Instructions to use activebus/BERT_Review with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use activebus/BERT_Review with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="activebus/BERT_Review")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("activebus/BERT_Review") model = AutoModelForMaskedLM.from_pretrained("activebus/BERT_Review") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 58bab959febd5f7c4ab2ffda16550b68dedf0019ed3613607fa76cc019b57e96
- Size of remote file:
- 438 MB
- SHA256:
- bfbe3a69334606805f671b99c649d938f3e4f4fff647ffce9bd3a5fc8da2fa48
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.