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# gpt2_stereoset_classifieronly

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5990
- Accuracy: 0.6923
- Tp: 0.3501
- Tn: 0.3422
- Fp: 0.1625
- Fn: 0.1452

## 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: 0.0005
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.8922        | 0.43  | 20   | 0.6913          | 0.5549   | 0.2402 | 0.3148 | 0.1900 | 0.2551 |
| 0.7884        | 0.85  | 40   | 0.6671          | 0.5934   | 0.2182 | 0.3752 | 0.1295 | 0.2771 |
| 0.6991        | 1.28  | 60   | 0.6561          | 0.6193   | 0.2206 | 0.3987 | 0.1060 | 0.2747 |
| 0.6819        | 1.7   | 80   | 0.6499          | 0.6311   | 0.2088 | 0.4223 | 0.0824 | 0.2865 |
| 0.6501        | 2.13  | 100  | 0.6379          | 0.6507   | 0.2991 | 0.3516 | 0.1531 | 0.1962 |
| 0.6566        | 2.55  | 120  | 0.6569          | 0.6185   | 0.1695 | 0.4490 | 0.0557 | 0.3257 |
| 0.6671        | 2.98  | 140  | 0.6313          | 0.6609   | 0.2943 | 0.3666 | 0.1381 | 0.2009 |
| 0.6551        | 3.4   | 160  | 0.6309          | 0.6484   | 0.3862 | 0.2622 | 0.2425 | 0.1091 |
| 0.633         | 3.83  | 180  | 0.6244          | 0.6656   | 0.3014 | 0.3642 | 0.1405 | 0.1939 |
| 0.6432        | 4.26  | 200  | 0.6320          | 0.6554   | 0.2402 | 0.4152 | 0.0895 | 0.2551 |
| 0.6326        | 4.68  | 220  | 0.6240          | 0.6601   | 0.2849 | 0.3752 | 0.1295 | 0.2104 |
| 0.6347        | 5.11  | 240  | 0.6259          | 0.6523   | 0.3689 | 0.2834 | 0.2214 | 0.1264 |
| 0.6204        | 5.53  | 260  | 0.6256          | 0.6499   | 0.3697 | 0.2802 | 0.2245 | 0.1256 |
| 0.6242        | 5.96  | 280  | 0.6172          | 0.6774   | 0.3210 | 0.3564 | 0.1484 | 0.1743 |
| 0.6189        | 6.38  | 300  | 0.6186          | 0.6546   | 0.3493 | 0.3053 | 0.1994 | 0.1460 |
| 0.625         | 6.81  | 320  | 0.6187          | 0.6727   | 0.2881 | 0.3846 | 0.1201 | 0.2072 |
| 0.5963        | 7.23  | 340  | 0.6173          | 0.6758   | 0.3571 | 0.3187 | 0.1860 | 0.1381 |
| 0.6214        | 7.66  | 360  | 0.6158          | 0.6695   | 0.3203 | 0.3493 | 0.1554 | 0.1750 |
| 0.6007        | 8.09  | 380  | 0.6123          | 0.6797   | 0.3611 | 0.3187 | 0.1860 | 0.1342 |
| 0.6454        | 8.51  | 400  | 0.6168          | 0.6570   | 0.3736 | 0.2834 | 0.2214 | 0.1217 |
| 0.6012        | 8.94  | 420  | 0.6115          | 0.6868   | 0.3320 | 0.3548 | 0.1499 | 0.1633 |
| 0.627         | 9.36  | 440  | 0.6485          | 0.6193   | 0.1656 | 0.4537 | 0.0510 | 0.3297 |
| 0.6213        | 9.79  | 460  | 0.6092          | 0.6829   | 0.3022 | 0.3807 | 0.1240 | 0.1931 |
| 0.6286        | 10.21 | 480  | 0.6109          | 0.6711   | 0.3603 | 0.3108 | 0.1939 | 0.1350 |
| 0.609         | 10.64 | 500  | 0.6134          | 0.6633   | 0.3611 | 0.3022 | 0.2025 | 0.1342 |
| 0.5958        | 11.06 | 520  | 0.6409          | 0.6248   | 0.4262 | 0.1986 | 0.3061 | 0.0691 |
| 0.6494        | 11.49 | 540  | 0.6332          | 0.6342   | 0.4192 | 0.2151 | 0.2896 | 0.0761 |
| 0.6012        | 11.91 | 560  | 0.6159          | 0.6593   | 0.3885 | 0.2708 | 0.2339 | 0.1068 |
| 0.606         | 12.34 | 580  | 0.6050          | 0.6947   | 0.3359 | 0.3587 | 0.1460 | 0.1593 |
| 0.5872        | 12.77 | 600  | 0.6135          | 0.6641   | 0.3878 | 0.2763 | 0.2284 | 0.1075 |
