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 reduced_main_data.csv from karths/binary_classification_train_documentation: direct link, hf CLI and curl.
- Browser
- Download file 2.27 GB
-
https://huggingface.co/karths/binary_classification_train_documentation/resolve/37c4901248fd4785cd606b477defd1ba744b01d3/reduced_main_data.csv
- Command line
-
hf download hf://karths/binary_classification_train_documentation@37c4901248fd4785cd606b477defd1ba744b01d3/reduced_main_data.csv
-
curl -L -o reduced_main_data.csv https://huggingface.co/karths/binary_classification_train_documentation/resolve/37c4901248fd4785cd606b477defd1ba744b01d3/reduced_main_data.csv
2.27 GB
- Xet hash:
- 3597d19e054c20a56f4a2504dd7fd4cf9e172e5c523194f45c317d612db00a32
- Size of remote file:
- 2.27 GB
- SHA256:
- 783d3851840c8e85122f919bafa8b83b681095c7da8061930d71f4d4e9d2a605
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