Instructions to use brad1141/bertBasev2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brad1141/bertBasev2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="brad1141/bertBasev2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("brad1141/bertBasev2") model = AutoModelForSequenceClassification.from_pretrained("brad1141/bertBasev2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 37d07f6d5ffc6059064cb7b5be7680c75d8c8ecb51f22a424e3cc4dfea280946
- Size of remote file:
- 2.99 kB
- SHA256:
- 3f81b99eb6c6c3ea9d902d8b6daea7d3fa37a8d35ce9bc7cae763aa9ed47e82e
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