Instructions to use karths/binary_classification_train_defect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_defect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_defect")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_defect") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_defect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download top_repo_data.csv from karths/binary_classification_train_defect: direct link, hf CLI and curl.
- Browser
- Download file 13.2 MB
-
https://huggingface.co/karths/binary_classification_train_defect/resolve/main/top_repo_data.csv
- Command line
-
hf download hf://karths/binary_classification_train_defect/top_repo_data.csv
-
curl -L -o top_repo_data.csv https://huggingface.co/karths/binary_classification_train_defect/resolve/main/top_repo_data.csv
13.2 MB
- Xet hash:
- 31a67ee7fc78affac1fedeff945a39bbe1e258a6b381547b2948aff676af0da4
- Size of remote file:
- 13.2 MB
- SHA256:
- 0a9e47969e7b35bfc8ea67dae2c07c94e6ae0e4e8e058b212d0cde42e5510e4b
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