---
license: mit
tags:
- generated_from_trainer
datasets:
- crows_pairs
metrics:
- accuracy
model-index:
- name: xlnet-base-cased_crows_pairs_classifieronly
  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: 0.5397350993377483
---

<!-- 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_classifieronly

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: 0.6905
- Accuracy: 0.5397
- Tp: 0.2550
- Tn: 0.2848
- Fp: 0.2417
- Fn: 0.2185

## 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: 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp     | Tn     | Fp     | Fn     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.723         | 1.05  | 20   | 0.7244          | 0.4735   | 0.4338 | 0.0397 | 0.4868 | 0.0397 |
| 0.7134        | 2.11  | 40   | 0.7069          | 0.4702   | 0.3113 | 0.1589 | 0.3675 | 0.1623 |
| 0.717         | 3.16  | 60   | 0.7003          | 0.5099   | 0.2152 | 0.2947 | 0.2318 | 0.2583 |
| 0.7032        | 4.21  | 80   | 0.7117          | 0.4868   | 0.4272 | 0.0596 | 0.4669 | 0.0464 |
| 0.705         | 5.26  | 100  | 0.6979          | 0.4834   | 0.1987 | 0.2848 | 0.2417 | 0.2748 |
| 0.7005        | 6.32  | 120  | 0.7112          | 0.4735   | 0.4073 | 0.0662 | 0.4603 | 0.0662 |
| 0.7163        | 7.37  | 140  | 0.6978          | 0.5430   | 0.0695 | 0.4735 | 0.0530 | 0.4040 |
| 0.7038        | 8.42  | 160  | 0.6960          | 0.5099   | 0.1722 | 0.3377 | 0.1887 | 0.3013 |
| 0.6948        | 9.47  | 180  | 0.6993          | 0.4834   | 0.2947 | 0.1887 | 0.3377 | 0.1788 |
| 0.6947        | 10.53 | 200  | 0.6959          | 0.5397   | 0.2219 | 0.3179 | 0.2086 | 0.2517 |
| 0.6991        | 11.58 | 220  | 0.6938          | 0.4967   | 0.1291 | 0.3675 | 0.1589 | 0.3444 |
| 0.7027        | 12.63 | 240  | 0.6959          | 0.5199   | 0.0662 | 0.4536 | 0.0728 | 0.4073 |
| 0.6945        | 13.68 | 260  | 0.6963          | 0.5166   | 0.3278 | 0.1887 | 0.3377 | 0.1457 |
| 0.7047        | 14.74 | 280  | 0.6902          | 0.5199   | 0.1424 | 0.3775 | 0.1490 | 0.3311 |
| 0.6971        | 15.79 | 300  | 0.6929          | 0.5596   | 0.2682 | 0.2914 | 0.2351 | 0.2053 |
| 0.6979        | 16.84 | 320  | 0.6919          | 0.5364   | 0.2119 | 0.3245 | 0.2020 | 0.2616 |
| 0.6941        | 17.89 | 340  | 0.6915          | 0.5232   | 0.2020 | 0.3212 | 0.2053 | 0.2715 |
| 0.693         | 18.95 | 360  | 0.6906          | 0.5397   | 0.1987 | 0.3411 | 0.1854 | 0.2748 |
| 0.6916        | 20.0  | 380  | 0.6912          | 0.5497   | 0.1954 | 0.3543 | 0.1722 | 0.2781 |
| 0.7005        | 21.05 | 400  | 0.6903          | 0.5397   | 0.2152 | 0.3245 | 0.2020 | 0.2583 |
| 0.6933        | 22.11 | 420  | 0.6904          | 0.5298   | 0.2219 | 0.3079 | 0.2185 | 0.2517 |
| 0.6968        | 23.16 | 440  | 0.6893          | 0.5464   | 0.1821 | 0.3642 | 0.1623 | 0.2914 |
| 0.686         | 24.21 | 460  | 0.6941          | 0.5199   | 0.3377 | 0.1821 | 0.3444 | 0.1358 |
| 0.6905        | 25.26 | 480  | 0.6918          | 0.5497   | 0.2848 | 0.2649 | 0.2616 | 0.1887 |
| 0.6954        | 26.32 | 500  | 0.6964          | 0.5199   | 0.3642 | 0.1556 | 0.3709 | 0.1093 |
| 0.6939        | 27.37 | 520  | 0.6897          | 0.5464   | 0.2583 | 0.2881 | 0.2384 | 0.2152 |
| 0.6885        | 28.42 | 540  | 0.6890          | 0.5430   | 0.1656 | 0.3775 | 0.1490 | 0.3079 |
| 0.6849        | 29.47 | 560  | 0.6922          | 0.5662   | 0.2914 | 0.2748 | 0.2517 | 0.1821 |
| 0.6869        | 30.53 | 580  | 0.6954          | 0.5331   | 0.3212 | 0.2119 | 0.3146 | 0.1523 |
| 0.6855        | 31.58 | 600  | 0.6910          | 0.5563   | 0.2185 | 0.3377 | 0.1887 | 0.2550 |
| 0.6876        | 32.63 | 620  | 0.6906          | 0.5861   | 0.2616 | 0.3245 | 0.2020 | 0.2119 |
| 0.6908        | 33.68 | 640  | 0.6954          | 0.5298   | 0.3444 | 0.1854 | 0.3411 | 0.1291 |
| 0.6757        | 34.74 | 660  | 0.6906          | 0.5662   | 0.2483 | 0.3179 | 0.2086 | 0.2252 |
| 0.6756        | 35.79 | 680  | 0.6905          | 0.5695   | 0.2550 | 0.3146 | 0.2119 | 0.2185 |
| 0.7021        | 36.84 | 700  | 0.6948          | 0.5298   | 0.3245 | 0.2053 | 0.3212 | 0.1490 |
| 0.6926        | 37.89 | 720  | 0.6909          | 0.5563   | 0.2682 | 0.2881 | 0.2384 | 0.2053 |
| 0.6913        | 38.95 | 740  | 0.6901          | 0.5563   | 0.2483 | 0.3079 | 0.2185 | 0.2252 |
| 0.6963        | 40.0  | 760  | 0.6921          | 0.5265   | 0.2848 | 0.2417 | 0.2848 | 0.1887 |
| 0.6922        | 41.05 | 780  | 0.6917          | 0.5331   | 0.2815 | 0.2517 | 0.2748 | 0.1921 |
| 0.6916        | 42.11 | 800  | 0.6912          | 0.5298   | 0.2616 | 0.2682 | 0.2583 | 0.2119 |
| 0.685         | 43.16 | 820  | 0.6900          | 0.5497   | 0.2318 | 0.3179 | 0.2086 | 0.2417 |
| 0.6839        | 44.21 | 840  | 0.6907          | 0.5364   | 0.2616 | 0.2748 | 0.2517 | 0.2119 |
| 0.6887        | 45.26 | 860  | 0.6913          | 0.5199   | 0.2682 | 0.2517 | 0.2748 | 0.2053 |
| 0.6845        | 46.32 | 880  | 0.6907          | 0.5331   | 0.2550 | 0.2781 | 0.2483 | 0.2185 |
| 0.684         | 47.37 | 900  | 0.6901          | 0.5464   | 0.2384 | 0.3079 | 0.2185 | 0.2351 |
| 0.6727        | 48.42 | 920  | 0.6903          | 0.5464   | 0.2517 | 0.2947 | 0.2318 | 0.2219 |
| 0.6801        | 49.47 | 940  | 0.6905          | 0.5397   | 0.2550 | 0.2848 | 0.2417 | 0.2185 |


### Framework versions

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