transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:rotten_tomatoes_movie_review license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- rotten_tomatoes_movie_review
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-rotten-tomatoes-epochs-5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: rotten_tomatoes_movie_review
      type: rotten_tomatoes_movie_review
      args: default
    metrics:
    - type: accuracy
      value: 0.975609756097561
      name: Accuracy
    - type: f1
      value: 0.9756096702430234
      name: F1
---

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# bert-base-cased-finetuned-rotten-tomatoes-epochs-5

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the rotten_tomatoes_movie_review dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1022
- Accuracy: 0.9756
- F1: 0.9756

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.595         | 1.0   | 34   | 0.3926          | 0.8780   | 0.8780 |
| 0.3767        | 2.0   | 68   | 0.2374          | 0.9390   | 0.9390 |
| 0.273         | 3.0   | 102  | 0.1522          | 0.9615   | 0.9615 |
| 0.1597        | 4.0   | 136  | 0.1154          | 0.9719   | 0.9719 |
| 0.1348        | 5.0   | 170  | 0.1022          | 0.9756   | 0.9756 |


### Framework versions

- Transformers 4.16.2
- Pytorch 2.1.0+cu121
- Datasets 1.16.1
- Tokenizers 0.15.0
