Instructions to use tiltaf/Packagex_Assignment-document-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiltaf/Packagex_Assignment-document-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiltaf/Packagex_Assignment-document-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("tiltaf/Packagex_Assignment-document-classification") model = AutoModelForSequenceClassification.from_pretrained("tiltaf/Packagex_Assignment-document-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiltaf/Packagex_Assignment-document-classification: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/tiltaf/Packagex_Assignment-document-classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://tiltaf/Packagex_Assignment-document-classification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiltaf/Packagex_Assignment-document-classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
504 MB
- Xet hash:
- b4d82bdaebb29c1b40132ecad2d0b67d0971e23a3034071253f086ada384becb
- Size of remote file:
- 504 MB
- SHA256:
- 8633c4efeac900f6159f865b7b5913935bb221c8ab5098b3a225c21e2049cc94
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