
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# multiberts-seed_0_stereoset_classifieronly

This model is a fine-tuned version of [google/multiberts-seed_0](https://huggingface.co/google/multiberts-seed_0) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6824
- Accuracy: 0.5856
- Tp: 0.3414
- Tn: 0.2441
- Fp: 0.2316
- Fn: 0.1829

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.7177        | 0.43  | 20   | 0.7071          | 0.4584   | 0.1028 | 0.3556 | 0.1201 | 0.4215 |
| 0.7096        | 0.85  | 40   | 0.6953          | 0.4867   | 0.3234 | 0.1633 | 0.3124 | 0.2009 |
| 0.7126        | 1.28  | 60   | 0.6988          | 0.4702   | 0.0573 | 0.4129 | 0.0628 | 0.4670 |
| 0.7012        | 1.7   | 80   | 0.6919          | 0.5141   | 0.2794 | 0.2347 | 0.2410 | 0.2449 |
| 0.7016        | 2.13  | 100  | 0.6881          | 0.5471   | 0.4466 | 0.1005 | 0.3752 | 0.0777 |
| 0.7027        | 2.55  | 120  | 0.6913          | 0.5204   | 0.2520 | 0.2684 | 0.2072 | 0.2724 |
| 0.6918        | 2.98  | 140  | 0.6894          | 0.5526   | 0.3564 | 0.1962 | 0.2794 | 0.1680 |
| 0.697         | 3.4   | 160  | 0.6886          | 0.5628   | 0.3807 | 0.1821 | 0.2936 | 0.1436 |
| 0.696         | 3.83  | 180  | 0.6876          | 0.5526   | 0.4066 | 0.1460 | 0.3297 | 0.1177 |
| 0.7047        | 4.26  | 200  | 0.6936          | 0.4984   | 0.1099 | 0.3885 | 0.0871 | 0.4144 |
| 0.6945        | 4.68  | 220  | 0.6884          | 0.5628   | 0.3713 | 0.1915 | 0.2841 | 0.1531 |
| 0.7051        | 5.11  | 240  | 0.6893          | 0.5518   | 0.3140 | 0.2378 | 0.2378 | 0.2104 |
| 0.6889        | 5.53  | 260  | 0.6869          | 0.5581   | 0.3901 | 0.1680 | 0.3077 | 0.1342 |
| 0.7033        | 5.96  | 280  | 0.6872          | 0.5612   | 0.3799 | 0.1813 | 0.2943 | 0.1444 |
| 0.7039        | 6.38  | 300  | 0.6904          | 0.5330   | 0.2096 | 0.3234 | 0.1523 | 0.3148 |
| 0.6945        | 6.81  | 320  | 0.6861          | 0.5573   | 0.4105 | 0.1468 | 0.3289 | 0.1138 |
| 0.6969        | 7.23  | 340  | 0.6899          | 0.5526   | 0.2575 | 0.2951 | 0.1805 | 0.2669 |
| 0.6951        | 7.66  | 360  | 0.6859          | 0.5573   | 0.4105 | 0.1468 | 0.3289 | 0.1138 |
| 0.6901        | 8.09  | 380  | 0.6903          | 0.5377   | 0.2057 | 0.3320 | 0.1436 | 0.3187 |
| 0.6839        | 8.51  | 400  | 0.6865          | 0.5644   | 0.3870 | 0.1774 | 0.2983 | 0.1374 |
| 0.6965        | 8.94  | 420  | 0.6875          | 0.5683   | 0.3391 | 0.2292 | 0.2465 | 0.1852 |
| 0.6887        | 9.36  | 440  | 0.6869          | 0.5667   | 0.3257 | 0.2410 | 0.2347 | 0.1986 |
| 0.6945        | 9.79  | 460  | 0.6852          | 0.5581   | 0.3846 | 0.1735 | 0.3022 | 0.1397 |
| 0.6864        | 10.21 | 480  | 0.6861          | 0.5659   | 0.3509 | 0.2151 | 0.2606 | 0.1735 |
| 0.6935        | 10.64 | 500  | 0.6876          | 0.5628   | 0.2794 | 0.2834 | 0.1923 | 0.2449 |
| 0.6981        | 11.06 | 520  | 0.6865          | 0.5699   | 0.3250 | 0.2449 | 0.2308 | 0.1994 |
| 0.7011        | 11.49 | 540  | 0.6874          | 0.5628   | 0.2755 | 0.2873 | 0.1884 | 0.2488 |
| 0.6833        | 11.91 | 560  | 0.6842          | 0.5573   | 0.4035 | 0.1538 | 0.3218 | 0.1209 |
| 0.692         | 12.34 | 580  | 0.6913          | 0.5220   | 0.1350 | 0.3870 | 0.0887 | 0.3893 |
