Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language: en
+library_name: LogClassifier
+tags:
+- log-classification
+- log feature
+- log-similarity
+- transformers
+- AIOps
+pipeline_tag: text-classification
+---
+
+
+# s2-log-classifier-BERT-v1
+This model is a transformers classification model, trained using BERTForSequenceClassification designed for use in network and device log mining tasks. 
+Developed by [Selector AI](https://www.selector.ai/) 
+
+## Model Usage
+```python
+from transformers import BertForSequenceClassification, BertTokenizer
+
+# Step 1: 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()
+
+# label mapping (can load from JSON file in repo or config)
+label_mapping = model.config.id2label
+
+# Step 5: Get the event name
+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.
+
+## Intended uses
+
+Our model is intended to be used as classifier. Given an input text (a log coming from a network/device/router), it outputs a corresponding event most associated with the log.
+The possible events that can be classified are shown in [encoder-main.json](https://huggingface.co/rahulm-selector/log-classifier-BERT-v1/blob/main/encoder-main.json)
+
+
+## Training Details
+
+### Data
+
+The model was trained on a variety of network events and system logs, focusing on monitoring and analyzing state changes,
+protocol behaviors, and hardware interactions across infrastructure components. This included tracking routing issues, 
+protocol neighbor state changes, link stability, and security events, ensuring that the model could recognize and 
+classify critical patterns in device communications, network health, and configuration activities.
+
+### 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`
+
+## Credits
+
+This project was developed by a collaborative team at [Selector AI](https://www.selector.ai/). Below are the key contributors:
+
+### Authors
+- **Rahul Muthuswamy**  
+  Role: Project Lead and Model Developer
+  Email: [rahulm@selector.ai]  
+
+- **Alex Lau**  
+  Role: Mentor
+  Email: [alexlau@selector.ai]  
+
+- **Sebastian Reyes**  
+  Role: Mentor
+  Email: [seb@selector.ai]  
+
+- **Surya Nimmagadda**  
+  Role: Mentor
+  Email: [nscsekhar@selector.ai]