Instructions to use JoshuaAAX/bert-large-cased-binary-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JoshuaAAX/bert-large-cased-binary-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JoshuaAAX/bert-large-cased-binary-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JoshuaAAX/bert-large-cased-binary-classification") model = AutoModelForSequenceClassification.from_pretrained("JoshuaAAX/bert-large-cased-binary-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1375308ebfe646bb290e6df02f0d847ee1a582428fcc7e94a4add49502e0ab87
- Size of remote file:
- 5.78 kB
- SHA256:
- d4977855906dd39e995d5574e2f494520745c180f1f9aa43d62e1d0592f12520
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