Certainly! Here is a concise model card for the incident impact classification model:

Incident Impact Classification Model

Model Description

This model is a fine-tuned version of BERT (bert-base-uncased) designed to classify the impact of incident records based on their short descriptions. The impact is categorized into three levels: low, medium, and high.

Intended Use

Only for Demo pupose - The model is intended to assist in the automatic categorization of incident impacts to streamline incident management processes. It can be used by IT service management teams to quickly identify the severity of incidents based on short descriptions.

How to Use

Inference

To use the model for inference, you can utilize the Hugging Face transformers library. Below is an example of how to load the model and make predictions:

from transformers import pipeline

model_name = "xeroISB/incidentImpactModel"
classifier = pipeline("text-classification", model=model_name)

short_description = "Network outage in building 12"
prediction = classifier(short_description)
print(prediction)

Training

The model was trained using the following configuration:

Model: BERT (bert-base-uncased)
Learning Rate: 2e-5
Batch Size: 16
Epochs: 3
Evaluation Strategy: Epoch
Optimizer: AdamW
Loss Function: Cross-Entropy Loss

Dataset

The dataset used for training includes the following columns:

short_description: A brief description of the incident.
impact: The impact level categorized into three classes: low (3), medium (2), and high (1).

The impact values were mapped to integer labels as follows:

3 (low) -> 0
2 (medium) -> 1
1 (high) -> 2

Tokenization

The short_description was tokenized using the BERT tokenizer with padding to the maximum length and truncation enabled.

Performance

Confusion Matrix

The confusion matrix on the validation set is as follows:

	Predicted Low	Predicted Medium
Low	414	194
Medium	463	220
High	33	13

Classification Report

              Precision - 47%
              Accuracy  - 47%
              Recall    - 47%
              F1 - 45%

Limitations

The model's performance is dependent on the quality and representativeness of the training data.
It may not perform well on unseen incident descriptions that are significantly different from the training data.
The model's predictions are limited to the context of the provided short descriptions and do not take into account other contextual information.

Ethical Considerations

Ensure the model is used in an ethical manner, considering the potential impact of misclassifications on incident management and prioritization.
Regularly monitor the model's performance and update it with new data to maintain accuracy and reliability.

License

This model is released under the Apache 2.0 License.

Contact Information

For questions or issues, please contact Tushar Mishra.