Instructions to use lukecarlate/BERT_Num with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/BERT_Num with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/BERT_Num")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/BERT_Num") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/BERT_Num") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fc30ee6a6fd2bdec1ca6699d01ed2987e3878bd7b838f7356ed69a78ca62068
- Size of remote file:
- 438 MB
- SHA256:
- b606fab1beb9d810216d2a7fa995867d1a9947ad77001498d6a34c28f01aba66
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