| 0.6026        | 13.19 | 620  | 0.6061          | 0.6962   | 0.3265 | 0.3697 | 0.1350 | 0.1688 |
| 0.6179        | 13.62 | 640  | 0.6118          | 0.6876   | 0.2826 | 0.4050 | 0.0997 | 0.2127 |
| 0.5744        | 14.04 | 660  | 0.6058          | 0.6923   | 0.3030 | 0.3893 | 0.1154 | 0.1923 |
| 0.6061        | 14.47 | 680  | 0.6072          | 0.6860   | 0.2849 | 0.4011 | 0.1036 | 0.2104 |
| 0.609         | 14.89 | 700  | 0.6025          | 0.7064   | 0.3367 | 0.3697 | 0.1350 | 0.1586 |
| 0.6019        | 15.32 | 720  | 0.6046          | 0.6876   | 0.3540 | 0.3336 | 0.1711 | 0.1413 |
| 0.6183        | 15.74 | 740  | 0.6087          | 0.6735   | 0.3791 | 0.2943 | 0.2104 | 0.1162 |
| 0.6173        | 16.17 | 760  | 0.6010          | 0.6954   | 0.3407 | 0.3548 | 0.1499 | 0.1546 |
| 0.5873        | 16.6  | 780  | 0.6078          | 0.6766   | 0.3815 | 0.2951 | 0.2096 | 0.1138 |
| 0.6095        | 17.02 | 800  | 0.6151          | 0.6625   | 0.3948 | 0.2677 | 0.2370 | 0.1005 |
| 0.5936        | 17.45 | 820  | 0.6026          | 0.6915   | 0.3469 | 0.3446 | 0.1601 | 0.1484 |
| 0.5821        | 17.87 | 840  | 0.6025          | 0.6931   | 0.3485 | 0.3446 | 0.1601 | 0.1468 |
| 0.6036        | 18.3  | 860  | 0.6032          | 0.7049   | 0.3391 | 0.3658 | 0.1389 | 0.1562 |
| 0.5872        | 18.72 | 880  | 0.6057          | 0.6813   | 0.3587 | 0.3226 | 0.1821 | 0.1366 |
| 0.6085        | 19.15 | 900  | 0.6045          | 0.6845   | 0.3571 | 0.3273 | 0.1774 | 0.1381 |
| 0.5972        | 19.57 | 920  | 0.6203          | 0.6562   | 0.4042 | 0.2520 | 0.2527 | 0.0911 |
| 0.5732        | 20.0  | 940  | 0.6095          | 0.6672   | 0.3807 | 0.2865 | 0.2182 | 0.1146 |
| 0.5718        | 20.43 | 960  | 0.6054          | 0.6868   | 0.2936 | 0.3932 | 0.1115 | 0.2017 |
| 0.5919        | 20.85 | 980  | 0.6031          | 0.6931   | 0.3501 | 0.3430 | 0.1617 | 0.1452 |
| 0.6175        | 21.28 | 1000 | 0.6088          | 0.6703   | 0.3823 | 0.2881 | 0.2166 | 0.1130 |
| 0.5793        | 21.7  | 1020 | 0.5986          | 0.6994   | 0.3430 | 0.3564 | 0.1484 | 0.1523 |
| 0.5943        | 22.13 | 1040 | 0.6064          | 0.6852   | 0.2826 | 0.4027 | 0.1020 | 0.2127 |
| 0.5716        | 22.55 | 1060 | 0.5996          | 0.6947   | 0.3485 | 0.3462 | 0.1586 | 0.1468 |
| 0.6115        | 22.98 | 1080 | 0.6111          | 0.6727   | 0.3893 | 0.2834 | 0.2214 | 0.1060 |
| 0.5984        | 23.4  | 1100 | 0.6058          | 0.6837   | 0.3807 | 0.3030 | 0.2017 | 0.1146 |
| 0.5882        | 23.83 | 1120 | 0.5993          | 0.6962   | 0.3352 | 0.3611 | 0.1436 | 0.1601 |
| 0.5924        | 24.26 | 1140 | 0.6128          | 0.6680   | 0.3909 | 0.2771 | 0.2276 | 0.1044 |
| 0.5984        | 24.68 | 1160 | 0.6017          | 0.6970   | 0.3242 | 0.3728 | 0.1319 | 0.1711 |
| 0.5781        | 25.11 | 1180 | 0.6018          | 0.7002   | 0.3352 | 0.3650 | 0.1397 | 0.1601 |
| 0.5937        | 25.53 | 1200 | 0.6051          | 0.6845   | 0.3619 | 0.3226 | 0.1821 | 0.1334 |
| 0.5678        | 25.96 | 1220 | 0.5998          | 0.7002   | 0.3297 | 0.3705 | 0.1342 | 0.1656 |
| 0.5776        | 26.38 | 1240 | 0.6202          | 0.6523   | 0.3972 | 0.2551 | 0.2496 | 0.0981 |
| 0.5891        | 26.81 | 1260 | 0.6080          | 0.6821   | 0.3791 | 0.3030 | 0.2017 | 0.1162 |
