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# t5-small_stereoset_finetuned

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the stereoset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3572
- Accuracy: 0.5181
- Tp: 0.5008
- Tn: 0.0173
- Fp: 0.4819
- Fn: 0.0

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 6.2515        | 0.43  | 20   | 5.1550          | 0.6507   | 0.4992 | 0.1515 | 0.3477 | 0.0016 |
| 3.3425        | 0.85  | 40   | 1.9895          | 0.8414   | 0.4451 | 0.3964 | 0.1028 | 0.0557 |
| 1.0076        | 1.28  | 60   | 0.5623          | 0.5110   | 0.5    | 0.0110 | 0.4882 | 0.0008 |
| 0.522         | 1.7   | 80   | 0.3896          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.4541        | 2.13  | 100  | 0.3777          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.4494        | 2.55  | 120  | 0.3694          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3915        | 2.98  | 140  | 0.3468          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3956        | 3.4   | 160  | 0.3510          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.395         | 3.83  | 180  | 0.3403          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.4005        | 4.26  | 200  | 0.3357          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3764        | 4.68  | 220  | 0.3367          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3798        | 5.11  | 240  | 0.3321          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3546        | 5.53  | 260  | 0.3452          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3716        | 5.96  | 280  | 0.3257          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3598        | 6.38  | 300  | 0.3255          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.366         | 6.81  | 320  | 0.3400          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3537        | 7.23  | 340  | 0.3239          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3395        | 7.66  | 360  | 0.3238          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.338         | 8.09  | 380  | 0.3323          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3402        | 8.51  | 400  | 0.3193          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3421        | 8.94  | 420  | 0.3188          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.318         | 9.36  | 440  | 0.3173          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3532        | 9.79  | 460  | 0.3164          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3333        | 10.21 | 480  | 0.3175          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3187        | 10.64 | 500  | 0.3157          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3004        | 11.06 | 520  | 0.3108          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3257        | 11.49 | 540  | 0.3094          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3342        | 11.91 | 560  | 0.3125          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3084        | 12.34 | 580  | 0.3141          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3134        | 12.77 | 600  | 0.3076          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3081        | 13.19 | 620  | 0.3059          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3017        | 13.62 | 640  | 0.3098          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.2924        | 14.04 | 660  | 0.3046          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3109        | 14.47 | 680  | 0.3054          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.287         | 14.89 | 700  | 0.3061          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.2783        | 15.32 | 720  | 0.3051          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.2938        | 15.74 | 740  | 0.3037          | 0.5031   | 0.5008 | 0.0024 | 0.4969 | 0.0    |
| 0.2788        | 16.17 | 760  | 0.3057          | 0.5024   | 0.5008 | 0.0016 | 0.4976 | 0.0    |
| 0.2872        | 16.6  | 780  | 0.3100          | 0.5016   | 0.5008 | 0.0008 | 0.4984 | 0.0    |
| 0.2794        | 17.02 | 800  | 0.3055          | 0.5031   | 0.5008 | 0.0024 | 0.4969 | 0.0    |
| 0.2847        | 17.45 | 820  | 0.3047          | 0.5055   | 0.5008 | 0.0047 | 0.4945 | 0.0    |
