Update README.md

#Model Card for DistilBertForSequenceClassification_ProductClassifier

##Model Description

Purpose: This model is a product classifier, specifically designed to categorize raw product titles into predefined categories. It's optimized for use with price comparison websites where product categorization can vary greatly across different sources. By accurately categorizing titles from scraped data, the model aims to improve data consistency for these websites.

Model Architecture: Built upon the DistilBERT architecture, this model offers a lightweight and efficient alternative to the original BERT model while maintaining a high level of performance. It utilizes 6 attention heads and a hidden dimension size of 768, making it suitable for deployment on edge devices or resource-constrained systems.

##Training Data:

The model was fine-tuned using a dataset sourced from Kaggle's Product Classification and Categorization challenge. The data underwent preprocessing steps including:

###Cleaning
Balancing class distribution
Segmentation into manageable chunks
Product titles were tokenized, and categorical labels were mapped to integer values for training purposes.

###Intended Use

Intended Users: This model is targeted primarily towards developers and data scientists working with e-commerce or price comparison websites. It provides significant value to those who need to standardize product categorization from diverse and inconsistent data sources.

###Use Cases:

Categorizing product listings on price comparison websites to enhance user experience by facilitating easier discovery of relevant items.
Supporting product search and filtering functionalities by providing clean and consistent category data.
Enabling other downstream tasks, such as recommendation engines, to leverage well-defined product categories for improved performance.
Limitations

##Known Limitations:

The model might exhibit reduced accuracy when encountering product titles containing rare or highly specialized terms outside the training data distribution.
It has the potential to inherit biases present in product category naming conventions within the training data. This may lead to struggles with generalizing across different naming styles without further fine-tuning.
Hardware

Training Platform: The model was trained within an Intel Developer Cloud environment utilizing scalable Intel® Xeon® 4th Gen Scalable processors to optimize for speed and resource efficiency.

##Software Optimizations

Known Optimizations: Training incorporated techniques like gradient accumulation and mixed-precision to reduce memory footprint and enhance performance. The AdamW optimizer was employed for effective learning rate adjustments throughout the fine-tuning process.

##Ethical Considerations

Ethical Concerns: As common with all classification models, there is a potential risk of inherent biases originating from the training data, impacting classification accuracy or fairness. Users are encouraged to validate the model for their specific use case and consider any necessary post-processing steps or bias mitigation strategies.

###More Information

For further details on the DistilBERT architecture and practical implementation, refer to the DistilBERT documentation on the Hugging Face hub.

README.md

@@ -1,3 +1,12 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- distilbert/distilbert-base-uncased
+- lxs1/DistilBertForSequenceClassification_6h_768dim
+pipeline_tag: text-classification
+tags:
+- ecommerce
+- classification
+---