Instructions to use SALT-NLP/CultureBank-Relevance-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SALT-NLP/CultureBank-Relevance-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SALT-NLP/CultureBank-Relevance-Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SALT-NLP/CultureBank-Relevance-Classifier") model = AutoModelForSequenceClassification.from_pretrained("SALT-NLP/CultureBank-Relevance-Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6651ac57b0ed9f1cfc7892d1fc796dad25e13ab32db9f82d5d5e3a9d59799213
- Size of remote file:
- 268 MB
- SHA256:
- f6d75cef31cde04782ed67f3c5199887ee91817a31108d03a6e0c58e2057803f
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