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# xlnet-base-cased_winobias_finetuned

This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6133
- Accuracy: 0.8232
- Tp: 0.4129
- Tn: 0.4104
- Fp: 0.0896
- Fn: 0.0871

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.7262        | 0.8   | 20   | 0.6972          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7214        | 1.6   | 40   | 0.7030          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7094        | 2.4   | 60   | 0.6976          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7102        | 3.2   | 80   | 0.6932          | 0.4975   | 0.0518 | 0.4457 | 0.0543 | 0.4482 |
| 0.6953        | 4.0   | 100  | 0.6932          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7017        | 4.8   | 120  | 0.6931          | 0.5044   | 0.4173 | 0.0871 | 0.4129 | 0.0827 |
| 0.7098        | 5.6   | 140  | 0.6950          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7075        | 6.4   | 160  | 0.6932          | 0.5      | 0.4811 | 0.0189 | 0.4811 | 0.0189 |
| 0.7138        | 7.2   | 180  | 0.6935          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7009        | 8.0   | 200  | 0.6932          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7007        | 8.8   | 220  | 0.6932          | 0.5006   | 0.4949 | 0.0057 | 0.4943 | 0.0051 |
| 0.6982        | 9.6   | 240  | 0.6929          | 0.5107   | 0.0638 | 0.4470 | 0.0530 | 0.4362 |
| 0.6991        | 10.4  | 260  | 0.6943          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7027        | 11.2  | 280  | 0.6937          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.6944        | 12.0  | 300  | 0.6952          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.6928        | 12.8  | 320  | 0.6931          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.707         | 13.6  | 340  | 0.6938          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7059        | 14.4  | 360  | 0.6935          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.7034        | 15.2  | 380  | 0.6939          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7048        | 16.0  | 400  | 0.6935          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.6967        | 16.8  | 420  | 0.6929          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.705         | 17.6  | 440  | 0.6924          | 0.5      | 0.0    | 0.5    | 0.0    | 0.5    |
| 0.7008        | 18.4  | 460  | 0.6909          | 0.5      | 0.5    | 0.0    | 0.5    | 0.0    |
| 0.565         | 19.2  | 480  | 0.5361          | 0.7418   | 0.4779 | 0.2639 | 0.2361 | 0.0221 |
| 0.4145        | 20.0  | 500  | 0.4368          | 0.7336   | 0.4956 | 0.2380 | 0.2620 | 0.0044 |
| 0.3663        | 20.8  | 520  | 0.4434          | 0.7355   | 0.4943 | 0.2412 | 0.2588 | 0.0057 |
| 0.3586        | 21.6  | 540  | 0.5477          | 0.7670   | 0.3491 | 0.4179 | 0.0821 | 0.1509 |
| 0.3668        | 22.4  | 560  | 0.3964          | 0.7765   | 0.4154 | 0.3611 | 0.1389 | 0.0846 |
| 0.3007        | 23.2  | 580  | 0.5550          | 0.7797   | 0.4394 | 0.3403 | 0.1597 | 0.0606 |
| 0.3052        | 24.0  | 600  | 0.4791          | 0.7992   | 0.4186 | 0.3807 | 0.1193 | 0.0814 |
| 0.2277        | 24.8  | 620  | 0.6168          | 0.8144   | 0.4179 | 0.3965 | 0.1035 | 0.0821 |
| 0.1419        | 25.6  | 640  | 1.0091          | 0.8131   | 0.4097 | 0.4034 | 0.0966 | 0.0903 |
| 0.1323        | 26.4  | 660  | 0.6764          | 0.8106   | 0.4097 | 0.4009 | 0.0991 | 0.0903 |
| 0.1191        | 27.2  | 680  | 0.5545          | 0.8125   | 0.4236 | 0.3889 | 0.1111 | 0.0764 |
| 0.0766        | 28.0  | 700  | 0.9580          | 0.8169   | 0.4167 | 0.4003 | 0.0997 | 0.0833 |
