AdTargetingBERTClassifier is a small-scale BERT-based classifier designed for the task of ad targeting classification. The model is trained to predict multi-class labels associated with domains, as provided in the DAC693K dataset.

The classifier is built on the BERT (Bidirectional Encoder Representations from Transformers) architecture. It takes domain text as input and outputs logits for each class, enabling multi-class classification for ad targeting.

The model is trained on the "AdTargetingDataset" using a supervised learning approach. The training involves optimizing for the categorical cross-entropy loss, and the model is fine-tuned on the specific ad targeting classes associated with each domain.

To use the trained classifier in your Python environment, you can load it using the following code:

To make predictions with the loaded model, you can use the obtained logits. Convert the logits to probabilities and determine the predicted class based on the highest probability.

The model's performance can be assessed using standard evaluation metrics such as accuracy, precision, recall, and F1-score on a separate validation set or through cross-validation.

This model is released under the Apache 2.0 License.

If you use this model in your work, please cite it using the following BibTeX entry:

We would like to thank the developers of the Hugging Face Transformers library for providing the BERT model implementation.