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# bert-large-uncased_stereoset_classifieronly

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6856
- Accuracy: 0.5479
- Tp: 0.3579
- Tn: 0.1900
- Fp: 0.3242
- Fn: 0.1279

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp     | Tn     | Fp     | Fn     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.6938        | 0.43  | 20   | 0.6891          | 0.5573   | 0.2630 | 0.2943 | 0.2198 | 0.2229 |
| 0.703         | 0.85  | 40   | 0.6942          | 0.5455   | 0.3901 | 0.1554 | 0.3587 | 0.0958 |
| 0.6929        | 1.28  | 60   | 0.6882          | 0.5510   | 0.3069 | 0.2441 | 0.2700 | 0.1790 |
| 0.7044        | 1.7   | 80   | 0.6895          | 0.5471   | 0.3603 | 0.1868 | 0.3273 | 0.1256 |
| 0.6947        | 2.13  | 100  | 0.6874          | 0.5463   | 0.3006 | 0.2457 | 0.2684 | 0.1852 |
| 0.7115        | 2.55  | 120  | 0.6910          | 0.5479   | 0.3768 | 0.1711 | 0.3430 | 0.1091 |
| 0.7016        | 2.98  | 140  | 0.6879          | 0.5518   | 0.3430 | 0.2088 | 0.3053 | 0.1429 |
| 0.6952        | 3.4   | 160  | 0.6918          | 0.5361   | 0.3980 | 0.1381 | 0.3760 | 0.0879 |
| 0.6959        | 3.83  | 180  | 0.6910          | 0.5463   | 0.3878 | 0.1586 | 0.3556 | 0.0981 |
| 0.6924        | 4.26  | 200  | 0.6906          | 0.5471   | 0.3830 | 0.1641 | 0.3501 | 0.1028 |
| 0.6943        | 4.68  | 220  | 0.6877          | 0.5487   | 0.3195 | 0.2292 | 0.2849 | 0.1664 |
| 0.7052        | 5.11  | 240  | 0.6879          | 0.5644   | 0.2473 | 0.3171 | 0.1970 | 0.2386 |
| 0.6881        | 5.53  | 260  | 0.6889          | 0.5479   | 0.3791 | 0.1688 | 0.3454 | 0.1068 |
| 0.6971        | 5.96  | 280  | 0.6882          | 0.5463   | 0.3752 | 0.1711 | 0.3430 | 0.1107 |
| 0.6781        | 6.38  | 300  | 0.6930          | 0.5330   | 0.4035 | 0.1295 | 0.3846 | 0.0824 |
| 0.6992        | 6.81  | 320  | 0.6875          | 0.5495   | 0.3579 | 0.1915 | 0.3226 | 0.1279 |
| 0.6954        | 7.23  | 340  | 0.6868          | 0.5557   | 0.3195 | 0.2363 | 0.2779 | 0.1664 |
| 0.6949        | 7.66  | 360  | 0.6877          | 0.5479   | 0.3556 | 0.1923 | 0.3218 | 0.1303 |
| 0.6946        | 8.09  | 380  | 0.6899          | 0.5471   | 0.3878 | 0.1593 | 0.3548 | 0.0981 |
| 0.6877        | 8.51  | 400  | 0.6862          | 0.5542   | 0.3218 | 0.2323 | 0.2818 | 0.1641 |
| 0.6994        | 8.94  | 420  | 0.6890          | 0.5479   | 0.3823 | 0.1656 | 0.3485 | 0.1036 |
| 0.7061        | 9.36  | 440  | 0.6867          | 0.5620   | 0.2347 | 0.3273 | 0.1868 | 0.2512 |
| 0.6945        | 9.79  | 460  | 0.6893          | 0.5479   | 0.3878 | 0.1601 | 0.3540 | 0.0981 |
| 0.7078        | 10.21 | 480  | 0.6908          | 0.5353   | 0.3972 | 0.1381 | 0.3760 | 0.0887 |
| 0.6911        | 10.64 | 500  | 0.6858          | 0.5502   | 0.3108 | 0.2394 | 0.2747 | 0.1750 |
| 0.684         | 11.06 | 520  | 0.6875          | 0.5502   | 0.3768 | 0.1735 | 0.3407 | 0.1091 |
| 0.6925        | 11.49 | 540  | 0.6906          | 0.5369   | 0.3972 | 0.1397 | 0.3744 | 0.0887 |
| 0.7104        | 11.91 | 560  | 0.6856          | 0.5597   | 0.2527 | 0.3069 | 0.2072 | 0.2331 |
| 0.6919        | 12.34 | 580  | 0.6857          | 0.5479   | 0.3391 | 0.2088 | 0.3053 | 0.1468 |
| 0.6873        | 12.77 | 600  | 0.6903          | 0.5338   | 0.3987 | 0.1350 | 0.3791 | 0.0871 |
| 0.6915        | 13.19 | 620  | 0.6862          | 0.5471   | 0.3540 | 0.1931 | 0.3210 | 0.1319 |
| 0.6921        | 13.62 | 640  | 0.6859          | 0.5518   | 0.3485 | 0.2033 | 0.3108 | 0.1374 |
| 0.7092        | 14.04 | 660  | 0.6888          | 0.5479   | 0.3807 | 0.1672 | 0.3469 | 0.1052 |
| 0.6874        | 14.47 | 680  | 0.6851          | 0.5518   | 0.3210 | 0.2308 | 0.2834 | 0.1648 |
| 0.682         | 14.89 | 700  | 0.6877          | 0.5510   | 0.3744 | 0.1766 | 0.3375 | 0.1115 |
| 0.6953        | 15.32 | 720  | 0.6853          | 0.5526   | 0.3273 | 0.2253 | 0.2889 | 0.1586 |
| 0.7056        | 15.74 | 740  | 0.6882          | 0.5487   | 0.3885 | 0.1601 | 0.3540 | 0.0973 |
