---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: bert_imdb
  results: []
datasets:
- stanfordnlp/imdb
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert_imdb

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3119
- Accuracy: 0.9403
- Recall: 0.9430
- Precision: 0.9379

To acccess my finetuning tutorial you can check the following [repository](https://github.com/GoktugGuvercin/Text-Classification).

## Comparison with SOTA:

- DistilBERT 66M: 92.82
- BERT-base + ITPT: 95.63
- BERT-large: 95.49

Reference: [Paperswithcode](https://paperswithcode.com/sota/sentiment-analysis-on-imdb)

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| 0.2099        | 1.0   | 1563 | 0.2456          | 0.9102   | 0.8481 | 0.9683    |
| 0.1379        | 2.0   | 3126 | 0.2443          | 0.9274   | 0.8911 | 0.9608    |
| 0.0752        | 3.0   | 4689 | 0.2845          | 0.9391   | 0.9509 | 0.9290    |
| 0.0352        | 4.0   | 6252 | 0.3119          | 0.9403   | 0.9430 | 0.9379    |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0