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:
- 1ad20bc9e32e1410f95311510c1014e7301e8adc57275190e3778fc36f85b46e
- Size of remote file:
- 3.45 kB
- SHA256:
- 7e2f0592b2d424e5ead0bd04b419d632164176ca808debc0f82b2f9c1b0a874e
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