| 0.5915        | 27.23 | 1280 | 0.6026          | 0.6947   | 0.2998 | 0.3948 | 0.1099 | 0.1954 |
| 0.5972        | 27.66 | 1300 | 0.5994          | 0.6931   | 0.3556 | 0.3375 | 0.1672 | 0.1397 |
| 0.5721        | 28.09 | 1320 | 0.6038          | 0.6829   | 0.3736 | 0.3093 | 0.1954 | 0.1217 |
| 0.5813        | 28.51 | 1340 | 0.5981          | 0.6954   | 0.3367 | 0.3587 | 0.1460 | 0.1586 |
| 0.5914        | 28.94 | 1360 | 0.5982          | 0.6986   | 0.3367 | 0.3619 | 0.1429 | 0.1586 |
| 0.5848        | 29.36 | 1380 | 0.5977          | 0.7002   | 0.3399 | 0.3603 | 0.1444 | 0.1554 |
| 0.5772        | 29.79 | 1400 | 0.6024          | 0.6876   | 0.3673 | 0.3203 | 0.1845 | 0.1279 |
| 0.581         | 30.21 | 1420 | 0.6004          | 0.6939   | 0.3611 | 0.3328 | 0.1719 | 0.1342 |
| 0.5881        | 30.64 | 1440 | 0.5969          | 0.7002   | 0.3462 | 0.3540 | 0.1507 | 0.1491 |
| 0.601         | 31.06 | 1460 | 0.5970          | 0.6994   | 0.3328 | 0.3666 | 0.1381 | 0.1625 |
| 0.5759        | 31.49 | 1480 | 0.5971          | 0.6986   | 0.3375 | 0.3611 | 0.1436 | 0.1578 |
| 0.5738        | 31.91 | 1500 | 0.5969          | 0.7002   | 0.3454 | 0.3548 | 0.1499 | 0.1499 |
| 0.5576        | 32.34 | 1520 | 0.5983          | 0.6931   | 0.3493 | 0.3438 | 0.1609 | 0.1460 |
| 0.58          | 32.77 | 1540 | 0.5976          | 0.7009   | 0.3359 | 0.3650 | 0.1397 | 0.1593 |
| 0.5798        | 33.19 | 1560 | 0.5980          | 0.7017   | 0.3469 | 0.3548 | 0.1499 | 0.1484 |
| 0.5802        | 33.62 | 1580 | 0.5988          | 0.6954   | 0.3477 | 0.3477 | 0.1570 | 0.1476 |
| 0.587         | 34.04 | 1600 | 0.5997          | 0.6931   | 0.3532 | 0.3399 | 0.1648 | 0.1421 |
| 0.5499        | 34.47 | 1620 | 0.6081          | 0.6797   | 0.3830 | 0.2967 | 0.2080 | 0.1122 |
| 0.5878        | 34.89 | 1640 | 0.5989          | 0.6970   | 0.3438 | 0.3532 | 0.1515 | 0.1515 |
| 0.5855        | 35.32 | 1660 | 0.6073          | 0.6829   | 0.3815 | 0.3014 | 0.2033 | 0.1138 |
| 0.5836        | 35.74 | 1680 | 0.5977          | 0.7002   | 0.3359 | 0.3642 | 0.1405 | 0.1593 |
| 0.5576        | 36.17 | 1700 | 0.5984          | 0.6986   | 0.3399 | 0.3587 | 0.1460 | 0.1554 |
| 0.5929        | 36.6  | 1720 | 0.6035          | 0.6907   | 0.3697 | 0.3210 | 0.1837 | 0.1256 |
| 0.5672        | 37.02 | 1740 | 0.6023          | 0.6923   | 0.3705 | 0.3218 | 0.1829 | 0.1248 |
| 0.5774        | 37.45 | 1760 | 0.5986          | 0.6947   | 0.3509 | 0.3438 | 0.1609 | 0.1444 |
| 0.5785        | 37.87 | 1780 | 0.5990          | 0.6962   | 0.3195 | 0.3768 | 0.1279 | 0.1758 |
| 0.5885        | 38.3  | 1800 | 0.5979          | 0.6994   | 0.3375 | 0.3619 | 0.1429 | 0.1578 |
| 0.5449        | 38.72 | 1820 | 0.6030          | 0.6923   | 0.3713 | 0.3210 | 0.1837 | 0.1240 |
| 0.5857        | 39.15 | 1840 | 0.5990          | 0.7009   | 0.3328 | 0.3681 | 0.1366 | 0.1625 |
| 0.5839        | 39.57 | 1860 | 0.6003          | 0.6907   | 0.3548 | 0.3359 | 0.1688 | 0.1405 |
| 0.5806        | 40.0  | 1880 | 0.5976          | 0.6962   | 0.3414 | 0.3548 | 0.1499 | 0.1538 |
| 0.5692        | 40.43 | 1900 | 0.5976          | 0.7025   | 0.3399 | 0.3626 | 0.1421 | 0.1554 |
| 0.593         | 40.85 | 1920 | 0.5984          | 0.6947   | 0.3430 | 0.3516 | 0.1531 | 0.1523 |
| 0.5736        | 41.28 | 1940 | 0.5992          | 0.6931   | 0.3556 | 0.3375 | 0.1672 | 0.1397 |
