Instructions to use sofom/bert-base-uncased-turingbench-aa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sofom/bert-base-uncased-turingbench-aa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sofom/bert-base-uncased-turingbench-aa")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sofom/bert-base-uncased-turingbench-aa") model = AutoModelForSequenceClassification.from_pretrained("sofom/bert-base-uncased-turingbench-aa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c95afc20132310a35d46c14e37ac0bdf5026495c0752d9f7a9b5a7fcd747645
- Size of remote file:
- 219 MB
- SHA256:
- 25c3f3495a2bfef371a8ba8baf32c3a923d486d85a4f68822ef3dd483ac1d448
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