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 reduced_main_data.csv from karths/binary_classification_train_defect: direct link, hf CLI and curl.
- Browser
- Download file 603 MB
-
https://huggingface.co/karths/binary_classification_train_defect/resolve/main/reduced_main_data.csv
- Command line
-
hf download hf://karths/binary_classification_train_defect/reduced_main_data.csv
-
curl -L -o reduced_main_data.csv https://huggingface.co/karths/binary_classification_train_defect/resolve/main/reduced_main_data.csv
603 MB
- Xet hash:
- ad5628697916f71b9bb5c4ecd912a8e4f79eeb7aa52bee20d7c3a9d71349e08d
- Size of remote file:
- 603 MB
- SHA256:
- bba2c2d8afa97666d09ac9ba804434380b04713898352c914b0acfe4fd960154
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.