| 0.2644        | 17.87 | 840  | 0.3067          | 0.5024   | 0.5008 | 0.0016 | 0.4976 | 0.0    |
| 0.2558        | 18.3  | 860  | 0.3062          | 0.5063   | 0.5008 | 0.0055 | 0.4937 | 0.0    |
| 0.2867        | 18.72 | 880  | 0.3160          | 0.5024   | 0.5008 | 0.0016 | 0.4976 | 0.0    |
| 0.2864        | 19.15 | 900  | 0.3067          | 0.5071   | 0.5008 | 0.0063 | 0.4929 | 0.0    |
| 0.2645        | 19.57 | 920  | 0.3211          | 0.5024   | 0.5008 | 0.0016 | 0.4976 | 0.0    |
| 0.2606        | 20.0  | 940  | 0.3067          | 0.5094   | 0.5008 | 0.0086 | 0.4906 | 0.0    |
| 0.2694        | 20.43 | 960  | 0.3125          | 0.5055   | 0.5008 | 0.0047 | 0.4945 | 0.0    |
| 0.2634        | 20.85 | 980  | 0.3072          | 0.5086   | 0.5008 | 0.0078 | 0.4914 | 0.0    |
| 0.2519        | 21.28 | 1000 | 0.3088          | 0.5086   | 0.5008 | 0.0078 | 0.4914 | 0.0    |
| 0.2537        | 21.7  | 1020 | 0.3136          | 0.5071   | 0.5008 | 0.0063 | 0.4929 | 0.0    |
| 0.2536        | 22.13 | 1040 | 0.3089          | 0.5133   | 0.5008 | 0.0126 | 0.4867 | 0.0    |
| 0.2488        | 22.55 | 1060 | 0.3108          | 0.5110   | 0.5008 | 0.0102 | 0.4890 | 0.0    |
| 0.2558        | 22.98 | 1080 | 0.3157          | 0.5071   | 0.5008 | 0.0063 | 0.4929 | 0.0    |
| 0.2626        | 23.4  | 1100 | 0.3104          | 0.5133   | 0.5008 | 0.0126 | 0.4867 | 0.0    |
| 0.2424        | 23.83 | 1120 | 0.3130          | 0.5133   | 0.5008 | 0.0126 | 0.4867 | 0.0    |
| 0.2496        | 24.26 | 1140 | 0.3139          | 0.5133   | 0.5008 | 0.0126 | 0.4867 | 0.0    |
| 0.2451        | 24.68 | 1160 | 0.3160          | 0.5133   | 0.5008 | 0.0126 | 0.4867 | 0.0    |
| 0.2399        | 25.11 | 1180 | 0.3148          | 0.5141   | 0.5008 | 0.0133 | 0.4859 | 0.0    |
| 0.236         | 25.53 | 1200 | 0.3163          | 0.5141   | 0.5008 | 0.0133 | 0.4859 | 0.0    |
| 0.2561        | 25.96 | 1220 | 0.3208          | 0.5118   | 0.5008 | 0.0110 | 0.4882 | 0.0    |
| 0.2425        | 26.38 | 1240 | 0.3161          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.2376        | 26.81 | 1260 | 0.3173          | 0.5133   | 0.5008 | 0.0126 | 0.4867 | 0.0    |
| 0.246         | 27.23 | 1280 | 0.3174          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.2256        | 27.66 | 1300 | 0.3186          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.2316        | 28.09 | 1320 | 0.3177          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.2406        | 28.51 | 1340 | 0.3197          | 0.5157   | 0.5008 | 0.0149 | 0.4843 | 0.0    |
| 0.218         | 28.94 | 1360 | 0.3227          | 0.5133   | 0.5008 | 0.0126 | 0.4867 | 0.0    |
| 0.2103        | 29.36 | 1380 | 0.3228          | 0.5141   | 0.5008 | 0.0133 | 0.4859 | 0.0    |
| 0.2161        | 29.79 | 1400 | 0.3244          | 0.5133   | 0.5008 | 0.0126 | 0.4867 | 0.0    |
| 0.2172        | 30.21 | 1420 | 0.3246          | 0.5141   | 0.5008 | 0.0133 | 0.4859 | 0.0    |
| 0.2119        | 30.64 | 1440 | 0.3256          | 0.5141   | 0.5008 | 0.0133 | 0.4859 | 0.0    |
| 0.2202        | 31.06 | 1460 | 0.3261          | 0.5181   | 0.5008 | 0.0173 | 0.4819 | 0.0    |
| 0.2244        | 31.49 | 1480 | 0.3297          | 0.5141   | 0.5008 | 0.0133 | 0.4859 | 0.0    |
| 0.2123        | 31.91 | 1500 | 0.3285          | 0.5165   | 0.5008 | 0.0157 | 0.4835 | 0.0    |
| 0.1914        | 32.34 | 1520 | 0.3308          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.2338        | 32.77 | 1540 | 0.3311          | 0.5173   | 0.5008 | 0.0165 | 0.4827 | 0.0    |
| 0.2206        | 33.19 | 1560 | 0.3317          | 0.5173   | 0.5008 | 0.0165 | 0.4827 | 0.0    |
| 0.2278        | 33.62 | 1580 | 0.3340          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.1982        | 34.04 | 1600 | 0.3343          | 0.5181   | 0.5008 | 0.0173 | 0.4819 | 0.0    |
| 0.2154        | 34.47 | 1620 | 0.3354          | 0.5141   | 0.5008 | 0.0133 | 0.4859 | 0.0    |
| 0.1901        | 34.89 | 1640 | 0.3372          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.2087        | 35.32 | 1660 | 0.3396          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.1931        | 35.74 | 1680 | 0.3401          | 0.5173   | 0.5008 | 0.0165 | 0.4827 | 0.0    |
| 0.1995        | 36.17 | 1700 | 0.3411          | 0.5235   | 0.5008 | 0.0228 | 0.4765 | 0.0    |