| 0.0452        | 28.8  | 720  | 1.1316          | 0.8157   | 0.4009 | 0.4148 | 0.0852 | 0.0991 |
| 0.0491        | 29.6  | 740  | 1.2071          | 0.8119   | 0.4154 | 0.3965 | 0.1035 | 0.0846 |
| 0.073         | 30.4  | 760  | 1.1414          | 0.8163   | 0.3914 | 0.4249 | 0.0751 | 0.1086 |
| 0.0496        | 31.2  | 780  | 1.0776          | 0.8182   | 0.4255 | 0.3927 | 0.1073 | 0.0745 |
| 0.0296        | 32.0  | 800  | 1.1979          | 0.8188   | 0.4078 | 0.4110 | 0.0890 | 0.0922 |
| 0.0379        | 32.8  | 820  | 1.2639          | 0.8201   | 0.4167 | 0.4034 | 0.0966 | 0.0833 |
| 0.0165        | 33.6  | 840  | 1.3292          | 0.8125   | 0.3958 | 0.4167 | 0.0833 | 0.1042 |
| 0.0132        | 34.4  | 860  | 1.4465          | 0.8169   | 0.3971 | 0.4198 | 0.0802 | 0.1029 |
| 0.0205        | 35.2  | 880  | 1.3776          | 0.8201   | 0.4091 | 0.4110 | 0.0890 | 0.0909 |
| 0.0051        | 36.0  | 900  | 1.3503          | 0.8207   | 0.4116 | 0.4091 | 0.0909 | 0.0884 |
| 0.0013        | 36.8  | 920  | 1.4425          | 0.8213   | 0.4072 | 0.4141 | 0.0859 | 0.0928 |
| 0.0002        | 37.6  | 940  | 1.4601          | 0.8226   | 0.4028 | 0.4198 | 0.0802 | 0.0972 |
| 0.0007        | 38.4  | 960  | 1.5047          | 0.8213   | 0.4034 | 0.4179 | 0.0821 | 0.0966 |
| 0.0002        | 39.2  | 980  | 1.5297          | 0.8207   | 0.4072 | 0.4135 | 0.0865 | 0.0928 |
| 0.0002        | 40.0  | 1000 | 1.5409          | 0.8226   | 0.4122 | 0.4104 | 0.0896 | 0.0878 |
| 0.0002        | 40.8  | 1020 | 1.5452          | 0.8239   | 0.4097 | 0.4141 | 0.0859 | 0.0903 |
| 0.0001        | 41.6  | 1040 | 1.6013          | 0.8194   | 0.4097 | 0.4097 | 0.0903 | 0.0903 |
| 0.0001        | 42.4  | 1060 | 1.6177          | 0.8201   | 0.4104 | 0.4097 | 0.0903 | 0.0896 |
| 0.0001        | 43.2  | 1080 | 1.6290          | 0.8201   | 0.4104 | 0.4097 | 0.0903 | 0.0896 |
| 0.0001        | 44.0  | 1100 | 1.6365          | 0.8201   | 0.4097 | 0.4104 | 0.0896 | 0.0903 |
| 0.0001        | 44.8  | 1120 | 1.6425          | 0.8194   | 0.4097 | 0.4097 | 0.0903 | 0.0903 |
| 0.0142        | 45.6  | 1140 | 1.6429          | 0.8220   | 0.4110 | 0.4110 | 0.0890 | 0.0890 |
| 0.0001        | 46.4  | 1160 | 1.6080          | 0.8239   | 0.4135 | 0.4104 | 0.0896 | 0.0865 |
| 0.0001        | 47.2  | 1180 | 1.6113          | 0.8239   | 0.4135 | 0.4104 | 0.0896 | 0.0865 |
| 0.0001        | 48.0  | 1200 | 1.6046          | 0.8239   | 0.4129 | 0.4110 | 0.0890 | 0.0871 |
| 0.0001        | 48.8  | 1220 | 1.6066          | 0.8239   | 0.4135 | 0.4104 | 0.0896 | 0.0865 |
| 0.0001        | 49.6  | 1240 | 1.6133          | 0.8232   | 0.4129 | 0.4104 | 0.0896 | 0.0871 |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "xlnet-base-cased_winobias_finetuned", "results": []}]}
transformers pytorch xlnet text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlnet #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

xlnet-base-cased\_winobias\_finetuned
=====================================


This model is a fine-tuned version of xlnet-base-cased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.6133
* Accuracy: 0.8232
* Tp: 0.4129
* Tn: 0.4104
* Fp: 0.0896
* Fn: 0.0871


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



### 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: 50 ### 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 #license-mit #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: 50 ### 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 #license-mit #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: 50### Training results### Framework versions


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