Instructions to use karths/binary_classification_train_documentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_documentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_documentation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_documentation") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_documentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_data_for_future_evaluation.csv from karths/binary_classification_train_documentation: direct link, hf CLI and curl.
- Browser
- Download file 396 MB
-
https://huggingface.co/karths/binary_classification_train_documentation/resolve/37c4901248fd4785cd606b477defd1ba744b01d3/test_data_for_future_evaluation.csv
- Command line
-
hf download hf://karths/binary_classification_train_documentation@37c4901248fd4785cd606b477defd1ba744b01d3/test_data_for_future_evaluation.csv
-
curl -L -o test_data_for_future_evaluation.csv https://huggingface.co/karths/binary_classification_train_documentation/resolve/37c4901248fd4785cd606b477defd1ba744b01d3/test_data_for_future_evaluation.csv
396 MB
- Xet hash:
- c3be9490b2aa5f6a9751873d610142e25b265fc9ee3853094b96cbac1fbce52b
- Size of remote file:
- 396 MB
- SHA256:
- 620d3442596fda772a78efc12e54fdef5aedf286051c67517d068821496f42a9
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