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 test_data_for_future_evaluation.csv from karths/binary_classification_train_process: direct link, hf CLI and curl.
- Browser
- Download file 21.9 MB
-
https://huggingface.co/karths/binary_classification_train_process/resolve/077a623b98ff38da026e81eb30054aeba158097f/test_data_for_future_evaluation.csv
- Command line
-
hf download hf://karths/binary_classification_train_process@077a623b98ff38da026e81eb30054aeba158097f/test_data_for_future_evaluation.csv
-
curl -L -o test_data_for_future_evaluation.csv https://huggingface.co/karths/binary_classification_train_process/resolve/077a623b98ff38da026e81eb30054aeba158097f/test_data_for_future_evaluation.csv
21.9 MB
- Xet hash:
- aebe0cee4fe4c04044b659b212270dab7eeafdda9e5a7d492b01c122e7e0cf7c
- Size of remote file:
- 21.9 MB
- SHA256:
- bdea62c87a939b813d24892cc4656111053e4622373318d7f10bfe91ea521746
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