Update

README.md

@@ -23,17 +23,18 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
-self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
-with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
-process to generate inputs and labels from those texts using the BERT base model. We replaced its head with our shopping relevance category to fine-tune it on 571,223 rows of training set while validate it on 142,806 rows of dev set. Finally, we evaluated our model performance on a held-out test set: 79,337 rows.
+This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).
+
+This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above, or for further finetuning on related sentiment analysis tasks.
+
+We replaced its head with our customer reviews to fine-tune it on 17,280 rows of training set while validating it on 4,320 rows of dev set. Finally, we evaluated our model performance on a held-out test set: 2,400 rows.
 
 ## Intended uses & limitations
 
-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.
+Bert-base 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 BERT-base is used to predict review rating star given the review.
 
-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.
+The limitations are this trained model is focusing on reviews and products on Amazon. If you apply this model to other domains, it may perform poorly.
 
 ## How to use 
 
@@ -49,12 +50,8 @@ model = AutoModelForSequenceClassification.from_pretrained("LiYuan/amazon-query-
 
 ## Training and evaluation data
 
-Download all the raw [dataset](https://www.aicrowd.com/challenges/esci-challenge-for-improving-product-search/dataset_files) from the Amazon KDD Cup website.
+Download all the raw [dataset](https://www.kaggle.com/datasets/cynthiarempel/amazon-us-customer-reviews-dataset) from the Kaggle website.
 
-1. Concatenate the all product attributes from the product dataset
-2. Join it with a training query dataset
-3. Stratified Split the merged data into 571,223-row training, 142,806-row validation, 79,337-row test set
-4. Train on the full training set
 
 
 ## Training procedure
@@ -74,8 +71,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 0.8981        | 1.0   | 35702 | 0.8662          | 0.6371   |
-| 0.7837        | 2.0   | 71404 | 0.8244          | 0.6617   |
+| 0.555400        | 1.0   | 1080 | 0.520294          | 0.800000   |
+| 0.424300        | 2.0   | 1080 | 0.549649          | 0.798380   |
 
 
 ### Framework versions