---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
base_model: google/bert_uncased_L-4_H-256_A-4
model-index:
- name: finetuned-base_mini
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: imdb
      type: imdb
      config: plain_text
      split: train
      args: plain_text
    metrics:
    - type: accuracy
      value: 0.9076
      name: Accuracy
    - type: f1
      value: 0.9515621723631789
      name: F1
---

<!-- 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. -->

# finetuned-base_mini

This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3938
- Accuracy: 0.9076
- F1: 0.9516

## 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: 128
- eval_batch_size: 128
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.354         | 2.55  | 500  | 0.2300          | 0.9116   | 0.9538 |
| 0.2086        | 5.1   | 1000 | 0.3182          | 0.8815   | 0.9370 |
| 0.1401        | 7.65  | 1500 | 0.2160          | 0.9241   | 0.9605 |
| 0.0902        | 10.2  | 2000 | 0.4684          | 0.8722   | 0.9317 |
| 0.0654        | 12.76 | 2500 | 0.4885          | 0.8747   | 0.9332 |
| 0.043         | 15.31 | 3000 | 0.3938          | 0.9076   | 0.9516 |


### Framework versions

- Transformers 4.25.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2