---
license: mit
tags:
- generated_from_trainer
datasets:
- stereoset
metrics:
- accuracy
model-index:
- name: xlnet-base-cased_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: 0.7441130298273155
---

<!-- 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. -->

# xlnet-base-cased_stereoset_finetuned

This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0332
- Accuracy: 0.7441

## 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    | 0.7165          | 0.5055   |
| No log        | 0.42  | 10   | 0.6932          | 0.5      |
| No log        | 0.62  | 15   | 0.6971          | 0.5047   |
| No log        | 0.83  | 20   | 0.7107          | 0.4953   |
| No log        | 1.04  | 25   | 0.6895          | 0.5047   |
| No log        | 1.25  | 30   | 0.6715          | 0.5840   |
| No log        | 1.46  | 35   | 0.6476          | 0.6476   |
| No log        | 1.67  | 40   | 0.6150          | 0.6970   |
| No log        | 1.88  | 45   | 0.6170          | 0.6884   |
| No log        | 2.08  | 50   | 0.6065          | 0.6797   |
| No log        | 2.29  | 55   | 0.5865          | 0.7033   |
| No log        | 2.5   | 60   | 0.5899          | 0.7064   |
| No log        | 2.71  | 65   | 0.5980          | 0.7151   |
| No log        | 2.92  | 70   | 0.5890          | 0.7229   |
| No log        | 3.12  | 75   | 0.5930          | 0.7190   |
| No log        | 3.33  | 80   | 0.6430          | 0.7049   |
| No log        | 3.54  | 85   | 0.6677          | 0.7198   |
| No log        | 3.75  | 90   | 0.6076          | 0.7370   |
| No log        | 3.96  | 95   | 0.6041          | 0.7339   |
| No log        | 4.17  | 100  | 0.6324          | 0.7323   |
| No log        | 4.38  | 105  | 0.6990          | 0.7308   |
| No log        | 4.58  | 110  | 0.7081          | 0.7433   |
| No log        | 4.79  | 115  | 0.6549          | 0.7237   |
| No log        | 5.0   | 120  | 0.6868          | 0.7072   |
| No log        | 5.21  | 125  | 0.6525          | 0.7363   |
| No log        | 5.42  | 130  | 0.7622          | 0.7418   |
| No log        | 5.62  | 135  | 0.7730          | 0.7402   |
| No log        | 5.83  | 140  | 0.7788          | 0.7449   |
| No log        | 6.04  | 145  | 0.7609          | 0.7347   |
| No log        | 6.25  | 150  | 0.8058          | 0.7323   |
| No log        | 6.46  | 155  | 0.8525          | 0.7331   |
| No log        | 6.67  | 160  | 0.8504          | 0.7339   |
| No log        | 6.88  | 165  | 0.8424          | 0.7300   |
| No log        | 7.08  | 170  | 0.8413          | 0.7394   |
| No log        | 7.29  | 175  | 0.8808          | 0.7268   |
| No log        | 7.5   | 180  | 0.9058          | 0.7292   |
| No log        | 7.71  | 185  | 0.9338          | 0.7363   |
| No log        | 7.92  | 190  | 0.9412          | 0.7370   |
| No log        | 8.12  | 195  | 0.9453          | 0.7339   |
| No log        | 8.33  | 200  | 0.9544          | 0.7394   |
| No log        | 8.54  | 205  | 0.9664          | 0.7402   |
| No log        | 8.75  | 210  | 0.9840          | 0.7339   |
| No log        | 8.96  | 215  | 0.9896          | 0.7370   |
| No log        | 9.17  | 220  | 1.0239          | 0.7410   |
| No log        | 9.38  | 225  | 1.0306          | 0.7418   |
| No log        | 9.58  | 230  | 1.0358          | 0.7402   |
| No log        | 9.79  | 235  | 1.0351          | 0.7410   |
| No log        | 10.0  | 240  | 1.0332          | 0.7441   |


### Framework versions

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