---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
base_model: bert-base-cased
model-index:
- name: bert-finetuned-rottentomatoes
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: rotten_tomatoes
      type: rotten_tomatoes
      config: default
      split: validation
      args: default
    metrics:
    - type: accuracy
      value: 0.8442776735459663
      name: Accuracy
---

<!-- 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. -->

# bert-finetuned-rottentomatoes

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9971
- Accuracy: 0.8443

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1626        | 1.0   | 1067 | 0.8012          | 0.8340   |
| 0.1048        | 2.0   | 2134 | 0.9137          | 0.8405   |
| 0.0472        | 3.0   | 3201 | 0.9971          | 0.8443   |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2