Instructions to use nbolton04/bert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nbolton04/bert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nbolton04/bert_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nbolton04/bert_model") model = AutoModelForSequenceClassification.from_pretrained("nbolton04/bert_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd83a90b81da7a792bdc27101b983c9394d6353817ac71438b17903759fa454c
- Size of remote file:
- 268 MB
- SHA256:
- 86cefe2d935b6f1ba931cda193d70c65ec6c6dec390fda9370534e5d5d532a8d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.