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# multiberts-seed_1_winobias_classifieronly

This model is a fine-tuned version of [google/multiberts-seed_1](https://huggingface.co/google/multiberts-seed_1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6936
- Accuracy: 0.5114
- Tp: 0.2734
- Tn: 0.2380
- Fp: 0.2620
- Fn: 0.2266

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.7029        | 0.8   | 20   | 0.6948          | 0.5019   | 0.1951 | 0.3068 | 0.1932 | 0.3049 |
| 0.6937        | 1.6   | 40   | 0.6952          | 0.4931   | 0.3390 | 0.1540 | 0.3460 | 0.1610 |
| 0.6974        | 2.4   | 60   | 0.6954          | 0.4937   | 0.3567 | 0.1370 | 0.3630 | 0.1433 |
| 0.7041        | 3.2   | 80   | 0.6946          | 0.5051   | 0.2191 | 0.2860 | 0.2140 | 0.2809 |
| 0.6975        | 4.0   | 100  | 0.6947          | 0.5013   | 0.1799 | 0.3213 | 0.1787 | 0.3201 |
| 0.6996        | 4.8   | 120  | 0.6948          | 0.5025   | 0.1521 | 0.3504 | 0.1496 | 0.3479 |
| 0.7008        | 5.6   | 140  | 0.6944          | 0.4975   | 0.2841 | 0.2134 | 0.2866 | 0.2159 |
| 0.7004        | 6.4   | 160  | 0.6943          | 0.4968   | 0.1850 | 0.3119 | 0.1881 | 0.3150 |
| 0.6913        | 7.2   | 180  | 0.6944          | 0.4924   | 0.1553 | 0.3371 | 0.1629 | 0.3447 |
| 0.703         | 8.0   | 200  | 0.6941          | 0.5025   | 0.2784 | 0.2241 | 0.2759 | 0.2216 |
| 0.6975        | 8.8   | 220  | 0.6941          | 0.4987   | 0.2197 | 0.2790 | 0.2210 | 0.2803 |
| 0.6964        | 9.6   | 240  | 0.6942          | 0.4949   | 0.2058 | 0.2891 | 0.2109 | 0.2942 |
| 0.692         | 10.4  | 260  | 0.6943          | 0.4949   | 0.3037 | 0.1913 | 0.3087 | 0.1963 |
| 0.6939        | 11.2  | 280  | 0.6943          | 0.4987   | 0.1900 | 0.3087 | 0.1913 | 0.3100 |
| 0.7043        | 12.0  | 300  | 0.6942          | 0.5044   | 0.2551 | 0.2494 | 0.2506 | 0.2449 |
| 0.7036        | 12.8  | 320  | 0.6942          | 0.4912   | 0.2102 | 0.2809 | 0.2191 | 0.2898 |
| 0.697         | 13.6  | 340  | 0.6943          | 0.4975   | 0.1604 | 0.3371 | 0.1629 | 0.3396 |
| 0.7028        | 14.4  | 360  | 0.6950          | 0.5032   | 0.3939 | 0.1092 | 0.3908 | 0.1061 |
| 0.7012        | 15.2  | 380  | 0.6940          | 0.4962   | 0.2045 | 0.2917 | 0.2083 | 0.2955 |
| 0.6976        | 16.0  | 400  | 0.6940          | 0.4968   | 0.2102 | 0.2866 | 0.2134 | 0.2898 |
| 0.695         | 16.8  | 420  | 0.6944          | 0.5095   | 0.1452 | 0.3643 | 0.1357 | 0.3548 |
| 0.6985        | 17.6  | 440  | 0.6939          | 0.5013   | 0.2210 | 0.2803 | 0.2197 | 0.2790 |
| 0.6946        | 18.4  | 460  | 0.6939          | 0.5032   | 0.2765 | 0.2266 | 0.2734 | 0.2235 |
| 0.6975        | 19.2  | 480  | 0.6940          | 0.4962   | 0.1749 | 0.3213 | 0.1787 | 0.3251 |
| 0.6958        | 20.0  | 500  | 0.6939          | 0.4905   | 0.2058 | 0.2847 | 0.2153 | 0.2942 |
| 0.6947        | 20.8  | 520  | 0.6938          | 0.5057   | 0.2771 | 0.2285 | 0.2715 | 0.2229 |
| 0.7044        | 21.6  | 540  | 0.6940          | 0.5019   | 0.2986 | 0.2033 | 0.2967 | 0.2014 |
| 0.698         | 22.4  | 560  | 0.6941          | 0.4918   | 0.3201 | 0.1717 | 0.3283 | 0.1799 |
| 0.7016        | 23.2  | 580  | 0.6939          | 0.5076   | 0.2771 | 0.2304 | 0.2696 | 0.2229 |
| 0.7029        | 24.0  | 600  | 0.6939          | 0.5063   | 0.2765 | 0.2298 | 0.2702 | 0.2235 |
| 0.6975        | 24.8  | 620  | 0.6938          | 0.5025   | 0.2904 | 0.2121 | 0.2879 | 0.2096 |