| 0.6902        | 12.77 | 600  | 0.6855          | 0.5683   | 0.3713 | 0.1970 | 0.2786 | 0.1531 |
| 0.6905        | 13.19 | 620  | 0.6853          | 0.5699   | 0.3736 | 0.1962 | 0.2794 | 0.1507 |
| 0.6866        | 13.62 | 640  | 0.6872          | 0.5683   | 0.2841 | 0.2841 | 0.1915 | 0.2402 |
| 0.7           | 14.04 | 660  | 0.6853          | 0.5714   | 0.3587 | 0.2127 | 0.2630 | 0.1656 |
| 0.6927        | 14.47 | 680  | 0.6869          | 0.5683   | 0.2684 | 0.2998 | 0.1758 | 0.2559 |
| 0.6891        | 14.89 | 700  | 0.6854          | 0.5683   | 0.3344 | 0.2339 | 0.2418 | 0.1900 |
| 0.684         | 15.32 | 720  | 0.6867          | 0.5691   | 0.2708 | 0.2983 | 0.1774 | 0.2535 |
| 0.6969        | 15.74 | 740  | 0.6842          | 0.5691   | 0.3854 | 0.1837 | 0.2920 | 0.1389 |
| 0.6782        | 16.17 | 760  | 0.6841          | 0.5620   | 0.3972 | 0.1648 | 0.3108 | 0.1272 |
| 0.7023        | 16.6  | 780  | 0.6868          | 0.5777   | 0.3046 | 0.2732 | 0.2025 | 0.2198 |
| 0.6979        | 17.02 | 800  | 0.6841          | 0.5722   | 0.3823 | 0.1900 | 0.2857 | 0.1421 |
| 0.6875        | 17.45 | 820  | 0.6840          | 0.5691   | 0.3846 | 0.1845 | 0.2912 | 0.1397 |
| 0.6852        | 17.87 | 840  | 0.6867          | 0.5675   | 0.2598 | 0.3077 | 0.1680 | 0.2645 |
| 0.688         | 18.3  | 860  | 0.6850          | 0.5691   | 0.3195 | 0.2496 | 0.2261 | 0.2049 |
| 0.6941        | 18.72 | 880  | 0.6858          | 0.5754   | 0.2834 | 0.2920 | 0.1837 | 0.2410 |
| 0.6942        | 19.15 | 900  | 0.6828          | 0.5667   | 0.4215 | 0.1452 | 0.3305 | 0.1028 |
| 0.6883        | 19.57 | 920  | 0.6842          | 0.5699   | 0.3438 | 0.2261 | 0.2496 | 0.1805 |
| 0.6942        | 20.0  | 940  | 0.6858          | 0.5722   | 0.2677 | 0.3046 | 0.1711 | 0.2567 |
| 0.6908        | 20.43 | 960  | 0.6827          | 0.5699   | 0.4027 | 0.1672 | 0.3085 | 0.1217 |
| 0.6857        | 20.85 | 980  | 0.6849          | 0.5777   | 0.3014 | 0.2763 | 0.1994 | 0.2229 |
| 0.7046        | 21.28 | 1000 | 0.6836          | 0.5761   | 0.3587 | 0.2174 | 0.2582 | 0.1656 |
| 0.6856        | 21.7  | 1020 | 0.6832          | 0.5691   | 0.3807 | 0.1884 | 0.2873 | 0.1436 |
| 0.6969        | 22.13 | 1040 | 0.6878          | 0.5447   | 0.1978 | 0.3469 | 0.1287 | 0.3265 |
| 0.6957        | 22.55 | 1060 | 0.6854          | 0.5769   | 0.2991 | 0.2779 | 0.1978 | 0.2253 |
| 0.6903        | 22.98 | 1080 | 0.6842          | 0.5761   | 0.3375 | 0.2386 | 0.2370 | 0.1868 |
| 0.6923        | 23.4  | 1100 | 0.6869          | 0.5683   | 0.2347 | 0.3336 | 0.1421 | 0.2896 |
| 0.7005        | 23.83 | 1120 | 0.6852          | 0.5801   | 0.3061 | 0.2739 | 0.2017 | 0.2182 |
| 0.6918        | 24.26 | 1140 | 0.6828          | 0.5722   | 0.3807 | 0.1915 | 0.2841 | 0.1436 |
| 0.701         | 24.68 | 1160 | 0.6839          | 0.5801   | 0.3367 | 0.2433 | 0.2323 | 0.1876 |
| 0.6947        | 25.11 | 1180 | 0.6831          | 0.5722   | 0.3689 | 0.2033 | 0.2724 | 0.1554 |
| 0.6941        | 25.53 | 1200 | 0.6833          | 0.5754   | 0.3571 | 0.2182 | 0.2575 | 0.1672 |
| 0.6877        | 25.96 | 1220 | 0.6836          | 0.5808   | 0.3446 | 0.2363 | 0.2394 | 0.1797 |
| 0.6891        | 26.38 | 1240 | 0.6829          | 0.5706   | 0.3673 | 0.2033 | 0.2724 | 0.1570 |
| 0.6954        | 26.81 | 1260 | 0.6834          | 0.5769   | 0.3509 | 0.2261 | 0.2496 | 0.1735 |
