---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-imdb
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: imdb
      type: imdb
      config: plain_text
      split: test
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value:
        accuracy: 0.93148
    - name: F1
      type: f1
      value:
        f1: 0.9314719475700824
---

<!-- 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-imdb

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2799
- Accuracy: {'accuracy': 0.93148}
- F1: {'f1': 0.9314719475700824}

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy              | F1                         |
|:-------------:|:-----:|:----:|:---------------:|:---------------------:|:--------------------------:|
| 0.2376        | 1.0   | 1563 | 0.2966          | {'accuracy': 0.8966}  | {'f1': 0.8959598583205258} |
| 0.1671        | 2.0   | 3126 | 0.2331          | {'accuracy': 0.92996} | {'f1': 0.9299430382567873} |
| 0.0993        | 3.0   | 4689 | 0.2799          | {'accuracy': 0.93148} | {'f1': 0.9314719475700824} |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1