transformers pytorch bert text-classification generated_from_trainer dataset:rotten_tomatoes license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- rotten_tomatoes
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: test_trainer
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: rotten_tomatoes
      type: rotten_tomatoes
      config: default
      split: test
      args: default
    metrics:
    - type: accuracy
      value: 0.501
      name: Accuracy
---

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# test_trainer

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: 0.7153
- Accuracy: 0.501

## 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
- training_steps: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6412        | 0.01  | 1    | 0.7288          | 0.501    |
| 0.6171        | 0.02  | 2    | 0.7083          | 0.501    |
| 0.5805        | 0.02  | 3    | 0.7153          | 0.501    |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
