Instructions to use nandapraisal/relevance-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nandapraisal/relevance-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nandapraisal/relevance-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nandapraisal/relevance-analysis") model = AutoModelForSequenceClassification.from_pretrained("nandapraisal/relevance-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e387507dfb7ad0b0f2e9c821bab0f50ad42753e612f5968eae4c1295c302d343
- Size of remote file:
- 5.24 kB
- SHA256:
- ea8fbc2f76054862e3909a22092c8c5d2f4a82bc9e855c54abba07d8f43a13d9
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