Update README.md

README.md

@@ -83,4 +83,47 @@ classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
 # Example usage
 product_description = "High-resolution digital camera with 20MP sensor."
 result = classifier(product_description)
-print(result)
+print(result)
+
+#Training Details
+##Training Data
+The model was trained on an e-commerce dataset that includes various product categories such as CPUs, Digital Cameras, Dishwashers, Fridge Freezers, Microwaves, Mobile Phones, TVs, and Washing Machines. The data was preprocessed by removing duplicates, lowercasing, and tokenizing text.
+
+##Training Procedure
+ 1. Preprocessing: Text data was cleaned, lowercased, and tokenized. Product descriptions were truncated to 128 tokens for uniformity.
+ 2. Hyperparameters: Fine-tuning was conducted with a learning rate of 2e-5 and batch size of 16 for 3 epochs.
+ 3. Training Hardware: The model was trained on a single NVIDIA Tesla V100 GPU for approximately 3 hours.
+
+##Evaluation
+Testing Data, Factors & Metrics
+The model was evaluated on a separate test set of product descriptions, using precision, recall, and F1-score as the evaluation metrics.
+
+##Summary
+The model achieved an overall accuracy of 96.16%, with strong performance across multiple product categories. The F1-scores indicate that the model performs particularly well in the "CPUs" and "Digital Cameras" categories.
+
+##Technical Specifications
+Model Architecture and Objective
+The DistilBERT-ProductClassifier model utilizes the DistilBERT architecture, fine-tuned with a text classification head for e-commerce product categorization tasks.
+
+##Compute Infrastructure
+The model is optimized to run efficiently on CPUs and small GPUs, making it suitable for real-time applications.
+
+##Hardware
+This model requires a minimum of 4GB of RAM for efficient inference, and a modern CPU or basic GPU is recommended.
+
+##Software
+Transformers library: v4.26.0
+Python version: 3.8 or higher
+CUDA [optional]: 10.2 or higher (if running on GPU)
+
+##Citation
+If you use this model, please cite as follows:
+
+```
+@misc{distilbert_product_classifier,
+  author = {Adnan AI Labs},
+  title = {DistilBERT-ProductClassifier for E-commerce},
+  year = {2024},
+  url = {https://huggingface.co/Adnan-AI-Labs/DistilBERT-ProductClassifier}
+}
+