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 test_top_repo_data.csv from karths/binary_classification_train_defect: direct link, hf CLI and curl.
- Browser
- Download file 28.1 MB
-
https://huggingface.co/karths/binary_classification_train_defect/resolve/main/test_top_repo_data.csv
- Command line
-
hf download hf://karths/binary_classification_train_defect/test_top_repo_data.csv
-
curl -L -o test_top_repo_data.csv https://huggingface.co/karths/binary_classification_train_defect/resolve/main/test_top_repo_data.csv
28.1 MB
- Xet hash:
- 6ac34a035fb2881eb4977c26ff91391d3e27e5760f7ff509bbe79e6c7ad7e37f
- Size of remote file:
- 28.1 MB
- SHA256:
- 1d0f3242bc9c8a4a37ad72a1bd80d63d5fef8c9cc207f47cf1de437b51d6e1e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.