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---
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
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