Instructions to use JoshuaAAX/bert-base-uncased-binary-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JoshuaAAX/bert-base-uncased-binary-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JoshuaAAX/bert-base-uncased-binary-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JoshuaAAX/bert-base-uncased-binary-classification") model = AutoModelForSequenceClassification.from_pretrained("JoshuaAAX/bert-base-uncased-binary-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7b1c45bf201074f97344ab227170360f55cbcd885b33f0f61b12ce4a4a8a1ec
- Size of remote file:
- 438 MB
- SHA256:
- d5d1b78bcfa02438c479c75b68513c32ac7bf947c66dd349f066e2001092289e
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