Instructions to use karths/binary_classification_train_process with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_process with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_process")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_process") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_process", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.onnx from karths/binary_classification_train_process: direct link, hf CLI and curl.
- Browser
- Download file 331 MB
-
https://huggingface.co/karths/binary_classification_train_process/resolve/main/model.onnx
- Command line
-
hf download hf://karths/binary_classification_train_process/model.onnx
-
curl -L -o model.onnx https://huggingface.co/karths/binary_classification_train_process/resolve/main/model.onnx
331 MB
- Xet hash:
- bc275055aad6c322a127a226f5b2e0c2b03517f70d7912f8e4ed066043a6297d
- Size of remote file:
- 331 MB
- SHA256:
- 813588abd66f2a092bd2ea85a3b75fa2d5c74d7de86c56b5132c77930aa08278
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.