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
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README.md
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- precision
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- recall
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- f1
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---
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# Model Card for Model ID
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## How to Get Started with the Model
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Use the code below to get started with the model.
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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print(prediction) # 0 = Normal, 1 = Anomaly
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## Training Details
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- precision
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- recall
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- f1
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license: apache-2.0
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base_model:
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- google-bert/bert-base-uncased
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pipeline_tag: text-classification
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---
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# Model Card for Model ID
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## How to Get Started with the Model
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Use the code below to get started with the model.
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# ----------------------------------------------------------------------------------
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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print(prediction) # 0 = Normal, 1 = Anomaly
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# ----------------------------------------------------------------------------------
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## Training Details
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