| 0.6854        | 27.23 | 1280 | 0.6845          | 0.5769   | 0.3140 | 0.2630 | 0.2127 | 0.2104 |
| 0.6829        | 27.66 | 1300 | 0.6866          | 0.5581   | 0.2166 | 0.3414 | 0.1342 | 0.3077 |
| 0.6936        | 28.09 | 1320 | 0.6826          | 0.5746   | 0.3768 | 0.1978 | 0.2779 | 0.1476 |
| 0.6808        | 28.51 | 1340 | 0.6831          | 0.5777   | 0.3548 | 0.2229 | 0.2527 | 0.1695 |
| 0.6909        | 28.94 | 1360 | 0.6836          | 0.5832   | 0.3375 | 0.2457 | 0.2300 | 0.1868 |
| 0.6863        | 29.36 | 1380 | 0.6835          | 0.5793   | 0.3430 | 0.2363 | 0.2394 | 0.1813 |
| 0.6897        | 29.79 | 1400 | 0.6825          | 0.5746   | 0.3783 | 0.1962 | 0.2794 | 0.1460 |
| 0.6889        | 30.21 | 1420 | 0.6838          | 0.5785   | 0.3273 | 0.2512 | 0.2245 | 0.1970 |
| 0.6917        | 30.64 | 1440 | 0.6828          | 0.5746   | 0.3619 | 0.2127 | 0.2630 | 0.1625 |
| 0.6953        | 31.06 | 1460 | 0.6849          | 0.5769   | 0.2786 | 0.2983 | 0.1774 | 0.2457 |
| 0.6819        | 31.49 | 1480 | 0.6868          | 0.5526   | 0.1868 | 0.3658 | 0.1099 | 0.3375 |
| 0.6915        | 31.91 | 1500 | 0.6830          | 0.5808   | 0.3383 | 0.2425 | 0.2331 | 0.1860 |
| 0.6968        | 32.34 | 1520 | 0.6815          | 0.5793   | 0.3987 | 0.1805 | 0.2951 | 0.1256 |
| 0.6816        | 32.77 | 1540 | 0.6824          | 0.5808   | 0.3587 | 0.2221 | 0.2535 | 0.1656 |
| 0.695         | 33.19 | 1560 | 0.6839          | 0.5793   | 0.2991 | 0.2802 | 0.1954 | 0.2253 |
| 0.6784        | 33.62 | 1580 | 0.6847          | 0.5801   | 0.2684 | 0.3116 | 0.1641 | 0.2559 |
| 0.688         | 34.04 | 1600 | 0.6825          | 0.5793   | 0.3548 | 0.2245 | 0.2512 | 0.1695 |
| 0.6872        | 34.47 | 1620 | 0.6835          | 0.5808   | 0.3132 | 0.2677 | 0.2080 | 0.2111 |
| 0.6975        | 34.89 | 1640 | 0.6828          | 0.5808   | 0.3469 | 0.2339 | 0.2418 | 0.1774 |
| 0.6889        | 35.32 | 1660 | 0.6837          | 0.5824   | 0.3124 | 0.2700 | 0.2057 | 0.2119 |
| 0.6873        | 35.74 | 1680 | 0.6825          | 0.5785   | 0.3611 | 0.2174 | 0.2582 | 0.1633 |
| 0.6938        | 36.17 | 1700 | 0.6825          | 0.5777   | 0.3611 | 0.2166 | 0.2590 | 0.1633 |
| 0.7051        | 36.6  | 1720 | 0.6829          | 0.5816   | 0.3422 | 0.2394 | 0.2363 | 0.1821 |
| 0.6894        | 37.02 | 1740 | 0.6822          | 0.5824   | 0.3626 | 0.2198 | 0.2559 | 0.1617 |
| 0.6987        | 37.45 | 1760 | 0.6828          | 0.5856   | 0.3414 | 0.2441 | 0.2316 | 0.1829 |
| 0.6916        | 37.87 | 1780 | 0.6835          | 0.5777   | 0.3061 | 0.2716 | 0.2041 | 0.2182 |
| 0.6835        | 38.3  | 1800 | 0.6830          | 0.5816   | 0.3234 | 0.2582 | 0.2174 | 0.2009 |
| 0.6866        | 38.72 | 1820 | 0.6832          | 0.5863   | 0.3203 | 0.2661 | 0.2096 | 0.2041 |
| 0.6856        | 39.15 | 1840 | 0.6829          | 0.5848   | 0.3320 | 0.2527 | 0.2229 | 0.1923 |
| 0.6884        | 39.57 | 1860 | 0.6821          | 0.5816   | 0.3595 | 0.2221 | 0.2535 | 0.1648 |
| 0.6833        | 40.0  | 1880 | 0.6828          | 0.5863   | 0.3352 | 0.2512 | 0.2245 | 0.1892 |
| 0.6805        | 40.43 | 1900 | 0.6826          | 0.5840   | 0.3407 | 0.2433 | 0.2323 | 0.1837 |
| 0.6941        | 40.85 | 1920 | 0.6817          | 0.5754   | 0.3681 | 0.2072 | 0.2684 | 0.1562 |
