Instructions to use waddledee/bertforsequenceclassification_news_title_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waddledee/bertforsequenceclassification_news_title_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="waddledee/bertforsequenceclassification_news_title_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("waddledee/bertforsequenceclassification_news_title_classification") model = AutoModelForSequenceClassification.from_pretrained("waddledee/bertforsequenceclassification_news_title_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e41685706e1fa7fdd3833da7a681f5cba092e06fb40b03ad9a84a6458c5dafa
- Size of remote file:
- 443 MB
- SHA256:
- 09aac43a47178566b7bc6b1a393d00724a5002b4a94e91573181cdfc6d431619
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