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:
- df636bab6c3ec77bb8358012970a5a1a71c0bfa5392c4fa2991bbf977088469f
- Size of remote file:
- 3.45 kB
- SHA256:
- 10b36cfb536e2122fdc9a4ed6e5a96a0136ca80ad8ccc358a10b9d2b6d5a5a79
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.