| 0.6902        | 41.28 | 1940 | 0.6821          | 0.5816   | 0.3532 | 0.2284 | 0.2473 | 0.1711 |
| 0.692         | 41.7  | 1960 | 0.6826          | 0.5863   | 0.3383 | 0.2480 | 0.2276 | 0.1860 |
| 0.6927        | 42.13 | 1980 | 0.6824          | 0.5848   | 0.3454 | 0.2394 | 0.2363 | 0.1790 |
| 0.6849        | 42.55 | 2000 | 0.6822          | 0.5793   | 0.3501 | 0.2292 | 0.2465 | 0.1743 |
| 0.6836        | 42.98 | 2020 | 0.6821          | 0.5801   | 0.3540 | 0.2261 | 0.2496 | 0.1703 |
| 0.6916        | 43.4  | 2040 | 0.6822          | 0.5824   | 0.3477 | 0.2347 | 0.2410 | 0.1766 |
| 0.6825        | 43.83 | 2060 | 0.6824          | 0.5832   | 0.3446 | 0.2386 | 0.2370 | 0.1797 |
| 0.6939        | 44.26 | 2080 | 0.6825          | 0.5863   | 0.3383 | 0.2480 | 0.2276 | 0.1860 |
| 0.6899        | 44.68 | 2100 | 0.6820          | 0.5801   | 0.3509 | 0.2292 | 0.2465 | 0.1735 |
| 0.6873        | 45.11 | 2120 | 0.6819          | 0.5801   | 0.3587 | 0.2214 | 0.2543 | 0.1656 |
| 0.696         | 45.53 | 2140 | 0.6820          | 0.5801   | 0.3564 | 0.2237 | 0.2520 | 0.1680 |
| 0.697         | 45.96 | 2160 | 0.6824          | 0.5856   | 0.3485 | 0.2370 | 0.2386 | 0.1758 |
| 0.6891        | 46.38 | 2180 | 0.6825          | 0.5848   | 0.3430 | 0.2418 | 0.2339 | 0.1813 |
| 0.6828        | 46.81 | 2200 | 0.6822          | 0.5816   | 0.3501 | 0.2316 | 0.2441 | 0.1743 |
| 0.6904        | 47.23 | 2220 | 0.6823          | 0.5848   | 0.3477 | 0.2370 | 0.2386 | 0.1766 |
| 0.6891        | 47.66 | 2240 | 0.6825          | 0.5863   | 0.3391 | 0.2473 | 0.2284 | 0.1852 |
| 0.6867        | 48.09 | 2260 | 0.6826          | 0.5871   | 0.3383 | 0.2488 | 0.2268 | 0.1860 |
| 0.688         | 48.51 | 2280 | 0.6824          | 0.5832   | 0.3430 | 0.2402 | 0.2355 | 0.1813 |
| 0.6938        | 48.94 | 2300 | 0.6824          | 0.5856   | 0.3399 | 0.2457 | 0.2300 | 0.1845 |
| 0.6823        | 49.36 | 2320 | 0.6824          | 0.5863   | 0.3407 | 0.2457 | 0.2300 | 0.1837 |
| 0.6886        | 49.79 | 2340 | 0.6824          | 0.5856   | 0.3414 | 0.2441 | 0.2316 | 0.1829 |


### 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"], "datasets": ["stereoset"], "metrics": ["accuracy"], "model-index": [{"name": "multiberts-seed_0_stereoset_classifieronly", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "stereoset", "type": "stereoset", "config": "intersentence", "split": "validation", "args": "intersentence"}, "metrics": [{"type": "accuracy", "value": 0.5855572998430141, "name": "Accuracy"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer dataset:stereoset license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-stereoset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

multiberts-seed\_0\_stereoset\_classifieronly
=============================================


This model is a fine-tuned version of google/multiberts-seed\_0 on the stereoset dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6824
* Accuracy: 0.5856
* Tp: 0.3414
* Tn: 0.2441
* Fp: 0.2316
* Fn: 0.1829


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 #dataset-stereoset #license-apache-2.0 #model-index #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 #dataset-stereoset #license-apache-2.0 #model-index #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