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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81d7c29dc64b30040a866c6d5d90906a73e0a1f6b738f0a4736c36e665b3d587
- Size of remote file:
- 4.98 kB
- SHA256:
- 84d603116c84ee65296eb0816a71072e8d9de2f16daf54ea38541099bca88b56
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