Update

README.md

@@ -33,11 +33,27 @@ process to generate inputs and labels from those texts using the BERT base model
 DistilBERT is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
 to make decisions, such as sequence classification, token classification, or question answering. This fine-tuned version of DistilBERT is used to predict the relevance between one query and one product description. It also can be used to rerank the relevance order of products given one query for the amazon platform or other e-commerce platforms.
 
+The limitations are this trained model is focusing on queries and products on Amazon. If you apply this model to other domains, it may perform poorly.
+
 ## How to use 
 
+You can use this model directly by downloading the trained weights and configurations like the below code snippet:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("LiYuan/amazon-query-product-ranking")
+
+model = AutoModelForSequenceClassification.from_pretrained("LiYuan/amazon-query-product-ranking")
+```
+
 ## Training and evaluation data
 
-More information needed
+Download all the raw [dataset](https://www.aicrowd.com/challenges/esci-challenge-for-improving-product-search/dataset_files) from the Amazon KDD Cup website.
+
+The three dataset we used like below:
+
+
 
 ## Training procedure