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# multiberts-seed_2-step_2000k_winobias_classifieronly

This model is a fine-tuned version of [google/multiberts-seed_2-step_2000k](https://huggingface.co/google/multiberts-seed_2-step_2000k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6937
- Accuracy: 0.4943
- Tp: 0.1629
- Tn: 0.3314
- Fp: 0.1686
- Fn: 0.3371

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.7201        | 0.8   | 20   | 0.7046          | 0.5044   | 0.0884 | 0.4160 | 0.0840 | 0.4116 |
| 0.7141        | 1.6   | 40   | 0.6997          | 0.5025   | 0.2001 | 0.3024 | 0.1976 | 0.2999 |
| 0.7113        | 2.4   | 60   | 0.6979          | 0.5063   | 0.2936 | 0.2128 | 0.2872 | 0.2064 |
| 0.7073        | 3.2   | 80   | 0.6967          | 0.5038   | 0.1225 | 0.3813 | 0.1187 | 0.3775 |
| 0.6922        | 4.0   | 100  | 0.6950          | 0.5019   | 0.1616 | 0.3403 | 0.1597 | 0.3384 |
| 0.7025        | 4.8   | 120  | 0.6953          | 0.5076   | 0.1313 | 0.3763 | 0.1237 | 0.3687 |
| 0.7029        | 5.6   | 140  | 0.6947          | 0.5019   | 0.2986 | 0.2033 | 0.2967 | 0.2014 |
| 0.6974        | 6.4   | 160  | 0.6952          | 0.5038   | 0.1092 | 0.3946 | 0.1054 | 0.3908 |
| 0.6992        | 7.2   | 180  | 0.6948          | 0.5088   | 0.1275 | 0.3813 | 0.1187 | 0.3725 |
| 0.6944        | 8.0   | 200  | 0.6939          | 0.4956   | 0.2557 | 0.2399 | 0.2601 | 0.2443 |
| 0.6953        | 8.8   | 220  | 0.6940          | 0.4912   | 0.1824 | 0.3087 | 0.1913 | 0.3176 |
| 0.6994        | 9.6   | 240  | 0.6942          | 0.4949   | 0.1503 | 0.3447 | 0.1553 | 0.3497 |
| 0.6955        | 10.4  | 260  | 0.6939          | 0.4949   | 0.2405 | 0.2544 | 0.2456 | 0.2595 |
| 0.6993        | 11.2  | 280  | 0.6942          | 0.5006   | 0.1446 | 0.3561 | 0.1439 | 0.3554 |
| 0.6925        | 12.0  | 300  | 0.6940          | 0.4975   | 0.1616 | 0.3359 | 0.1641 | 0.3384 |
| 0.6985        | 12.8  | 320  | 0.6938          | 0.4905   | 0.2008 | 0.2898 | 0.2102 | 0.2992 |
| 0.7014        | 13.6  | 340  | 0.6951          | 0.5051   | 0.0821 | 0.4230 | 0.0770 | 0.4179 |
| 0.6947        | 14.4  | 360  | 0.6939          | 0.4912   | 0.3150 | 0.1761 | 0.3239 | 0.1850 |
| 0.698         | 15.2  | 380  | 0.6940          | 0.5006   | 0.1654 | 0.3352 | 0.1648 | 0.3346 |
| 0.6912        | 16.0  | 400  | 0.6946          | 0.5032   | 0.1073 | 0.3958 | 0.1042 | 0.3927 |
| 0.6929        | 16.8  | 420  | 0.6946          | 0.5      | 0.1035 | 0.3965 | 0.1035 | 0.3965 |
| 0.6946        | 17.6  | 440  | 0.6938          | 0.4994   | 0.1951 | 0.3043 | 0.1957 | 0.3049 |
| 0.6955        | 18.4  | 460  | 0.6937          | 0.4962   | 0.2481 | 0.2481 | 0.2519 | 0.2519 |
| 0.7           | 19.2  | 480  | 0.6938          | 0.4994   | 0.1894 | 0.3100 | 0.1900 | 0.3106 |
| 0.6947        | 20.0  | 500  | 0.6938          | 0.4994   | 0.2008 | 0.2986 | 0.2014 | 0.2992 |
| 0.6978        | 20.8  | 520  | 0.6937          | 0.4937   | 0.2462 | 0.2475 | 0.2525 | 0.2538 |
| 0.7004        | 21.6  | 540  | 0.6937          | 0.4962   | 0.2677 | 0.2285 | 0.2715 | 0.2323 |
| 0.6977        | 22.4  | 560  | 0.6937          | 0.4975   | 0.2620 | 0.2355 | 0.2645 | 0.2380 |
| 0.6933        | 23.2  | 580  | 0.6937          | 0.4968   | 0.2342 | 0.2626 | 0.2374 | 0.2658 |
| 0.6991        | 24.0  | 600  | 0.6938          | 0.5      | 0.1824 | 0.3176 | 0.1824 | 0.3176 |
| 0.6961        | 24.8  | 620  | 0.6937          | 0.4987   | 0.2140 | 0.2847 | 0.2153 | 0.2860 |
| 0.7054        | 25.6  | 640  | 0.6944          | 0.5032   | 0.1029 | 0.4003 | 0.0997 | 0.3971 |
| 0.6991        | 26.4  | 660  | 0.6942          | 0.4943   | 0.1181 | 0.3763 | 0.1237 | 0.3819 |
