Instructions to use priyabrat/new_bert_url_clasification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use priyabrat/new_bert_url_clasification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="priyabrat/new_bert_url_clasification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("priyabrat/new_bert_url_clasification") model = AutoModelForSequenceClassification.from_pretrained("priyabrat/new_bert_url_clasification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from priyabrat/new_bert_url_clasification: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/priyabrat/new_bert_url_clasification/resolve/main/training_args.bin
- Command line
-
hf download hf://priyabrat/new_bert_url_clasification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/priyabrat/new_bert_url_clasification/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- 514a201bfd856372c5d5355ec21aa6961488052be040f1b568884430cb7531f3
- Size of remote file:
- 3.45 kB
- SHA256:
- 13109e54aa1f11a3d2dfc5171ab23dcb75d031459b4b2692871d27990ead7eff
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