Instructions to use priyabrat/Blog_category_url_categorisation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use priyabrat/Blog_category_url_categorisation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="priyabrat/Blog_category_url_categorisation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("priyabrat/Blog_category_url_categorisation") model = AutoModelForSequenceClassification.from_pretrained("priyabrat/Blog_category_url_categorisation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20f2eee6ad897820bc443104048af35726b12950c04e7bb40bfcecda80c49a69
- Size of remote file:
- 268 MB
- SHA256:
- e069527398127354355a0d102b1adf05682cdf1e8ef46538ede0eb4525b351c4
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