Text Classification
Transformers
TensorFlow
TensorBoard
Safetensors
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use PDAP/url-relevance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PDAP/url-relevance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PDAP/url-relevance")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PDAP/url-relevance") model = AutoModelForSequenceClassification.from_pretrained("PDAP/url-relevance") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df5bcfedf0bd1b6d85b49eaf2c2eaad5b78787d72c1a2c07df32bc30ef266581
- Size of remote file:
- 273 MB
- SHA256:
- e89d5f425c7331aeda80b01db56502de2cb2a5e2a53d0554427c6caff67b1e0e
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