Instructions to use lukecarlate/BERT_CM_Num with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/BERT_CM_Num with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/BERT_CM_Num")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/BERT_CM_Num") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/BERT_CM_Num") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d5a3c05a6d0eb8fad81f29180a45a743aa9ad6eca4e30a36012dfe963ffa5a7
- Size of remote file:
- 3 kB
- SHA256:
- 49447d5040fb432a33856200f357ade0392c45c3f679de8e041e0168459d7833
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.