Adnan-AI-Labs
/

DistilBERT-ProductClassifier

@@ -86,38 +86,38 @@ result = classifier(product_description)
 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:

 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: