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# multiberts-seed_0_winobias_classifieronly

This model is a fine-tuned version of [google/multiberts-seed_0](https://huggingface.co/google/multiberts-seed_0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6937
- Accuracy: 0.5088
- Tp: 0.3011
- Tn: 0.2077
- Fp: 0.2923
- Fn: 0.1989

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.6992        | 0.8   | 20   | 0.6945          | 0.4943   | 0.2285 | 0.2658 | 0.2342 | 0.2715 |
| 0.7005        | 1.6   | 40   | 0.6944          | 0.4937   | 0.2702 | 0.2235 | 0.2765 | 0.2298 |
| 0.7025        | 2.4   | 60   | 0.6958          | 0.5032   | 0.4457 | 0.0574 | 0.4426 | 0.0543 |
| 0.6937        | 3.2   | 80   | 0.6943          | 0.5044   | 0.3403 | 0.1641 | 0.3359 | 0.1597 |
| 0.6935        | 4.0   | 100  | 0.6942          | 0.4943   | 0.2847 | 0.2096 | 0.2904 | 0.2153 |
| 0.6995        | 4.8   | 120  | 0.6942          | 0.4962   | 0.2702 | 0.2260 | 0.2740 | 0.2298 |
| 0.7035        | 5.6   | 140  | 0.6955          | 0.5      | 0.4324 | 0.0676 | 0.4324 | 0.0676 |
| 0.7001        | 6.4   | 160  | 0.6941          | 0.5      | 0.2437 | 0.2563 | 0.2437 | 0.2563 |
| 0.6967        | 7.2   | 180  | 0.6941          | 0.5032   | 0.2418 | 0.2614 | 0.2386 | 0.2582 |
| 0.6934        | 8.0   | 200  | 0.6943          | 0.4987   | 0.3472 | 0.1515 | 0.3485 | 0.1528 |
| 0.6963        | 8.8   | 220  | 0.6941          | 0.4956   | 0.3112 | 0.1843 | 0.3157 | 0.1888 |
| 0.6962        | 9.6   | 240  | 0.6940          | 0.5006   | 0.3112 | 0.1894 | 0.3106 | 0.1888 |
| 0.6955        | 10.4  | 260  | 0.6944          | 0.4949   | 0.3580 | 0.1370 | 0.3630 | 0.1420 |
| 0.7032        | 11.2  | 280  | 0.6941          | 0.4912   | 0.2683 | 0.2229 | 0.2771 | 0.2317 |
| 0.6996        | 12.0  | 300  | 0.6943          | 0.4949   | 0.3580 | 0.1370 | 0.3630 | 0.1420 |
| 0.6995        | 12.8  | 320  | 0.6940          | 0.5032   | 0.3188 | 0.1843 | 0.3157 | 0.1812 |
| 0.6985        | 13.6  | 340  | 0.6939          | 0.5057   | 0.2330 | 0.2727 | 0.2273 | 0.2670 |
| 0.696         | 14.4  | 360  | 0.6947          | 0.5038   | 0.4211 | 0.0827 | 0.4173 | 0.0789 |
| 0.6988        | 15.2  | 380  | 0.6941          | 0.4937   | 0.2854 | 0.2083 | 0.2917 | 0.2146 |
| 0.6998        | 16.0  | 400  | 0.6942          | 0.5044   | 0.2191 | 0.2854 | 0.2146 | 0.2809 |
| 0.6971        | 16.8  | 420  | 0.6940          | 0.5063   | 0.2323 | 0.2740 | 0.2260 | 0.2677 |
| 0.704         | 17.6  | 440  | 0.6939          | 0.4937   | 0.2664 | 0.2273 | 0.2727 | 0.2336 |
| 0.695         | 18.4  | 460  | 0.6940          | 0.4956   | 0.2797 | 0.2159 | 0.2841 | 0.2203 |
| 0.6992        | 19.2  | 480  | 0.6940          | 0.4949   | 0.2872 | 0.2077 | 0.2923 | 0.2128 |
| 0.6869        | 20.0  | 500  | 0.6940          | 0.4880   | 0.3150 | 0.1730 | 0.3270 | 0.1850 |
| 0.7022        | 20.8  | 520  | 0.6939          | 0.4968   | 0.3239 | 0.1730 | 0.3270 | 0.1761 |
| 0.6961        | 21.6  | 540  | 0.6942          | 0.5019   | 0.3857 | 0.1162 | 0.3838 | 0.1143 |
| 0.7024        | 22.4  | 560  | 0.6940          | 0.4962   | 0.3453 | 0.1509 | 0.3491 | 0.1547 |
| 0.6995        | 23.2  | 580  | 0.6940          | 0.5019   | 0.3542 | 0.1477 | 0.3523 | 0.1458 |
| 0.7           | 24.0  | 600  | 0.6939          | 0.5019   | 0.3131 | 0.1888 | 0.3112 | 0.1869 |
| 0.7035        | 24.8  | 620  | 0.6939          | 0.5      | 0.3396 | 0.1604 | 0.3396 | 0.1604 |
