Instructions to use lukecarlate/SECBERT_Num with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/SECBERT_Num with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/SECBERT_Num")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/SECBERT_Num") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/SECBERT_Num") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6c7042f239714ae7736418b203758818400fa34db85cc67c0ed3475dfafd03a
- Size of remote file:
- 437 MB
- SHA256:
- 95d0f5143db59b6ed7b3da916ab5465c7f6ce73db519b87d13a0f7fbdf593c03
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