---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
datasets:
- stanfordnlp/imdb
language:
- en
library_name: transformers
model-index:
- name: movie-review-classifier
  results:
  - task:
      type: text-classification             # Required. Example: automatic-speech-recognition
    dataset:
      type: standfordnlp/imdb               # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: IMDB Movie Reviews              # Required. A pretty name for the dataset. Example: Common Voice (French)
    metrics:
      - type: f1                            # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.9327                       # Required. Example: 20.90
---

# movie-review-classifier

This model classifies (text) movie reviews as either a 1 (*i.e.,* thumbs-up) or a 0 (*i.e.,* a thumbs-down).

## Model description

This model is a version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) that was fine-tuned on the [IMDB movie-review dataset](https://huggingface.co/datasets/stanfordnlp/imdb).
It achieves the following results on the evaluation set:
- Loss: 0.2743
- F1: 0.9327

## Intended uses & limitations

Training this model was completed as part of a project from a data science bootcamp. It is intended to be used perhaps by students and/or hobbyists.

## Training and evaluation data

This model was trained on the [IMDB movie-review dataset](https://huggingface.co/datasets/stanfordnlp/imdb), a set of highly polarized (*i.e.,* clearly positive or negative) movie reviews. The dataset contains 25k labelled train samples, 25k labelled test samples, and 50k unlabelled samples. 

## 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: 3
- weight_decay: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2258        | 1.0   | 1563 | 0.2161          | 0.9122 |
| 0.1486        | 2.0   | 3126 | 0.2291          | 0.9306 |
| 0.0916        | 3.0   | 4689 | 0.2743          | 0.9327 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1