Instructions to use NbAiLab/nb-roberta-base-scandi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-roberta-base-scandi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLab/nb-roberta-base-scandi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-roberta-base-scandi") model = AutoModelForMaskedLM.from_pretrained("NbAiLab/nb-roberta-base-scandi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
test
Browse files
run.sh
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python run_mlm_flax_stream.py \
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--hub_model_id="NbAiLab/nb-roberta-base-scandi" \
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--model_name_or_path="xlm-roberta-base" \
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--config_name="./config.json" \
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--tokenizer_name="./" \
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--logging_steps="50" \
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--save_steps="50" \
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--eval_steps="50" \
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--dtype="bfloat16" \
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--push_to_hub
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python run_mlm_flax_stream.py \
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--model_name_or_path="xlm-roberta-base" \
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--config_name="./config.json" \
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--tokenizer_name="./" \
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--logging_steps="50" \
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--save_steps="50" \
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--eval_steps="50" \
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--output_dir="./" \
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--dtype="bfloat16" \
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--hub_model_id="NbAiLab/nb-roberta-base-scandi" \
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--push_to_hub
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