Instructions to use karths/binary_classification_train_secu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karths/binary_classification_train_secu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="karths/binary_classification_train_secu")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("karths/binary_classification_train_secu") model = AutoModelForSequenceClassification.from_pretrained("karths/binary_classification_train_secu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download classification_report.json from karths/binary_classification_train_secu: direct link, hf CLI and curl.
- Browser
- Download file 530 Bytes
-
https://huggingface.co/karths/binary_classification_train_secu/resolve/05adcef0fc64fa99b6dc3f759210d8aa7ac9fdfd/classification_report.json
- Command line
-
hf download hf://karths/binary_classification_train_secu@05adcef0fc64fa99b6dc3f759210d8aa7ac9fdfd/classification_report.json
-
curl -L -o classification_report.json https://huggingface.co/karths/binary_classification_train_secu/resolve/05adcef0fc64fa99b6dc3f759210d8aa7ac9fdfd/classification_report.json
530 Bytes
| {"0": {"precision": 0.9978501947595837, "recall": 0.9968953344958108, "f1-score": 0.9973725360877381, "support": 47026}, "1": {"precision": 0.9968763371844245, "recall": 0.9978370275190063, "f1-score": 0.9973564510087226, "support": 46695}, "accuracy": 0.9973645180909294, "macro avg": {"precision": 0.9973632659720041, "recall": 0.9973661810074086, "f1-score": 0.9973644935482304, "support": 93721}, "weighted avg": {"precision": 0.9973649856872087, "recall": 0.9973645180909294, "f1-score": 0.997364521952543, "support": 93721}} |