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# bert-base-uncased_winobias_classifieronly

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6924
- Accuracy: 0.5290
- Tp: 0.2614
- Tn: 0.2677
- Fp: 0.2323
- Fn: 0.2386

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.6962        | 0.8   | 20   | 0.6948          | 0.5019   | 0.2203 | 0.2816 | 0.2184 | 0.2797 |
| 0.6962        | 1.6   | 40   | 0.6944          | 0.4981   | 0.2696 | 0.2285 | 0.2715 | 0.2304 |
| 0.6967        | 2.4   | 60   | 0.6954          | 0.5019   | 0.4078 | 0.0941 | 0.4059 | 0.0922 |
| 0.6963        | 3.2   | 80   | 0.6944          | 0.5088   | 0.0581 | 0.4508 | 0.0492 | 0.4419 |
| 0.6949        | 4.0   | 100  | 0.6936          | 0.4987   | 0.2948 | 0.2039 | 0.2961 | 0.2052 |
| 0.7003        | 4.8   | 120  | 0.6944          | 0.5013   | 0.0322 | 0.4691 | 0.0309 | 0.4678 |
| 0.6948        | 5.6   | 140  | 0.6938          | 0.4987   | 0.3630 | 0.1357 | 0.3643 | 0.1370 |
| 0.695         | 6.4   | 160  | 0.6945          | 0.5032   | 0.0284 | 0.4747 | 0.0253 | 0.4716 |
| 0.6954        | 7.2   | 180  | 0.6939          | 0.5025   | 0.0739 | 0.4287 | 0.0713 | 0.4261 |
| 0.6914        | 8.0   | 200  | 0.6936          | 0.5019   | 0.3491 | 0.1528 | 0.3472 | 0.1509 |
| 0.6968        | 8.8   | 220  | 0.6945          | 0.4981   | 0.0234 | 0.4747 | 0.0253 | 0.4766 |
| 0.6961        | 9.6   | 240  | 0.6934          | 0.5069   | 0.2020 | 0.3049 | 0.1951 | 0.2980 |
| 0.6957        | 10.4  | 260  | 0.6932          | 0.5038   | 0.2917 | 0.2121 | 0.2879 | 0.2083 |
| 0.6929        | 11.2  | 280  | 0.6939          | 0.5032   | 0.0271 | 0.4760 | 0.0240 | 0.4729 |
| 0.7065        | 12.0  | 300  | 0.6934          | 0.4987   | 0.4009 | 0.0979 | 0.4021 | 0.0991 |
| 0.6986        | 12.8  | 320  | 0.6934          | 0.5025   | 0.0833 | 0.4192 | 0.0808 | 0.4167 |
| 0.6974        | 13.6  | 340  | 0.6945          | 0.4949   | 0.0221 | 0.4729 | 0.0271 | 0.4779 |
| 0.6991        | 14.4  | 360  | 0.6944          | 0.5044   | 0.4956 | 0.0088 | 0.4912 | 0.0044 |
| 0.6961        | 15.2  | 380  | 0.6932          | 0.5038   | 0.0751 | 0.4287 | 0.0713 | 0.4249 |
| 0.6982        | 16.0  | 400  | 0.6928          | 0.5057   | 0.1641 | 0.3415 | 0.1585 | 0.3359 |
| 0.6986        | 16.8  | 420  | 0.6928          | 0.5101   | 0.1351 | 0.375  | 0.125  | 0.3649 |
| 0.695         | 17.6  | 440  | 0.6927          | 0.5107   | 0.2702 | 0.2405 | 0.2595 | 0.2298 |
| 0.6954        | 18.4  | 460  | 0.6928          | 0.5095   | 0.3441 | 0.1654 | 0.3346 | 0.1559 |
| 0.6954        | 19.2  | 480  | 0.6926          | 0.5133   | 0.2626 | 0.2506 | 0.2494 | 0.2374 |
| 0.7003        | 20.0  | 500  | 0.6926          | 0.5183   | 0.2708 | 0.2475 | 0.2525 | 0.2292 |
| 0.6982        | 20.8  | 520  | 0.6926          | 0.5082   | 0.2652 | 0.2431 | 0.2569 | 0.2348 |
| 0.696         | 21.6  | 540  | 0.6927          | 0.5170   | 0.2456 | 0.2715 | 0.2285 | 0.2544 |
| 0.6928        | 22.4  | 560  | 0.6928          | 0.5038   | 0.3958 | 0.1080 | 0.3920 | 0.1042 |
| 0.6968        | 23.2  | 580  | 0.6927          | 0.5164   | 0.3062 | 0.2102 | 0.2898 | 0.1938 |
| 0.693         | 24.0  | 600  | 0.6926          | 0.5196   | 0.2702 | 0.2494 | 0.2506 | 0.2298 |
| 0.7           | 24.8  | 620  | 0.6927          | 0.5088   | 0.4148 | 0.0941 | 0.4059 | 0.0852 |
| 0.6993        | 25.6  | 640  | 0.6932          | 0.5044   | 0.0335 | 0.4710 | 0.0290 | 0.4665 |
| 0.7022        | 26.4  | 660  | 0.6926          | 0.5152   | 0.1806 | 0.3346 | 0.1654 | 0.3194 |