| 0.6776        | 16.17 | 760  | 0.6875          | 0.5471   | 0.3783 | 0.1688 | 0.3454 | 0.1075 |
| 0.6862        | 16.6  | 780  | 0.6863          | 0.5510   | 0.3642 | 0.1868 | 0.3273 | 0.1217 |
| 0.6827        | 17.02 | 800  | 0.6868          | 0.5510   | 0.3705 | 0.1805 | 0.3336 | 0.1154 |
| 0.7161        | 17.45 | 820  | 0.6878          | 0.5502   | 0.3791 | 0.1711 | 0.3430 | 0.1068 |
| 0.6991        | 17.87 | 840  | 0.6852          | 0.5487   | 0.3359 | 0.2127 | 0.3014 | 0.1499 |
| 0.6836        | 18.3  | 860  | 0.6876          | 0.5487   | 0.3830 | 0.1656 | 0.3485 | 0.1028 |
| 0.7023        | 18.72 | 880  | 0.6862          | 0.5487   | 0.3595 | 0.1892 | 0.3250 | 0.1264 |
| 0.6939        | 19.15 | 900  | 0.6854          | 0.5495   | 0.3485 | 0.2009 | 0.3132 | 0.1374 |
| 0.6883        | 19.57 | 920  | 0.6860          | 0.5479   | 0.3587 | 0.1892 | 0.3250 | 0.1272 |
| 0.6872        | 20.0  | 940  | 0.6866          | 0.5518   | 0.3697 | 0.1821 | 0.3320 | 0.1162 |
| 0.685         | 20.43 | 960  | 0.6861          | 0.5487   | 0.3595 | 0.1892 | 0.3250 | 0.1264 |
| 0.6771        | 20.85 | 980  | 0.6853          | 0.5510   | 0.3477 | 0.2033 | 0.3108 | 0.1381 |
| 0.6904        | 21.28 | 1000 | 0.6859          | 0.5487   | 0.3564 | 0.1923 | 0.3218 | 0.1295 |
| 0.6925        | 21.7  | 1020 | 0.6848          | 0.5518   | 0.3132 | 0.2386 | 0.2755 | 0.1727 |
| 0.6982        | 22.13 | 1040 | 0.6856          | 0.5463   | 0.3532 | 0.1931 | 0.3210 | 0.1327 |
| 0.7015        | 22.55 | 1060 | 0.6859          | 0.5479   | 0.3587 | 0.1892 | 0.3250 | 0.1272 |
| 0.6851        | 22.98 | 1080 | 0.6860          | 0.5518   | 0.3650 | 0.1868 | 0.3273 | 0.1209 |
| 0.6875        | 23.4  | 1100 | 0.6856          | 0.5463   | 0.3532 | 0.1931 | 0.3210 | 0.1327 |
| 0.7035        | 23.83 | 1120 | 0.6851          | 0.5510   | 0.3454 | 0.2057 | 0.3085 | 0.1405 |
| 0.699         | 24.26 | 1140 | 0.6846          | 0.5534   | 0.3281 | 0.2253 | 0.2889 | 0.1578 |
| 0.6954        | 24.68 | 1160 | 0.6851          | 0.5495   | 0.3485 | 0.2009 | 0.3132 | 0.1374 |
| 0.6881        | 25.11 | 1180 | 0.6851          | 0.5510   | 0.3485 | 0.2025 | 0.3116 | 0.1374 |
| 0.6931        | 25.53 | 1200 | 0.6862          | 0.5487   | 0.3666 | 0.1821 | 0.3320 | 0.1193 |
| 0.6967        | 25.96 | 1220 | 0.6868          | 0.5487   | 0.3752 | 0.1735 | 0.3407 | 0.1107 |
| 0.6826        | 26.38 | 1240 | 0.6863          | 0.5502   | 0.3689 | 0.1813 | 0.3328 | 0.1170 |
| 0.6927        | 26.81 | 1260 | 0.6857          | 0.5487   | 0.3587 | 0.1900 | 0.3242 | 0.1272 |
| 0.692         | 27.23 | 1280 | 0.6853          | 0.5471   | 0.3524 | 0.1947 | 0.3195 | 0.1334 |
| 0.6936        | 27.66 | 1300 | 0.6856          | 0.5479   | 0.3579 | 0.1900 | 0.3242 | 0.1279 |
| 0.6871        | 28.09 | 1320 | 0.6856          | 0.5487   | 0.3579 | 0.1907 | 0.3234 | 0.1279 |
| 0.6956        | 28.51 | 1340 | 0.6857          | 0.5487   | 0.3595 | 0.1892 | 0.3250 | 0.1264 |
| 0.6788        | 28.94 | 1360 | 0.6859          | 0.5479   | 0.3611 | 0.1868 | 0.3273 | 0.1248 |
| 0.6933        | 29.36 | 1380 | 0.6856          | 0.5479   | 0.3579 | 0.1900 | 0.3242 | 0.1279 |
| 0.6909        | 29.79 | 1400 | 0.6856          | 0.5479   | 0.3579 | 0.1900 | 0.3242 | 0.1279 |


### 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": "bert-large-uncased_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": 0.5478806907378336, "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 

bert-large-uncased\_stereoset\_classifieronly
=============================================


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


* Loss: 0.6856
* Accuracy: 0.5479
* Tp: 0.3579
* Tn: 0.1900
* Fp: 0.3242
* Fn: 0.1279


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: 30


### 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: 30 ### 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: 30 ### 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: 30### Training results### Framework versions


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