metadata

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

Model Card for distilbert-base-uncased-finetuned-amazon-reviews
Table of Contents
Model Details
Uses
Fine-tuning hyperparameters
Evaluation
Framework versions

Model Details

Model Description

This model is a fine-tuned version of distilbert-base-uncased on 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.
Resources for more information:
- Model Documentation

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) 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