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 pytorch_model.bin from priyabrat/new_bert_url_clasification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/priyabrat/new_bert_url_clasification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://priyabrat/new_bert_url_clasification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/priyabrat/new_bert_url_clasification/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- ecdd638db429ee5059c004e111f9918e80332d9c3848ba171b355979e396b58c
- Size of remote file:
- 438 MB
- SHA256:
- 56a79ebf00e7617bf9b30c673c5a1d6979a43da73b7c04029edb6003f463db3e
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