Instructions to use AungMoonLord/bert-log-anomaly-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AungMoonLord/bert-log-anomaly-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AungMoonLord/bert-log-anomaly-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AungMoonLord/bert-log-anomaly-detection") model = AutoModelForSequenceClassification.from_pretrained("AungMoonLord/bert-log-anomaly-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -127,7 +127,7 @@ print(predict_log(text3))
|
|
| 127 |
## Training Data
|
| 128 |
|
| 129 |
|
| 130 |
-
- SQL database transaction logs (1,611 samples) synthetically generated
|
| 131 |
|
| 132 |
- Each log labeled as either `Normal` or `Anomaly`
|
| 133 |
|
|
|
|
| 127 |
## Training Data
|
| 128 |
|
| 129 |
|
| 130 |
+
- SQL database transaction logs (1,611 samples) synthetically generated by ChatGPT, Qwen, DeepSeek, Grok, Gemini, and Claude
|
| 131 |
|
| 132 |
- Each log labeled as either `Normal` or `Anomaly`
|
| 133 |
|