| 0.702         | 27.2  | 680  | 0.6943          | 0.5013   | 0.1004 | 0.4009 | 0.0991 | 0.3996 |
| 0.6968        | 28.0  | 700  | 0.6941          | 0.4918   | 0.1206 | 0.3712 | 0.1288 | 0.3794 |
| 0.6939        | 28.8  | 720  | 0.6941          | 0.4912   | 0.1136 | 0.3775 | 0.1225 | 0.3864 |
| 0.6956        | 29.6  | 740  | 0.6936          | 0.5019   | 0.2361 | 0.2658 | 0.2342 | 0.2639 |
| 0.6956        | 30.4  | 760  | 0.6936          | 0.4968   | 0.2172 | 0.2797 | 0.2203 | 0.2828 |
| 0.6987        | 31.2  | 780  | 0.6937          | 0.4924   | 0.1679 | 0.3245 | 0.1755 | 0.3321 |
| 0.6935        | 32.0  | 800  | 0.6936          | 0.4912   | 0.1970 | 0.2942 | 0.2058 | 0.3030 |
| 0.6967        | 32.8  | 820  | 0.6936          | 0.4937   | 0.1913 | 0.3024 | 0.1976 | 0.3087 |
| 0.6982        | 33.6  | 840  | 0.6941          | 0.4968   | 0.1288 | 0.3681 | 0.1319 | 0.3712 |
| 0.7007        | 34.4  | 860  | 0.6937          | 0.4949   | 0.1824 | 0.3125 | 0.1875 | 0.3176 |
| 0.7           | 35.2  | 880  | 0.6936          | 0.4949   | 0.2197 | 0.2753 | 0.2247 | 0.2803 |
| 0.6904        | 36.0  | 900  | 0.6935          | 0.5025   | 0.2431 | 0.2595 | 0.2405 | 0.2569 |
| 0.6945        | 36.8  | 920  | 0.6936          | 0.4937   | 0.2096 | 0.2841 | 0.2159 | 0.2904 |
| 0.7025        | 37.6  | 940  | 0.6935          | 0.5019   | 0.2519 | 0.25   | 0.25   | 0.2481 |
| 0.6969        | 38.4  | 960  | 0.6935          | 0.4994   | 0.2235 | 0.2759 | 0.2241 | 0.2765 |
| 0.7038        | 39.2  | 980  | 0.6936          | 0.4949   | 0.1818 | 0.3131 | 0.1869 | 0.3182 |
| 0.698         | 40.0  | 1000 | 0.6937          | 0.4931   | 0.1736 | 0.3194 | 0.1806 | 0.3264 |
| 0.6973        | 40.8  | 1020 | 0.6938          | 0.5013   | 0.1540 | 0.3472 | 0.1528 | 0.3460 |
| 0.6964        | 41.6  | 1040 | 0.6939          | 0.5032   | 0.1408 | 0.3624 | 0.1376 | 0.3592 |
| 0.6999        | 42.4  | 1060 | 0.6939          | 0.5      | 0.1370 | 0.3630 | 0.1370 | 0.3630 |
| 0.7002        | 43.2  | 1080 | 0.6939          | 0.5006   | 0.1376 | 0.3630 | 0.1370 | 0.3624 |
| 0.6939        | 44.0  | 1100 | 0.6940          | 0.4956   | 0.1225 | 0.3731 | 0.1269 | 0.3775 |
| 0.6984        | 44.8  | 1120 | 0.6939          | 0.4994   | 0.1338 | 0.3655 | 0.1345 | 0.3662 |
| 0.6946        | 45.6  | 1140 | 0.6939          | 0.5019   | 0.1395 | 0.3624 | 0.1376 | 0.3605 |
| 0.6972        | 46.4  | 1160 | 0.6937          | 0.4962   | 0.1616 | 0.3346 | 0.1654 | 0.3384 |
| 0.694         | 47.2  | 1180 | 0.6937          | 0.4905   | 0.1679 | 0.3226 | 0.1774 | 0.3321 |
| 0.6974        | 48.0  | 1200 | 0.6937          | 0.4886   | 0.1648 | 0.3239 | 0.1761 | 0.3352 |
| 0.6956        | 48.8  | 1220 | 0.6937          | 0.4893   | 0.1648 | 0.3245 | 0.1755 | 0.3352 |
| 0.7032        | 49.6  | 1240 | 0.6937          | 0.4943   | 0.1629 | 0.3314 | 0.1686 | 0.3371 |


### 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": "multiberts-seed_2-step_2000k_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 

multiberts-seed\_2-step\_2000k\_winobias\_classifieronly
========================================================


This model is a fine-tuned version of google/multiberts-seed\_2-step\_2000k on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6937
* Accuracy: 0.4943
* Tp: 0.1629
* Tn: 0.3314
* Fp: 0.1686
* Fn: 0.3371


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 #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: 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 #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: 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