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# t5-small_stereoset_finetuned_HBRPOI

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: 0.4383
- Accuracy: 0.6028
- Tp: 0.4890
- Tn: 0.1138
- Fp: 0.3854
- Fn: 0.0118

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp     | Tn     | Fp     | Fn     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.4447        | 0.43  | 20   | 0.3978          | 0.5008   | 0.5008 | 0.0    | 0.4992 | 0.0    |
| 0.3776        | 0.85  | 40   | 0.3448          | 0.6232   | 0.5008 | 0.1224 | 0.3768 | 0.0    |
| 0.3649        | 1.28  | 60   | 0.3269          | 0.5612   | 0.5    | 0.0612 | 0.4380 | 0.0008 |
| 0.3275        | 1.7   | 80   | 0.3218          | 0.5330   | 0.4992 | 0.0338 | 0.4655 | 0.0016 |
| 0.2969        | 2.13  | 100  | 0.3104          | 0.6256   | 0.4961 | 0.1295 | 0.3697 | 0.0047 |
| 0.3283        | 2.55  | 120  | 0.3111          | 0.5730   | 0.4992 | 0.0738 | 0.4254 | 0.0016 |
| 0.3046        | 2.98  | 140  | 0.3040          | 0.5416   | 0.4992 | 0.0424 | 0.4568 | 0.0016 |
| 0.2603        | 3.4   | 160  | 0.3057          | 0.5447   | 0.4992 | 0.0455 | 0.4537 | 0.0016 |
| 0.2828        | 3.83  | 180  | 0.3186          | 0.5479   | 0.4984 | 0.0495 | 0.4498 | 0.0024 |
| 0.2326        | 4.26  | 200  | 0.3036          | 0.6193   | 0.4937 | 0.1256 | 0.3736 | 0.0071 |
| 0.2289        | 4.68  | 220  | 0.3328          | 0.5479   | 0.4976 | 0.0502 | 0.4490 | 0.0031 |
| 0.2234        | 5.11  | 240  | 0.3140          | 0.5777   | 0.4976 | 0.0801 | 0.4192 | 0.0031 |
| 0.2225        | 5.53  | 260  | 0.3245          | 0.5691   | 0.4976 | 0.0714 | 0.4278 | 0.0031 |
| 0.187         | 5.96  | 280  | 0.3300          | 0.5785   | 0.4961 | 0.0824 | 0.4168 | 0.0047 |
| 0.179         | 6.38  | 300  | 0.3344          | 0.5848   | 0.4961 | 0.0887 | 0.4105 | 0.0047 |
| 0.1523        | 6.81  | 320  | 0.3528          | 0.5895   | 0.4969 | 0.0926 | 0.4066 | 0.0039 |
| 0.1499        | 7.23  | 340  | 0.3788          | 0.6232   | 0.4906 | 0.1327 | 0.3666 | 0.0102 |
| 0.1292        | 7.66  | 360  | 0.3889          | 0.5942   | 0.4914 | 0.1028 | 0.3964 | 0.0094 |
| 0.13          | 8.09  | 380  | 0.3959          | 0.5903   | 0.4937 | 0.0965 | 0.4027 | 0.0071 |
| 0.1216        | 8.51  | 400  | 0.4169          | 0.5856   | 0.4922 | 0.0934 | 0.4058 | 0.0086 |
| 0.1306        | 8.94  | 420  | 0.4227          | 0.6005   | 0.4898 | 0.1107 | 0.3885 | 0.0110 |
| 0.0968        | 9.36  | 440  | 0.4334          | 0.5965   | 0.4914 | 0.1052 | 0.3940 | 0.0094 |
| 0.1044        | 9.79  | 460  | 0.4383          | 0.6028   | 0.4890 | 0.1138 | 0.3854 | 0.0118 |


### 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_HBRPOI", "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": 0.6028257456828885, "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\_HBRPOI
======================================


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


* Loss: 0.4383
* Accuracy: 0.6028
* Tp: 0.4890
* Tn: 0.1138
* Fp: 0.3854
* Fn: 0.0118


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: 10


### 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: 10 ### 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: 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: 10 ### Training results ### Framework versions


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