Update README.md

README.md

@@ -1,3 +1,78 @@
 ---
 license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- advertising
 ---
+
+# Tiny Bert Domain Advertising Classifier
+
+## Overview
+
+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.
+
+## Model Architecture
+
+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.
+
+## Model Training
+
+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.
+
+## Usage
+
+### Loading the Model
+
+To use the trained classifier in your Python environment, you can load it using the following code:
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# Load the pre-trained model and tokenizer
+model = BertForSequenceClassification.from_pretrained("ansi-code/bert-domain-advertising-classifier")
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+
+# Example inference
+text = "google.com"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits
+```
+
+## Prediction
+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.
+
+```python
+Copy code
+probabilities = torch.nn.functional.sigmoid(logits, dim=-1)
+predicted_class = torch.argmax(probabilities).item()
+```
+
+## Model Evaluation
+
+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.
+
+## License
+
+This model is released under the Apache 2.0 License.
+
+## Citation
+
+If you use this model in your work, please cite it using the following BibTeX entry:
+
+```bibtex
+@model{silvi_2023_bert-domain-advertising-classifier,
+  title = {bert-domain-advertising-classifier},
+  author = {Andrea Silvi},
+  year = {2023},
+}
+```
+
+## Acknowledgements
+
+We would like to thank the developers of the Hugging Face Transformers library for providing the BERT model implementation.