Instructions to use lukecarlate/SecBERT_CM_Num with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/SecBERT_CM_Num with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/SecBERT_CM_Num")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/SecBERT_CM_Num") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/SecBERT_CM_Num") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 879793331e1256cf5e357c50e7c3b1d8b0ec8029d7050810c29c36a8c67d0ed5
- Size of remote file:
- 437 MB
- SHA256:
- 4d482906f25d003539744809a63d9d028149680c7fe7c53baee4da4fdf5faa62
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