---
license: mit
tags:
- generated_from_trainer
datasets:
- crows_pairs
metrics:
- accuracy
model-index:
- name: xlnet-base-cased_crows_pairs_finetuned
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: crows_pairs
      type: crows_pairs
      config: crows_pairs
      split: test
      args: crows_pairs
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5066225165562914
---

<!-- 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_crows_pairs_finetuned

This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the crows_pairs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- Accuracy: 0.5066

## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9006        | 0.53  | 10   | 0.7493          | 0.4934   |
| 0.7565        | 1.05  | 20   | 0.7202          | 0.4934   |
| 0.7303        | 1.58  | 30   | 0.6968          | 0.4934   |
| 0.7495        | 2.11  | 40   | 0.7210          | 0.5066   |
| 0.8008        | 2.63  | 50   | 0.6944          | 0.5066   |
| 0.7251        | 3.16  | 60   | 0.6982          | 0.5066   |
| 0.7193        | 3.68  | 70   | 0.7032          | 0.5066   |
| 0.7118        | 4.21  | 80   | 0.6975          | 0.5066   |
| 0.7419        | 4.74  | 90   | 0.7311          | 0.5066   |
| 0.7175        | 5.26  | 100  | 0.6946          | 0.5066   |
| 0.7293        | 5.79  | 110  | 0.7008          | 0.4934   |
| 0.7208        | 6.32  | 120  | 0.6940          | 0.4934   |
| 0.7101        | 6.84  | 130  | 0.6975          | 0.5066   |
| 0.7138        | 7.37  | 140  | 0.7065          | 0.4934   |
| 0.7112        | 7.89  | 150  | 0.6931          | 0.5066   |
| 0.7093        | 8.42  | 160  | 0.6931          | 0.5066   |
| 0.6996        | 8.95  | 170  | 0.6931          | 0.5066   |
| 0.6948        | 9.47  | 180  | 0.7050          | 0.4934   |
| 0.7118        | 10.0  | 190  | 0.6935          | 0.4934   |
| 0.7015        | 10.53 | 200  | 0.6993          | 0.5066   |
| 0.6985        | 11.05 | 210  | 0.6941          | 0.4934   |
| 0.6983        | 11.58 | 220  | 0.7118          | 0.4934   |
| 0.7031        | 12.11 | 230  | 0.7110          | 0.5066   |
| 0.6987        | 12.63 | 240  | 0.7643          | 0.4934   |
| 0.7483        | 13.16 | 250  | 0.7019          | 0.5066   |
| 0.7065        | 13.68 | 260  | 0.7018          | 0.4934   |
| 0.7008        | 14.21 | 270  | 0.6931          | 0.5066   |
| 0.7074        | 14.74 | 280  | 0.6932          | 0.4934   |
| 0.7097        | 15.26 | 290  | 0.6931          | 0.5066   |
| 0.7284        | 15.79 | 300  | 0.6956          | 0.4934   |
| 0.7045        | 16.32 | 310  | 0.6948          | 0.5066   |
| 0.7041        | 16.84 | 320  | 0.7176          | 0.4934   |
| 0.7118        | 17.37 | 330  | 0.6941          | 0.5066   |
| 0.7044        | 17.89 | 340  | 0.6931          | 0.5066   |
| 0.7034        | 18.42 | 350  | 0.6938          | 0.4934   |
| 0.683         | 18.95 | 360  | 0.6984          | 0.4934   |
| 0.7024        | 19.47 | 370  | 0.7009          | 0.4934   |
| 0.6988        | 20.0  | 380  | 0.6999          | 0.5066   |
| 0.6977        | 20.53 | 390  | 0.6974          | 0.4934   |
| 0.709         | 21.05 | 400  | 0.6932          | 0.5066   |
| 0.6991        | 21.58 | 410  | 0.6940          | 0.4934   |
| 0.7058        | 22.11 | 420  | 0.6931          | 0.5066   |
| 0.7101        | 22.63 | 430  | 0.6934          | 0.4934   |
| 0.7086        | 23.16 | 440  | 0.6956          | 0.4934   |
| 0.6973        | 23.68 | 450  | 0.6970          | 0.5066   |
| 0.7059        | 24.21 | 460  | 0.6931          | 0.5066   |
| 0.7021        | 24.74 | 470  | 0.6988          | 0.4934   |
| 0.6996        | 25.26 | 480  | 0.7006          | 0.4934   |
| 0.6963        | 25.79 | 490  | 0.6931          | 0.5066   |
| 0.6962        | 26.32 | 500  | 0.6932          | 0.5066   |
| 0.691         | 26.84 | 510  | 0.6944          | 0.4934   |
| 0.7003        | 27.37 | 520  | 0.6933          | 0.4934   |
| 0.6944        | 27.89 | 530  | 0.6934          | 0.4934   |
| 0.6988        | 28.42 | 540  | 0.6931          | 0.5066   |
| 0.7009        | 28.95 | 550  | 0.6931          | 0.5066   |
| 0.699         | 29.47 | 560  | 0.6933          | 0.5066   |
| 0.696         | 30.0  | 570  | 0.6932          | 0.5066   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2