| 0.6978        | 25.6  | 640  | 0.6938          | 0.5133   | 0.2064 | 0.3068 | 0.1932 | 0.2936 |
| 0.7032        | 26.4  | 660  | 0.6938          | 0.4981   | 0.2399 | 0.2582 | 0.2418 | 0.2601 |
| 0.6898        | 27.2  | 680  | 0.6938          | 0.4956   | 0.2449 | 0.2506 | 0.2494 | 0.2551 |
| 0.6923        | 28.0  | 700  | 0.6939          | 0.4924   | 0.2929 | 0.1995 | 0.3005 | 0.2071 |
| 0.7025        | 28.8  | 720  | 0.6938          | 0.4968   | 0.2809 | 0.2159 | 0.2841 | 0.2191 |
| 0.6999        | 29.6  | 740  | 0.6943          | 0.5025   | 0.3838 | 0.1187 | 0.3813 | 0.1162 |
| 0.6993        | 30.4  | 760  | 0.6940          | 0.5006   | 0.3573 | 0.1433 | 0.3567 | 0.1427 |
| 0.7013        | 31.2  | 780  | 0.6939          | 0.4968   | 0.3333 | 0.1635 | 0.3365 | 0.1667 |
| 0.6985        | 32.0  | 800  | 0.6939          | 0.4981   | 0.3460 | 0.1521 | 0.3479 | 0.1540 |
| 0.6957        | 32.8  | 820  | 0.6938          | 0.5076   | 0.3277 | 0.1799 | 0.3201 | 0.1723 |
| 0.6978        | 33.6  | 840  | 0.6937          | 0.5107   | 0.2475 | 0.2633 | 0.2367 | 0.2525 |
| 0.6953        | 34.4  | 860  | 0.6938          | 0.5133   | 0.3201 | 0.1932 | 0.3068 | 0.1799 |
| 0.6923        | 35.2  | 880  | 0.6939          | 0.5      | 0.3485 | 0.1515 | 0.3485 | 0.1515 |
| 0.6947        | 36.0  | 900  | 0.6939          | 0.4987   | 0.3491 | 0.1496 | 0.3504 | 0.1509 |
| 0.7018        | 36.8  | 920  | 0.6937          | 0.5095   | 0.3018 | 0.2077 | 0.2923 | 0.1982 |
| 0.6957        | 37.6  | 940  | 0.6939          | 0.4975   | 0.3403 | 0.1572 | 0.3428 | 0.1597 |
| 0.698         | 38.4  | 960  | 0.6939          | 0.4981   | 0.3415 | 0.1566 | 0.3434 | 0.1585 |
| 0.6902        | 39.2  | 980  | 0.6938          | 0.4968   | 0.3289 | 0.1679 | 0.3321 | 0.1711 |
| 0.6973        | 40.0  | 1000 | 0.6938          | 0.4956   | 0.3346 | 0.1610 | 0.3390 | 0.1654 |
| 0.6962        | 40.8  | 1020 | 0.6938          | 0.5025   | 0.3194 | 0.1831 | 0.3169 | 0.1806 |
| 0.699         | 41.6  | 1040 | 0.6937          | 0.5019   | 0.3093 | 0.1926 | 0.3074 | 0.1907 |
| 0.6965        | 42.4  | 1060 | 0.6937          | 0.5013   | 0.2816 | 0.2197 | 0.2803 | 0.2184 |
| 0.694         | 43.2  | 1080 | 0.6937          | 0.5013   | 0.2677 | 0.2336 | 0.2664 | 0.2323 |
| 0.699         | 44.0  | 1100 | 0.6937          | 0.4962   | 0.2664 | 0.2298 | 0.2702 | 0.2336 |
| 0.6932        | 44.8  | 1120 | 0.6937          | 0.4975   | 0.2645 | 0.2330 | 0.2670 | 0.2355 |
| 0.6994        | 45.6  | 1140 | 0.6937          | 0.5006   | 0.2696 | 0.2311 | 0.2689 | 0.2304 |
| 0.7008        | 46.4  | 1160 | 0.6937          | 0.5095   | 0.2992 | 0.2102 | 0.2898 | 0.2008 |
| 0.7006        | 47.2  | 1180 | 0.6937          | 0.5082   | 0.3049 | 0.2033 | 0.2967 | 0.1951 |
| 0.6954        | 48.0  | 1200 | 0.6937          | 0.5101   | 0.3030 | 0.2071 | 0.2929 | 0.1970 |
| 0.7044        | 48.8  | 1220 | 0.6937          | 0.5088   | 0.3030 | 0.2058 | 0.2942 | 0.1970 |
| 0.6993        | 49.6  | 1240 | 0.6937          | 0.5088   | 0.3011 | 0.2077 | 0.2923 | 0.1989 |


### 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_0_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\_0\_winobias\_classifieronly
============================================


This model is a fine-tuned version of google/multiberts-seed\_0 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6937
* Accuracy: 0.5088
* Tp: 0.3011
* Tn: 0.2077
* Fp: 0.2923
* Fn: 0.1989


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