| 0.6954        | 27.2  | 680  | 0.6925          | 0.5170   | 0.2241 | 0.2929 | 0.2071 | 0.2759 |
| 0.6978        | 28.0  | 700  | 0.6925          | 0.5158   | 0.3289 | 0.1869 | 0.3131 | 0.1711 |
| 0.7013        | 28.8  | 720  | 0.6925          | 0.5145   | 0.2601 | 0.2544 | 0.2456 | 0.2399 |
| 0.7004        | 29.6  | 740  | 0.6931          | 0.5063   | 0.4729 | 0.0335 | 0.4665 | 0.0271 |
| 0.6913        | 30.4  | 760  | 0.6926          | 0.5164   | 0.1982 | 0.3182 | 0.1818 | 0.3018 |
| 0.6974        | 31.2  | 780  | 0.6926          | 0.5164   | 0.1900 | 0.3264 | 0.1736 | 0.3100 |
| 0.6904        | 32.0  | 800  | 0.6926          | 0.5107   | 0.3933 | 0.1174 | 0.3826 | 0.1067 |
| 0.6917        | 32.8  | 820  | 0.6925          | 0.5271   | 0.2961 | 0.2311 | 0.2689 | 0.2039 |
| 0.6956        | 33.6  | 840  | 0.6927          | 0.5114   | 0.1098 | 0.4015 | 0.0985 | 0.3902 |
| 0.6941        | 34.4  | 860  | 0.6927          | 0.5133   | 0.4350 | 0.0783 | 0.4217 | 0.0650 |
| 0.6935        | 35.2  | 880  | 0.6928          | 0.5114   | 0.4571 | 0.0543 | 0.4457 | 0.0429 |
| 0.6935        | 36.0  | 900  | 0.6925          | 0.5114   | 0.3643 | 0.1471 | 0.3529 | 0.1357 |
| 0.6935        | 36.8  | 920  | 0.6924          | 0.5177   | 0.2020 | 0.3157 | 0.1843 | 0.2980 |
| 0.6916        | 37.6  | 940  | 0.6925          | 0.5177   | 0.3314 | 0.1862 | 0.3138 | 0.1686 |
| 0.703         | 38.4  | 960  | 0.6925          | 0.5183   | 0.3220 | 0.1963 | 0.3037 | 0.1780 |
| 0.698         | 39.2  | 980  | 0.6925          | 0.5234   | 0.3005 | 0.2229 | 0.2771 | 0.1995 |
| 0.6966        | 40.0  | 1000 | 0.6925          | 0.5240   | 0.3150 | 0.2090 | 0.2910 | 0.1850 |
| 0.6942        | 40.8  | 1020 | 0.6925          | 0.5290   | 0.3049 | 0.2241 | 0.2759 | 0.1951 |
| 0.6916        | 41.6  | 1040 | 0.6924          | 0.5240   | 0.2999 | 0.2241 | 0.2759 | 0.2001 |
| 0.6946        | 42.4  | 1060 | 0.6925          | 0.5221   | 0.1862 | 0.3359 | 0.1641 | 0.3138 |
| 0.6925        | 43.2  | 1080 | 0.6926          | 0.5025   | 0.1155 | 0.3870 | 0.1130 | 0.3845 |
| 0.6977        | 44.0  | 1100 | 0.6925          | 0.5170   | 0.1559 | 0.3611 | 0.1389 | 0.3441 |
| 0.6938        | 44.8  | 1120 | 0.6924          | 0.5221   | 0.2008 | 0.3213 | 0.1787 | 0.2992 |
| 0.6972        | 45.6  | 1140 | 0.6924          | 0.5208   | 0.2449 | 0.2759 | 0.2241 | 0.2551 |
| 0.6938        | 46.4  | 1160 | 0.6924          | 0.5227   | 0.3213 | 0.2014 | 0.2986 | 0.1787 |
| 0.6924        | 47.2  | 1180 | 0.6924          | 0.5107   | 0.3396 | 0.1711 | 0.3289 | 0.1604 |
| 0.7034        | 48.0  | 1200 | 0.6924          | 0.5322   | 0.2980 | 0.2342 | 0.2658 | 0.2020 |
| 0.6935        | 48.8  | 1220 | 0.6924          | 0.5316   | 0.2809 | 0.2506 | 0.2494 | 0.2191 |
| 0.6971        | 49.6  | 1240 | 0.6924          | 0.5290   | 0.2614 | 0.2677 | 0.2323 | 0.2386 |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased_winobias_classifieronly", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased\_winobias\_classifieronly
===========================================


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


* Loss: 0.6924
* Accuracy: 0.5290
* Tp: 0.2614
* Tn: 0.2677
* Fp: 0.2323
* Fn: 0.2386


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



### 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: 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


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