| 0.2226        | 36.6  | 1720 | 0.3413          | 0.5181   | 0.5008 | 0.0173 | 0.4819 | 0.0    |
| 0.216         | 37.02 | 1740 | 0.3443          | 0.5141   | 0.5008 | 0.0133 | 0.4859 | 0.0    |
| 0.1911        | 37.45 | 1760 | 0.3434          | 0.5141   | 0.5008 | 0.0133 | 0.4859 | 0.0    |
| 0.2138        | 37.87 | 1780 | 0.3435          | 0.5173   | 0.5008 | 0.0165 | 0.4827 | 0.0    |
| 0.1918        | 38.3  | 1800 | 0.3449          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.2021        | 38.72 | 1820 | 0.3462          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.2046        | 39.15 | 1840 | 0.3454          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.2084        | 39.57 | 1860 | 0.3460          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.1834        | 40.0  | 1880 | 0.3462          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.19          | 40.43 | 1900 | 0.3479          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.1877        | 40.85 | 1920 | 0.3479          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.2152        | 41.28 | 1940 | 0.3480          | 0.5212   | 0.5008 | 0.0204 | 0.4788 | 0.0    |
| 0.1795        | 41.7  | 1960 | 0.3487          | 0.5157   | 0.5008 | 0.0149 | 0.4843 | 0.0    |
| 0.1804        | 42.13 | 1980 | 0.3502          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.1748        | 42.55 | 2000 | 0.3520          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.177         | 42.98 | 2020 | 0.3518          | 0.5181   | 0.5008 | 0.0173 | 0.4819 | 0.0    |
| 0.179         | 43.4  | 2040 | 0.3529          | 0.5204   | 0.5008 | 0.0196 | 0.4796 | 0.0    |
| 0.1895        | 43.83 | 2060 | 0.3538          | 0.5204   | 0.5008 | 0.0196 | 0.4796 | 0.0    |
| 0.1867        | 44.26 | 2080 | 0.3539          | 0.5196   | 0.5008 | 0.0188 | 0.4804 | 0.0    |
| 0.2155        | 44.68 | 2100 | 0.3546          | 0.5165   | 0.5008 | 0.0157 | 0.4835 | 0.0    |
| 0.1783        | 45.11 | 2120 | 0.3550          | 0.5181   | 0.5008 | 0.0173 | 0.4819 | 0.0    |
| 0.1969        | 45.53 | 2140 | 0.3560          | 0.5157   | 0.5008 | 0.0149 | 0.4843 | 0.0    |
| 0.1826        | 45.96 | 2160 | 0.3564          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.1957        | 46.38 | 2180 | 0.3571          | 0.5149   | 0.5008 | 0.0141 | 0.4851 | 0.0    |
| 0.1864        | 46.81 | 2200 | 0.3568          | 0.5157   | 0.5008 | 0.0149 | 0.4843 | 0.0    |
| 0.1889        | 47.23 | 2220 | 0.3564          | 0.5196   | 0.5008 | 0.0188 | 0.4804 | 0.0    |
| 0.1837        | 47.66 | 2240 | 0.3569          | 0.5165   | 0.5008 | 0.0157 | 0.4835 | 0.0    |
| 0.1713        | 48.09 | 2260 | 0.3568          | 0.5196   | 0.5008 | 0.0188 | 0.4804 | 0.0    |
| 0.1997        | 48.51 | 2280 | 0.3570          | 0.5188   | 0.5008 | 0.0181 | 0.4812 | 0.0    |
| 0.1844        | 48.94 | 2300 | 0.3570          | 0.5188   | 0.5008 | 0.0181 | 0.4812 | 0.0    |
| 0.1854        | 49.36 | 2320 | 0.3571          | 0.5181   | 0.5008 | 0.0173 | 0.4819 | 0.0    |
| 0.1897        | 49.79 | 2340 | 0.3572          | 0.5181   | 0.5008 | 0.0173 | 0.4819 | 0.0    |


### 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": "t5-small_stereoset_finetuned", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "stereoset", "type": "stereoset", "config": "intersentence", "split": "validation", "args": "intersentence"}, "metrics": [{"type": "accuracy", "value": 0.5180533751962323, "name": "Accuracy"}]}]}]}
transformers pytorch t5 text2text-generation generated_from_trainer dataset:stereoset license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-stereoset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small\_stereoset\_finetuned
==============================


This model is a fine-tuned version of t5-small on the stereoset dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3572
* Accuracy: 0.5181
* Tp: 0.5008
* Tn: 0.0173
* Fp: 0.4819
* Fn: 0.0


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 #t5 #text2text-generation #generated_from_trainer #dataset-stereoset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #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 #t5 #text2text-generation #generated_from_trainer #dataset-stereoset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #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