---
language: en
license: apache-2.0
datasets:
  - amazon_reviews_multi
model-index:
  - name: distilbert-base-uncased-finetuned-amazon-reviews
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          type: amazon-reviews-multi
          name: amazon_reviews_multi
          split: test
        metrics:
          - type: accuracy
            value: 0.8558
            name: Accuracy top2
          - type: loss
            value: 1.2339
            name: Loss

tags:
- generated_from_keras_callback

pipeline_tag: text-classification
---


# Model Card for distilbert-base-uncased-finetuned-amazon-reviews


#  Table of Contents

- [Model Card for distilbert-base-uncased-finetuned-amazon-reviews](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Uses](#uses)
- [Fine-tuning hyperparameters](#training-details)
- [Evaluation](#evaluation)
- [Framework versions](#framework-versions)


# Model Details

## Model Description

<!-- Provide a longer summary of what this model is/does. -->
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi) dataset.
This model reaches an accuracy of xxx on the dev set.

- **Model type:** Language model
- **Language(s) (NLP):** en
- **License:** apache-2.0
- **Parent Model:** For more details about DistilBERT, check out [this model card](https://huggingface.co/distilbert-base-uncased).
- **Resources for more information:**
    - [Model Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification)


# Uses

You can use this model directly with a pipeline for text classification.

```
from transformers import pipeline

checkpoint = "amir7d0/distilbert-base-uncased-finetuned-amazon-reviews"
classifier = pipeline("text-classification", model=checkpoint)
classifier(["Replace me by any text you'd like."])
```
and in TensorFlow:
```
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification

checkpoint = "amir7d0/distilbert-base-uncased-finetuned-amazon-reviews"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)

text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```


# Training Details

## Training and Evaluation Data

Here is the raw dataset ([amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi)) we used for finetuning the model.
The dataset contains 200,000, 5,000, and 5,000 reviews in the training, dev, and test sets respectively.

## Fine-tuning 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: 5

## Accuracy

The fine-tuned model was evaluated on the test set of `amazon_reviews_multi`.
- Accuracy (exact) is the exact match of the number of stars.
- Accuracy (off-by-1) is the percentage of reviews where the number of stars the model predicts differs by a maximum of 1 from the number given by the human reviewer. 

| Split | Accuracy (exact) | Accuracy (off-by-1) |
| -------- | ---------------------- | ------------------- |
| Dev set  | 56.96%                 | 85.50%
| Test set | 57.36%                 | 85.58%



# Framework versions

- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.1.0
- Tokenizers 0.13.2