskedLM.from_pretrained("vesteinn/ScandiBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Change `tokenizer_class` to "PreTrainedTokenizerFast"
#2
by saattrupdan - opened
This changes the tokenizer class to PreTrainedTokenizerFast, which allows it to be loaded properly with the TokenizersBackend. This is because it uses a BPE tokenizer and not the unigram tokenizer that XLM-RoBERTa does.
Source: https://github.com/huggingface/transformers/issues/47020#issuecomment-4893582482
vesteinn changed pull request status to merged