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:
- 1e397b3f9b878af01ad0b034fb818ba4168288d2211fe5b2cf903a77cfeb2e2e
- Size of remote file:
- 438 MB
- SHA256:
- 6c71272749d1e12037fc4e6bd1f2b7c4f31ac73280213b9e74d605e7229f3c1f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.