---
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: 0.5298013245033113
---

<!-- 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: 2.7148
- Accuracy: 0.5298
- Tp: 0.2550
- Tn: 0.2748
- Fp: 0.1987
- Fn: 0.2715

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.716         | 1.05  | 20   | 0.6921          | 0.5265   | 0.5265 | 0.0    | 0.4735 | 0.0    |
| 0.7095        | 2.11  | 40   | 0.7040          | 0.4735   | 0.0    | 0.4735 | 0.0    | 0.5265 |
| 0.7124        | 3.16  | 60   | 0.7153          | 0.4735   | 0.0    | 0.4735 | 0.0    | 0.5265 |
| 0.7226        | 4.21  | 80   | 0.7080          | 0.4735   | 0.0    | 0.4735 | 0.0    | 0.5265 |
| 0.7018        | 5.26  | 100  | 0.7256          | 0.4735   | 0.0563 | 0.4172 | 0.0563 | 0.4702 |
| 0.6419        | 6.32  | 120  | 0.8416          | 0.5298   | 0.3212 | 0.2086 | 0.2649 | 0.2053 |
| 0.4401        | 7.37  | 140  | 1.1483          | 0.5497   | 0.2781 | 0.2715 | 0.2020 | 0.2483 |
| 0.2331        | 8.42  | 160  | 1.4366          | 0.5199   | 0.2649 | 0.2550 | 0.2185 | 0.2616 |
| 0.1172        | 9.47  | 180  | 2.4989          | 0.5232   | 0.2616 | 0.2616 | 0.2119 | 0.2649 |
| 0.1918        | 10.53 | 200  | 1.9571          | 0.5629   | 0.2616 | 0.3013 | 0.1722 | 0.2649 |
| 0.0907        | 11.58 | 220  | 2.0011          | 0.5298   | 0.2384 | 0.2914 | 0.1821 | 0.2881 |
| 0.1393        | 12.63 | 240  | 1.8743          | 0.5364   | 0.2815 | 0.2550 | 0.2185 | 0.2450 |
| 0.0994        | 13.68 | 260  | 2.0843          | 0.5166   | 0.2285 | 0.2881 | 0.1854 | 0.2980 |
| 0.0916        | 14.74 | 280  | 1.8777          | 0.5232   | 0.2318 | 0.2914 | 0.1821 | 0.2947 |
| 0.2059        | 15.79 | 300  | 2.5899          | 0.5199   | 0.1689 | 0.3510 | 0.1225 | 0.3576 |
| 0.0534        | 16.84 | 320  | 2.2538          | 0.5364   | 0.2715 | 0.2649 | 0.2086 | 0.2550 |
| 0.056         | 17.89 | 340  | 2.2485          | 0.5298   | 0.2748 | 0.2550 | 0.2185 | 0.2517 |
| 0.0707        | 18.95 | 360  | 1.9060          | 0.5430   | 0.2815 | 0.2616 | 0.2119 | 0.2450 |
| 0.1208        | 20.0  | 380  | 2.4532          | 0.5364   | 0.2781 | 0.2583 | 0.2152 | 0.2483 |
| 0.0831        | 21.05 | 400  | 2.0115          | 0.5397   | 0.2417 | 0.2980 | 0.1755 | 0.2848 |
| 0.0746        | 22.11 | 420  | 2.2016          | 0.5331   | 0.3245 | 0.2086 | 0.2649 | 0.2020 |
| 0.0485        | 23.16 | 440  | 2.1963          | 0.5464   | 0.2781 | 0.2682 | 0.2053 | 0.2483 |
| 0.0254        | 24.21 | 460  | 2.2650          | 0.5265   | 0.2616 | 0.2649 | 0.2086 | 0.2649 |
| 0.0604        | 25.26 | 480  | 2.1988          | 0.5298   | 0.2318 | 0.2980 | 0.1755 | 0.2947 |
| 0.0513        | 26.32 | 500  | 2.2894          | 0.5298   | 0.2881 | 0.2417 | 0.2318 | 0.2384 |
| 0.035         | 27.37 | 520  | 2.2012          | 0.5364   | 0.2219 | 0.3146 | 0.1589 | 0.3046 |
| 0.0632        | 28.42 | 540  | 2.2575          | 0.5397   | 0.2583 | 0.2815 | 0.1921 | 0.2682 |
| 0.0391        | 29.47 | 560  | 2.2376          | 0.5497   | 0.2483 | 0.3013 | 0.1722 | 0.2781 |
| 0.0281        | 30.53 | 580  | 2.3408          | 0.5364   | 0.2682 | 0.2682 | 0.2053 | 0.2583 |
| 0.0286        | 31.58 | 600  | 2.4082          | 0.5397   | 0.2715 | 0.2682 | 0.2053 | 0.2550 |
| 0.0411        | 32.63 | 620  | 2.4859          | 0.5331   | 0.2351 | 0.2980 | 0.1755 | 0.2914 |
| 0.0308        | 33.68 | 640  | 2.5221          | 0.5430   | 0.2947 | 0.2483 | 0.2252 | 0.2318 |
| 0.0419        | 34.74 | 660  | 2.4549          | 0.5166   | 0.2517 | 0.2649 | 0.2086 | 0.2748 |
| 0.0442        | 35.79 | 680  | 2.3828          | 0.5397   | 0.2914 | 0.2483 | 0.2252 | 0.2351 |
| 0.0346        | 36.84 | 700  | 2.4542          | 0.5497   | 0.3179 | 0.2318 | 0.2417 | 0.2086 |
| 0.0277        | 37.89 | 720  | 2.5188          | 0.5265   | 0.2848 | 0.2417 | 0.2318 | 0.2417 |
| 0.0299        | 38.95 | 740  | 2.4768          | 0.5331   | 0.2815 | 0.2517 | 0.2219 | 0.2450 |
| 0.0381        | 40.0  | 760  | 2.4496          | 0.5331   | 0.3013 | 0.2318 | 0.2417 | 0.2252 |
| 0.0317        | 41.05 | 780  | 2.4512          | 0.5265   | 0.2748 | 0.2517 | 0.2219 | 0.2517 |
| 0.0377        | 42.11 | 800  | 2.5661          | 0.5199   | 0.3046 | 0.2152 | 0.2583 | 0.2219 |
| 0.0526        | 43.16 | 820  | 2.6317          | 0.5132   | 0.2881 | 0.2252 | 0.2483 | 0.2384 |
| 0.0321        | 44.21 | 840  | 2.6637          | 0.5132   | 0.2616 | 0.2517 | 0.2219 | 0.2649 |
| 0.0181        | 45.26 | 860  | 2.6816          | 0.5331   | 0.2583 | 0.2748 | 0.1987 | 0.2682 |
| 0.0322        | 46.32 | 880  | 2.6758          | 0.5364   | 0.2517 | 0.2848 | 0.1887 | 0.2748 |
| 0.013         | 47.37 | 900  | 2.6944          | 0.5298   | 0.2517 | 0.2781 | 0.1954 | 0.2748 |
| 0.033         | 48.42 | 920  | 2.7166          | 0.5265   | 0.2550 | 0.2715 | 0.2020 | 0.2715 |
| 0.0229        | 49.47 | 940  | 2.7148          | 0.5298   | 0.2550 | 0.2748 | 0.1987 | 0.2715 |


### Framework versions

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