readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-mnli-amazon-query-shopping
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # distilbert-base-uncased-finetuned-mnli-amazon-query-shopping
  
  This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment?text=I+like+you.+I+love+you) on an [Amazon US Customer Reviews Dataset](https://www.kaggle.com/datasets/cynthiarempel/amazon-us-customer-reviews-dataset). The code for the fine-tuning process can be found
  [here](https://github.com/vanderbilt-data-science/bigdata/blob/main/06-fine-tune-BERT-on-our-dataset.ipynb). This model is uncased: it does
  not make a difference between english and English.
  It achieves the following results on the evaluation set:
  - Loss: 0.5202942490577698
  - Accuracy: 0.8
  
  ## Model description
  
  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
  
  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 reviews 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-review-sentiment-analysis")
  
  model = AutoModelForSequenceClassification.from_pretrained("LiYuan/amazon-review-sentiment-analysis")
  ```
  
  ## Training and evaluation data
  
  Download all the raw [dataset](https://www.kaggle.com/datasets/cynthiarempel/amazon-us-customer-reviews-dataset) from the Kaggle website.
  
  
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 2
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.555400        | 1.0   | 1080 | 0.520294          | 0.800000   |
  | 0.424300        | 2.0   | 1080 | 0.549649          | 0.798380   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1