Instructions to use AI-Sweden-Models/bert-large-nordic-pile-1M-steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI-Sweden-Models/bert-large-nordic-pile-1M-steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="AI-Sweden-Models/bert-large-nordic-pile-1M-steps")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI-Sweden-Models/bert-large-nordic-pile-1M-steps") model = AutoModelForMaskedLM.from_pretrained("AI-Sweden-Models/bert-large-nordic-pile-1M-steps") - Notebooks
- Google Colab
- Kaggle
Bert Large Cased
This model is trained on the Swedish subset of the Nordic Pile corpus using the Wafer-Scale Engine (WSE).
from transformers import pipeline
classifier = pipeline("fill-mask", model="AI-Sweden-Models/bert-large-nordic-pile-1M-steps")
classifier("Stockholm är Sveriges [MASK].")
[{'score': 0.6612114906311035,
'token': 21168,
'token_str': 'huvudstad',
'sequence': 'Stockholm är Sveriges huvudstad.'},
{'score': 0.06771031022071838,
'token': 7625,
'token_str': 'hjärta',
'sequence': 'Stockholm är Sveriges hjärta.'},
{'score': 0.040731027722358704,
'token': 40158,
'token_str': 'framsida',
'sequence': 'Stockholm är Sveriges framsida.'},
{'score': 0.03236057981848717,
'token': 58649,
'token_str': 'mittpunkt',
'sequence': 'Stockholm är Sveriges mittpunkt.'},
{'score': 0.018861057236790657,
'token': 37202,
'token_str': 'navel',
'sequence': 'Stockholm är Sveriges navel.'}]
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