Instructions to use karths/binary_classification_train_code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_code")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_code") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_code", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_data_for_future_evaluation.csv from karths/binary_classification_train_code: direct link, hf CLI and curl.
- Browser
- Download file 156 MB
-
https://huggingface.co/karths/binary_classification_train_code/resolve/main/test_data_for_future_evaluation.csv
- Command line
-
hf download hf://karths/binary_classification_train_code/test_data_for_future_evaluation.csv
-
curl -L -o test_data_for_future_evaluation.csv https://huggingface.co/karths/binary_classification_train_code/resolve/main/test_data_for_future_evaluation.csv
156 MB
- Xet hash:
- 020586556313d3b932730c46f4f5f8dc042161382d45118ec1cf81a1eff9d4ce
- Size of remote file:
- 156 MB
- SHA256:
- 163ad0b1a9e526b779763b9777029650fb104c8660e8aee2bc2a7f1f75912c93
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.