---
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: 0.5
---

<!-- 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: 0.6933
- Accuracy: 0.5
- Tp: 0.5
- Tn: 0.0
- Fp: 0.5
- Fn: 0.0

## 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: 0.0001
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.7406        | 1.05  | 20   | 0.6941          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7008        | 2.11  | 40   | 0.6959          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7067        | 3.16  | 60   | 0.6932          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7029        | 4.21  | 80   | 0.6937          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7103        | 5.26  | 100  | 0.6932          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7085        | 6.32  | 120  | 0.7004          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7061        | 7.37  | 140  | 0.6933          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7013        | 8.42  | 160  | 0.6954          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.6952        | 9.47  | 180  | 0.6933          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7084        | 10.53 | 200  | 0.7079          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.71          | 11.58 | 220  | 0.6999          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7036        | 12.63 | 240  | 0.6932          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7043        | 13.68 | 260  | 0.6942          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7058        | 14.74 | 280  | 0.6947          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.6993        | 15.79 | 300  | 0.6951          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7009        | 16.84 | 320  | 0.6936          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7069        | 17.89 | 340  | 0.7002          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7068        | 18.95 | 360  | 0.6970          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7042        | 20.0  | 380  | 0.6935          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.6999        | 21.05 | 400  | 0.6957          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.6966        | 22.11 | 420  | 0.6936          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.6975        | 23.16 | 440  | 0.6934          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7043        | 24.21 | 460  | 0.6934          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7002        | 25.26 | 480  | 0.6932          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7039        | 26.32 | 500  | 0.7004          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.6927        | 27.37 | 520  | 0.6932          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7078        | 28.42 | 540  | 0.6941          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.6999        | 29.47 | 560  | 0.6969          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7063        | 30.53 | 580  | 0.6936          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7011        | 31.58 | 600  | 0.6934          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7061        | 32.63 | 620  | 0.6958          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.6971        | 33.68 | 640  | 0.6932          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7007        | 34.74 | 660  | 0.6932          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7014        | 35.79 | 680  | 0.6954          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.6976        | 36.84 | 700  | 0.6951          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.6957        | 37.89 | 720  | 0.6936          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7009        | 38.95 | 740  | 0.6950          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.6941        | 40.0  | 760  | 0.6933          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.6989        | 41.05 | 780  | 0.6948          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.6935        | 42.11 | 800  | 0.6974          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.6939        | 43.16 | 820  | 0.6956          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.6975        | 44.21 | 840  | 0.6955          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.669         | 45.26 | 860  | 0.7089          | 0.5132   | 0.1623 | 0.3510 | 0.1490 | 0.3377 |
| 0.6896        | 46.32 | 880  | 0.7088          | 0.4669   | 0.4106 | 0.0563 | 0.4437 | 0.0894 |
| 0.6942        | 47.37 | 900  | 0.6944          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.6942        | 48.42 | 920  | 0.6933          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.6921        | 49.47 | 940  | 0.6933          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |


### Framework versions

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