---
license: mit
tags:
- generated_from_trainer
datasets:
- stereoset
metrics:
- accuracy
model-index:
- name: roberta-large_stereoset_finetuned
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: stereoset
      type: stereoset
      config: intersentence
      split: validation
      args: intersentence
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8335949764521193
---

<!-- 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. -->

# roberta-large_stereoset_finetuned

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7989
- Accuracy: 0.8336

## 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    | 0.6920          | 0.5196   |
| No log        | 0.42  | 10   | 0.6909          | 0.5290   |
| No log        | 0.62  | 15   | 0.6899          | 0.5220   |
| No log        | 0.83  | 20   | 0.6883          | 0.5408   |
| No log        | 1.04  | 25   | 0.6573          | 0.6609   |
| No log        | 1.25  | 30   | 0.5892          | 0.7088   |
| No log        | 1.46  | 35   | 0.6633          | 0.5408   |
| No log        | 1.67  | 40   | 0.6322          | 0.6852   |
| No log        | 1.88  | 45   | 0.6393          | 0.7159   |
| No log        | 2.08  | 50   | 0.5494          | 0.7410   |
| No log        | 2.29  | 55   | 0.5498          | 0.7386   |
| No log        | 2.5   | 60   | 0.5069          | 0.7692   |
| No log        | 2.71  | 65   | 0.4930          | 0.7630   |
| No log        | 2.92  | 70   | 0.4939          | 0.7614   |
| No log        | 3.12  | 75   | 0.5379          | 0.7724   |
| No log        | 3.33  | 80   | 0.5981          | 0.7732   |
| No log        | 3.54  | 85   | 0.5842          | 0.7716   |
| No log        | 3.75  | 90   | 0.4405          | 0.8030   |
| No log        | 3.96  | 95   | 0.4970          | 0.7951   |
| No log        | 4.17  | 100  | 0.5172          | 0.8093   |
| No log        | 4.38  | 105  | 0.5052          | 0.8108   |
| No log        | 4.58  | 110  | 0.4685          | 0.8085   |
| No log        | 4.79  | 115  | 0.4663          | 0.8218   |
| No log        | 5.0   | 120  | 0.5086          | 0.8218   |
| No log        | 5.21  | 125  | 0.5096          | 0.8179   |
| No log        | 5.42  | 130  | 0.5705          | 0.8203   |
| No log        | 5.62  | 135  | 0.5294          | 0.8312   |
| No log        | 5.83  | 140  | 0.4377          | 0.8375   |
| No log        | 6.04  | 145  | 0.5699          | 0.8100   |
| No log        | 6.25  | 150  | 0.6062          | 0.8265   |
| No log        | 6.46  | 155  | 0.7237          | 0.8218   |
| No log        | 6.67  | 160  | 0.6816          | 0.8210   |
| No log        | 6.88  | 165  | 0.6413          | 0.8124   |
| No log        | 7.08  | 170  | 0.5931          | 0.8359   |
| No log        | 7.29  | 175  | 0.6149          | 0.8399   |
| No log        | 7.5   | 180  | 0.7190          | 0.8195   |
| No log        | 7.71  | 185  | 0.7339          | 0.8352   |
| No log        | 7.92  | 190  | 0.7244          | 0.8352   |
| No log        | 8.12  | 195  | 0.7722          | 0.8203   |
| No log        | 8.33  | 200  | 0.6890          | 0.8344   |
| No log        | 8.54  | 205  | 0.6938          | 0.8336   |
| No log        | 8.75  | 210  | 0.7234          | 0.8320   |
| No log        | 8.96  | 215  | 0.7517          | 0.8391   |
| No log        | 9.17  | 220  | 0.7713          | 0.8383   |
| No log        | 9.38  | 225  | 0.7745          | 0.8375   |
| No log        | 9.58  | 230  | 0.8006          | 0.8375   |
| No log        | 9.79  | 235  | 0.8003          | 0.8367   |
| No log        | 10.0  | 240  | 0.7989          | 0.8336   |


### Framework versions

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