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 metrics_std.json from karths/binary_classification_train_defect: direct link, hf CLI and curl.
- Browser
- Download file 206 Bytes
-
https://huggingface.co/karths/binary_classification_train_defect/resolve/main/metrics_std.json
- Command line
-
hf download hf://karths/binary_classification_train_defect/metrics_std.json
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curl -L -o metrics_std.json https://huggingface.co/karths/binary_classification_train_defect/resolve/main/metrics_std.json
206 Bytes
| { | |
| "precision": 0.029413786308644156, | |
| "recall": 0.034152026680727844, | |
| "f1": 0.03171557632986603, | |
| "auc": 0.009603915372343777, | |
| "acc": 0.030645026009323964, | |
| "mcc": 0.06133041110098471 | |
| } |