| 0.5653        | 41.7  | 1960 | 0.5978          | 0.6970   | 0.3438 | 0.3532 | 0.1515 | 0.1515 |
| 0.5631        | 42.13 | 1980 | 0.6006          | 0.6947   | 0.3603 | 0.3344 | 0.1703 | 0.1350 |
| 0.5794        | 42.55 | 2000 | 0.5983          | 0.6994   | 0.3336 | 0.3658 | 0.1389 | 0.1617 |
| 0.5876        | 42.98 | 2020 | 0.5984          | 0.6939   | 0.3422 | 0.3516 | 0.1531 | 0.1531 |
| 0.5726        | 43.4  | 2040 | 0.6005          | 0.6962   | 0.3634 | 0.3328 | 0.1719 | 0.1319 |
| 0.566         | 43.83 | 2060 | 0.5982          | 0.6970   | 0.3242 | 0.3728 | 0.1319 | 0.1711 |
| 0.5603        | 44.26 | 2080 | 0.5994          | 0.6947   | 0.3579 | 0.3367 | 0.1680 | 0.1374 |
| 0.5697        | 44.68 | 2100 | 0.6037          | 0.6892   | 0.3728 | 0.3163 | 0.1884 | 0.1224 |
| 0.5624        | 45.11 | 2120 | 0.5981          | 0.7002   | 0.3297 | 0.3705 | 0.1342 | 0.1656 |
| 0.5648        | 45.53 | 2140 | 0.5979          | 0.6962   | 0.3422 | 0.3540 | 0.1507 | 0.1531 |
| 0.578         | 45.96 | 2160 | 0.6024          | 0.6907   | 0.3713 | 0.3195 | 0.1852 | 0.1240 |
| 0.5593        | 46.38 | 2180 | 0.5977          | 0.7002   | 0.3391 | 0.3611 | 0.1436 | 0.1562 |
| 0.5755        | 46.81 | 2200 | 0.5979          | 0.6978   | 0.3336 | 0.3642 | 0.1405 | 0.1617 |
| 0.59          | 47.23 | 2220 | 0.6046          | 0.6868   | 0.3736 | 0.3132 | 0.1915 | 0.1217 |
| 0.5648        | 47.66 | 2240 | 0.5997          | 0.6931   | 0.3564 | 0.3367 | 0.1680 | 0.1389 |
| 0.5812        | 48.09 | 2260 | 0.5979          | 0.6954   | 0.3336 | 0.3619 | 0.1429 | 0.1617 |
| 0.5796        | 48.51 | 2280 | 0.5979          | 0.6962   | 0.3336 | 0.3626 | 0.1421 | 0.1617 |
| 0.5701        | 48.94 | 2300 | 0.5981          | 0.6947   | 0.3454 | 0.3493 | 0.1554 | 0.1499 |
| 0.5807        | 49.36 | 2320 | 0.5988          | 0.6931   | 0.3501 | 0.3430 | 0.1617 | 0.1452 |
| 0.5836        | 49.79 | 2340 | 0.5990          | 0.6923   | 0.3501 | 0.3422 | 0.1625 | 0.1452 |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["stereoset"], "metrics": ["accuracy"], "model-index": [{"name": "gpt2_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": 0.6923076923076923, "name": "Accuracy"}]}]}]}
transformers pytorch gpt2 text-classification generated_from_trainer dataset:stereoset license:mit model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-classification #generated_from_trainer #dataset-stereoset #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gpt2\_stereoset\_classifieronly
===============================


This model is a fine-tuned version of gpt2 on the stereoset dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5990
* Accuracy: 0.6923
* Tp: 0.3501
* Tn: 0.3422
* Fp: 0.1625
* Fn: 0.1452


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: 0.0005
* 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: 0.0005
* 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 #gpt2 #text-classification #generated_from_trainer #dataset-stereoset #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* 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 #gpt2 #text-classification #generated_from_trainer #dataset-stereoset #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* 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