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# 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: 2.5652
- Accuracy: 0.7119

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.728         | 0.53  | 10   | 0.6939          | 0.4901   |
| 0.6914        | 1.05  | 20   | 0.6939          | 0.4901   |
| 0.705         | 1.58  | 30   | 0.6925          | 0.5066   |
| 0.6993        | 2.11  | 40   | 0.6949          | 0.5066   |
| 0.6979        | 2.63  | 50   | 0.6996          | 0.5066   |
| 0.7152        | 3.16  | 60   | 0.6940          | 0.4901   |
| 0.7158        | 3.68  | 70   | 0.7007          | 0.4934   |
| 0.6968        | 4.21  | 80   | 0.6999          | 0.5066   |
| 0.7164        | 4.74  | 90   | 0.6977          | 0.4934   |
| 0.6698        | 5.26  | 100  | 0.7079          | 0.4536   |
| 0.611         | 5.79  | 110  | 0.8882          | 0.5099   |
| 0.6487        | 6.32  | 120  | 0.8360          | 0.5066   |
| 0.5223        | 6.84  | 130  | 0.8047          | 0.5728   |
| 0.2879        | 7.37  | 140  | 1.1483          | 0.5795   |
| 0.2369        | 7.89  | 150  | 1.1773          | 0.5993   |
| 0.2542        | 8.42  | 160  | 0.9170          | 0.6424   |
| 0.1743        | 8.95  | 170  | 1.3674          | 0.6424   |
| 0.1307        | 9.47  | 180  | 1.0740          | 0.7152   |
| 0.0718        | 10.0  | 190  | 1.4397          | 0.6424   |
| 0.0278        | 10.53 | 200  | 1.9821          | 0.6523   |
| 0.0519        | 11.05 | 210  | 1.6970          | 0.6755   |
| 0.0269        | 11.58 | 220  | 1.8299          | 0.6656   |
| 0.0556        | 12.11 | 230  | 1.9459          | 0.7086   |
| 0.0455        | 12.63 | 240  | 1.6443          | 0.6854   |
| 0.0665        | 13.16 | 250  | 1.9887          | 0.6821   |
| 0.009         | 13.68 | 260  | 2.0236          | 0.6788   |
| 0.0146        | 14.21 | 270  | 1.8515          | 0.7152   |
| 0.0034        | 14.74 | 280  | 1.9315          | 0.7252   |
| 0.0248        | 15.26 | 290  | 2.0754          | 0.7119   |
| 0.0536        | 15.79 | 300  | 2.0371          | 0.7053   |
| 0.0393        | 16.32 | 310  | 1.9381          | 0.6987   |
| 0.0255        | 16.84 | 320  | 1.9074          | 0.6788   |
| 0.0116        | 17.37 | 330  | 2.2182          | 0.6623   |
| 0.0128        | 17.89 | 340  | 2.3002          | 0.6689   |
| 0.0006        | 18.42 | 350  | 2.2353          | 0.6788   |
| 0.0053        | 18.95 | 360  | 2.4277          | 0.6755   |
| 0.0013        | 19.47 | 370  | 2.5156          | 0.6490   |
| 0.0004        | 20.0  | 380  | 2.5091          | 0.6689   |
| 0.0003        | 20.53 | 390  | 2.4096          | 0.6854   |
| 0.0017        | 21.05 | 400  | 2.3497          | 0.6921   |
| 0.0001        | 21.58 | 410  | 2.3376          | 0.6854   |
| 0.012         | 22.11 | 420  | 2.3832          | 0.6854   |
| 0.0002        | 22.63 | 430  | 2.4388          | 0.7053   |
| 0.0001        | 23.16 | 440  | 2.4821          | 0.7152   |
| 0.0001        | 23.68 | 450  | 2.5027          | 0.7119   |
| 0.0001        | 24.21 | 460  | 2.5105          | 0.7152   |
| 0.0001        | 24.74 | 470  | 2.5145          | 0.7152   |
| 0.0002        | 25.26 | 480  | 2.5143          | 0.6954   |
| 0.0001        | 25.79 | 490  | 2.5629          | 0.6821   |
| 0.0002        | 26.32 | 500  | 2.5414          | 0.6887   |
| 0.0001        | 26.84 | 510  | 2.5301          | 0.7119   |
| 0.0012        | 27.37 | 520  | 2.5360          | 0.7020   |
| 0.0           | 27.89 | 530  | 2.5428          | 0.6921   |
| 0.0117        | 28.42 | 540  | 2.5455          | 0.6954   |
| 0.0001        | 28.95 | 550  | 2.5598          | 0.7086   |
| 0.0001        | 29.47 | 560  | 2.5648          | 0.7119   |
| 0.0001        | 30.0  | 570  | 2.5652          | 0.7119   |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["crows_pairs"], "metrics": ["accuracy"], "model-index": [{"name": "xlnet-base-cased_crows_pairs_finetuned", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "crows_pairs", "type": "crows_pairs", "config": "crows_pairs", "split": "test", "args": "crows_pairs"}, "metrics": [{"type": "accuracy", "value": 0.7119205298013245, "name": "Accuracy"}]}]}]}
transformers pytorch xlnet text-classification generated_from_trainer dataset:crows_pairs license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlnet #text-classification #generated_from_trainer #dataset-crows_pairs #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

xlnet-base-cased\_crows\_pairs\_finetuned
=========================================


This model is a fine-tuned version of xlnet-base-cased on the crows\_pairs dataset.
It achieves the following results on the evaluation set:


* Loss: 2.5652
* Accuracy: 0.7119


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: 30


### Training results



### Framework versions


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



### 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: 30 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #xlnet #text-classification #generated_from_trainer #dataset-crows_pairs #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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: 30 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #xlnet #text-classification #generated_from_trainer #dataset-crows_pairs #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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: 30### Training results### Framework versions


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