Instructions to use karths/binary_classification_train_service with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_service with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_service")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_service") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_service", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download reduced_main_data.csv from karths/binary_classification_train_service: direct link, hf CLI and curl.
- Browser
- Download file 515 MB
-
https://huggingface.co/karths/binary_classification_train_service/resolve/main/reduced_main_data.csv
- Command line
-
hf download hf://karths/binary_classification_train_service/reduced_main_data.csv
-
curl -L -o reduced_main_data.csv https://huggingface.co/karths/binary_classification_train_service/resolve/main/reduced_main_data.csv
515 MB
- Xet hash:
- ed8f1169f15168e7afdd15d25c907c064362c8881164ff5e9f00ec14f7e86e0c
- Size of remote file:
- 515 MB
- SHA256:
- 2094f68d7c804ecb000d36ef4705aee88e041aef341e4a8ed145e6bf77e26db2
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