Instructions to use qud-parsing/relevance_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qud-parsing/relevance_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="qud-parsing/relevance_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("qud-parsing/relevance_model") model = AutoModelForSequenceClassification.from_pretrained("qud-parsing/relevance_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5221387bd29c71dac3e72b4ce647dd7515e92453cb5f260552ee0e95a07240fb
- Size of remote file:
- 1.42 GB
- SHA256:
- f859173d705e99a50dcfb6557d14c9c8f7fc153e347e6784c3897e88e96125e0
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