---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: base-vanilla-target-imdb
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: imdb
      type: imdb
      config: plain_text
      split: train
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9604
    - name: F1
      type: f1
      value: 0.9798000408079984
---

<!-- 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. -->

# base-vanilla-target-imdb

This model is a fine-tuned version of [google/bert_uncased_L-12_H-768_A-12](https://huggingface.co/google/bert_uncased_L-12_H-768_A-12) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1564
- Accuracy: 0.9604
- F1: 0.9798

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2549        | 0.64  | 500  | 0.2049          | 0.9234   | 0.9602 |
| 0.1771        | 1.28  | 1000 | 0.0750          | 0.9763   | 0.9880 |
| 0.1271        | 1.92  | 1500 | 0.0871          | 0.9707   | 0.9851 |
| 0.0755        | 2.56  | 2000 | 0.3361          | 0.9235   | 0.9602 |
| 0.0631        | 3.2   | 2500 | 0.1564          | 0.9604   | 0.9798 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.2