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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PDAP/url-relevance") model = AutoModelForSequenceClassification.from_pretrained("PDAP/url-relevance", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from PDAP/url-relevance: direct link, hf CLI and curl.
- Browser
- Download file 273 MB
-
https://huggingface.co/PDAP/url-relevance/resolve/main/tf_model.h5
- Command line
-
hf download hf://PDAP/url-relevance/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/PDAP/url-relevance/resolve/main/tf_model.h5
273 MB
- Xet hash:
- adc496308dd92642cb307ba151a4cbc0235fd0a295883cd5239438109434129b
- Size of remote file:
- 273 MB
- SHA256:
- e36b3723386ad1b075b75aa9067d6a897cd1cae625248c8fe20c0072321c9d71
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