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 test_data_for_future_evaluation.csv from karths/binary_classification_train_service: direct link, hf CLI and curl.
- Browser
- Download file 92.5 MB
-
https://huggingface.co/karths/binary_classification_train_service/resolve/main/test_data_for_future_evaluation.csv
- Command line
-
hf download hf://karths/binary_classification_train_service/test_data_for_future_evaluation.csv
-
curl -L -o test_data_for_future_evaluation.csv https://huggingface.co/karths/binary_classification_train_service/resolve/main/test_data_for_future_evaluation.csv
92.5 MB
- Xet hash:
- c2958d136e6a88dbbabfeb5b6af4c521e488c4403cfae182293b0632088e8a3f
- Size of remote file:
- 92.5 MB
- SHA256:
- 712dc65aae40ec65db0fe89f640a7ddfebf83d0fceaa50bdf5259a645314c2de
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.