Instructions to use karths/binary_classification_train_build with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_build with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_build")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_build") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_build", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_data_for_future_evaluation.csv from karths/binary_classification_train_build: direct link, hf CLI and curl.
- Browser
- Download file 158 MB
-
https://huggingface.co/karths/binary_classification_train_build/resolve/main/test_data_for_future_evaluation.csv
- Command line
-
hf download hf://karths/binary_classification_train_build/test_data_for_future_evaluation.csv
-
curl -L -o test_data_for_future_evaluation.csv https://huggingface.co/karths/binary_classification_train_build/resolve/main/test_data_for_future_evaluation.csv
158 MB
- Xet hash:
- 58e1e0d93f8cffb8eb9752e43e9c3de5b36528e55c1a45ee7af7fca1c5ff5838
- Size of remote file:
- 158 MB
- SHA256:
- 733dfa5955db9e10d260c78dfecbfb7fafc9b6a8d37eeefdf724910a8cb5f224
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.