transformers pytorch roberta text-classification generated_from_trainer dataset:stereoset license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- stereoset
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: roberta-base_stereoset_finetuned
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: stereoset
      type: stereoset
      config: intersentence
      split: validation
      args: intersentence
    metrics:
    - type: accuracy
      value: 0.7904238618524333
      name: Accuracy
---

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# roberta-base_stereoset_finetuned

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8461
- Accuracy: 0.7904

## 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: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.21  | 5    | 0.6915          | 0.5149   |
| No log        | 0.42  | 10   | 0.6945          | 0.4914   |
| No log        | 0.62  | 15   | 0.6931          | 0.4945   |
| No log        | 0.83  | 20   | 0.6814          | 0.5086   |
| No log        | 1.04  | 25   | 0.6454          | 0.6978   |
| No log        | 1.25  | 30   | 0.5807          | 0.7088   |
| No log        | 1.46  | 35   | 0.5620          | 0.7284   |
| No log        | 1.67  | 40   | 0.5410          | 0.7331   |
| No log        | 1.88  | 45   | 0.4965          | 0.7630   |
| No log        | 2.08  | 50   | 0.4924          | 0.7614   |
| No log        | 2.29  | 55   | 0.4906          | 0.7661   |
| No log        | 2.5   | 60   | 0.5141          | 0.7661   |
| No log        | 2.71  | 65   | 0.4826          | 0.7700   |
| No log        | 2.92  | 70   | 0.4977          | 0.7630   |
| No log        | 3.12  | 75   | 0.4890          | 0.7802   |
| No log        | 3.33  | 80   | 0.4819          | 0.7857   |
| No log        | 3.54  | 85   | 0.4840          | 0.7834   |
| No log        | 3.75  | 90   | 0.5189          | 0.7794   |
| No log        | 3.96  | 95   | 0.5000          | 0.7912   |
| No log        | 4.17  | 100  | 0.4958          | 0.7865   |
| No log        | 4.38  | 105  | 0.5149          | 0.7896   |
| No log        | 4.58  | 110  | 0.5515          | 0.7975   |
| No log        | 4.79  | 115  | 0.5766          | 0.7873   |
| No log        | 5.0   | 120  | 0.5867          | 0.7873   |
| No log        | 5.21  | 125  | 0.6143          | 0.7936   |
| No log        | 5.42  | 130  | 0.6226          | 0.7881   |
| No log        | 5.62  | 135  | 0.6374          | 0.7865   |
| No log        | 5.83  | 140  | 0.6405          | 0.7983   |
| No log        | 6.04  | 145  | 0.6116          | 0.8006   |
| No log        | 6.25  | 150  | 0.6372          | 0.7983   |
| No log        | 6.46  | 155  | 0.6804          | 0.7881   |
| No log        | 6.67  | 160  | 0.7237          | 0.7857   |
| No log        | 6.88  | 165  | 0.7038          | 0.7904   |
| No log        | 7.08  | 170  | 0.7100          | 0.7991   |
| No log        | 7.29  | 175  | 0.6837          | 0.7920   |
| No log        | 7.5   | 180  | 0.7203          | 0.8046   |
| No log        | 7.71  | 185  | 0.7478          | 0.7959   |
| No log        | 7.92  | 190  | 0.7667          | 0.7920   |
| No log        | 8.12  | 195  | 0.7792          | 0.7959   |
| No log        | 8.33  | 200  | 0.8014          | 0.7943   |
| No log        | 8.54  | 205  | 0.8193          | 0.7959   |
| No log        | 8.75  | 210  | 0.8316          | 0.7967   |
| No log        | 8.96  | 215  | 0.8411          | 0.7896   |
| No log        | 9.17  | 220  | 0.8652          | 0.7936   |
| No log        | 9.38  | 225  | 0.8553          | 0.7841   |
| No log        | 9.58  | 230  | 0.8458          | 0.7881   |
| No log        | 9.79  | 235  | 0.8456          | 0.7912   |
| No log        | 10.0  | 240  | 0.8461          | 0.7904   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2
