rahulm-selector
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log-classifier-BERT-v1

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 ---
+language: en
+library_name: LogClassifier
+tags:
+- log-classification
+- log feature
+- log-similarity
+- transformers
+- AIOps
+pipeline_tag: text-classification
 ---
+# log-classifier-BERT-v1
+log-classifier-v1 is a neural network-based log classification model, trained from BERTForSequenceClassification designed for use in network and device log mining tasks.
+Developed by [Selector AI](https://www.selector.ai/)
+## Model Usage (HuggingFace Transformers)
+```python
+from transformers import BertForSequenceClassification, BertTokenizer
+# Load the model and tokenizer from Hugging Face
+model = BertForSequenceClassification.from_pretrained("rahulm-selector/log-classifier-BERT-v1")
+tokenizer = BertTokenizer.from_pretrained("rahulm-selector/log-classifier-BERT-v1")
+import torch
+model.eval()
+# Step 2: Prepare the input data (Example log text)
+log_text = "Error occurred while accessing the database."
+# Tokenize the input data
+inputs = tokenizer(log_text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+# Step 3: Make predictions
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+# Step 4: Get the predicted class (the class with the highest score)
+predicted_class = torch.argmax(logits, dim=1).item()
+# Example label mapping (you can load this from a JSON file or config)
+label_mapping = model.config.id2label
+# Get the event name from the predicted class
+predicted_event = label_mapping[predicted_class]
+print(f"Predicted Event: {predicted_event}")
+```
+## Background
+The model focuses on structured and semi-structured log data, outputing around 60 different event categories. It is highly effective
+for real-time log analysis, anomaly detection, and operational monitoring, helping organizations manage
+large-scale network data by automatically classifying logs into predefined categories, facilitating faster
+and more accurate diagnosis of network issues. The log-classifier-BERT-v1 model is designed to process logs as
+input and output a corresponding classification.
+## Intended uses
+Our model is intended to be used as classifier. Given an input text (a log coming from a network/device), it outputs the corresponding event most associated with the log.
+The possible events that can be classified are shown in [encoder.json](https://huggingface.co/rahulm-selector/log-classifier-v1/blob/main/encoder.json)
 ## Training Details
+### Data
+The model was trained on log data sourced from various network and infrastructure devices,
+capturing crucial system events and performance metrics. Syslogs originated from network routers,
+switches, firewalls, and servers, providing a rich dataset of operational insights including security events,
+traffic patterns, and hardware health statuses.
+### Train/Test Split
+- **Train Data Size**: `~80K Logs`
+- **Test Data Size**: `~20K Logs`
+#### Hyper Parameters
+The following hyperparameters were used during training to optimize the model's performance:
+- **Batch Size**: `32`
+- **Learning Rate**: `.001`
+- **Optimizer**: `Adam`
+- **Epochs**: `10`
+- **Dropout Rate**: N/A
+- **LSTM Hidden Dimension**: `384`
+- **Embedding Dimension**: `384`