| 0.6966        | 25.6  | 640  | 0.6940          | 0.5032   | 0.1660 | 0.3371 | 0.1629 | 0.3340 |
| 0.6974        | 26.4  | 660  | 0.6938          | 0.4994   | 0.1926 | 0.3068 | 0.1932 | 0.3074 |
| 0.6998        | 27.2  | 680  | 0.6938          | 0.5013   | 0.2229 | 0.2784 | 0.2216 | 0.2771 |
| 0.6899        | 28.0  | 700  | 0.6937          | 0.5082   | 0.25   | 0.2582 | 0.2418 | 0.25   |
| 0.6954        | 28.8  | 720  | 0.6937          | 0.4968   | 0.2109 | 0.2860 | 0.2140 | 0.2891 |
| 0.6926        | 29.6  | 740  | 0.6941          | 0.4899   | 0.3479 | 0.1420 | 0.3580 | 0.1521 |
| 0.6936        | 30.4  | 760  | 0.6938          | 0.5006   | 0.2822 | 0.2184 | 0.2816 | 0.2178 |
| 0.6911        | 31.2  | 780  | 0.6937          | 0.5057   | 0.2519 | 0.2538 | 0.2462 | 0.2481 |
| 0.69          | 32.0  | 800  | 0.6938          | 0.5038   | 0.2904 | 0.2134 | 0.2866 | 0.2096 |
| 0.6953        | 32.8  | 820  | 0.6937          | 0.5051   | 0.2765 | 0.2285 | 0.2715 | 0.2235 |
| 0.6971        | 33.6  | 840  | 0.6937          | 0.4956   | 0.2020 | 0.2936 | 0.2064 | 0.2980 |
| 0.6983        | 34.4  | 860  | 0.6937          | 0.5025   | 0.2727 | 0.2298 | 0.2702 | 0.2273 |
| 0.698         | 35.2  | 880  | 0.6938          | 0.4987   | 0.3024 | 0.1963 | 0.3037 | 0.1976 |
| 0.6949        | 36.0  | 900  | 0.6938          | 0.5032   | 0.3081 | 0.1951 | 0.3049 | 0.1919 |
| 0.6969        | 36.8  | 920  | 0.6937          | 0.5082   | 0.2885 | 0.2197 | 0.2803 | 0.2115 |
| 0.6978        | 37.6  | 940  | 0.6937          | 0.5088   | 0.3087 | 0.2001 | 0.2999 | 0.1913 |
| 0.6965        | 38.4  | 960  | 0.6936          | 0.5088   | 0.2588 | 0.25   | 0.25   | 0.2412 |
| 0.6929        | 39.2  | 980  | 0.6936          | 0.5101   | 0.2620 | 0.2481 | 0.2519 | 0.2380 |
| 0.6967        | 40.0  | 1000 | 0.6936          | 0.5101   | 0.2702 | 0.2399 | 0.2601 | 0.2298 |
| 0.6971        | 40.8  | 1020 | 0.6936          | 0.5069   | 0.2431 | 0.2639 | 0.2361 | 0.2569 |
| 0.6976        | 41.6  | 1040 | 0.6936          | 0.5063   | 0.2418 | 0.2645 | 0.2355 | 0.2582 |
| 0.6989        | 42.4  | 1060 | 0.6936          | 0.5038   | 0.2304 | 0.2734 | 0.2266 | 0.2696 |
| 0.6995        | 43.2  | 1080 | 0.6936          | 0.5019   | 0.2254 | 0.2765 | 0.2235 | 0.2746 |
| 0.6981        | 44.0  | 1100 | 0.6936          | 0.5069   | 0.2386 | 0.2683 | 0.2317 | 0.2614 |
| 0.6914        | 44.8  | 1120 | 0.6936          | 0.5095   | 0.25   | 0.2595 | 0.2405 | 0.25   |
| 0.6936        | 45.6  | 1140 | 0.6936          | 0.5095   | 0.25   | 0.2595 | 0.2405 | 0.25   |
| 0.6951        | 46.4  | 1160 | 0.6936          | 0.5107   | 0.2734 | 0.2374 | 0.2626 | 0.2266 |
| 0.6964        | 47.2  | 1180 | 0.6936          | 0.5114   | 0.2854 | 0.2260 | 0.2740 | 0.2146 |
| 0.7004        | 48.0  | 1200 | 0.6936          | 0.5114   | 0.2822 | 0.2292 | 0.2708 | 0.2178 |
| 0.696         | 48.8  | 1220 | 0.6936          | 0.5088   | 0.2759 | 0.2330 | 0.2670 | 0.2241 |
| 0.6966        | 49.6  | 1240 | 0.6936          | 0.5114   | 0.2734 | 0.2380 | 0.2620 | 0.2266 |


### 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_1_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\_1\_winobias\_classifieronly
============================================


This model is a fine-tuned version of google/multiberts-seed\_1 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6936
* Accuracy: 0.5114
* Tp: 0.2734
* Tn: 0.2380
* Fp: 0.2620
* Fn: 0.2266


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