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:
- 35086be8ed1e50e24cc3318348d278844e6a966843fb46179510c1b2a2e9c1de
- Size of remote file:
- 1.33 GB
- SHA256:
- dc70d89d9d1727fb099541092ea4351299c6ed695e82d2145fa3cb8612f63403
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