
<!-- 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. -->

# gpt2_stereoset_finetuned

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: 0.6545
- Accuracy: 0.7088

## 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: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.21  | 5    | 1.1855          | 0.5259   |
| No log        | 0.42  | 10   | 0.7056          | 0.5338   |
| No log        | 0.62  | 15   | 0.7009          | 0.5400   |
| No log        | 0.83  | 20   | 0.7230          | 0.5173   |
| No log        | 1.04  | 25   | 0.6666          | 0.5989   |
| No log        | 1.25  | 30   | 0.6812          | 0.5699   |
| No log        | 1.46  | 35   | 0.6479          | 0.6272   |
| No log        | 1.67  | 40   | 0.6323          | 0.6484   |
| No log        | 1.88  | 45   | 0.6306          | 0.6515   |
| No log        | 2.08  | 50   | 0.6474          | 0.6633   |
| No log        | 2.29  | 55   | 0.6158          | 0.6641   |
| No log        | 2.5   | 60   | 0.6059          | 0.6703   |
| No log        | 2.71  | 65   | 0.6151          | 0.6695   |
| No log        | 2.92  | 70   | 0.5860          | 0.6782   |
| No log        | 3.12  | 75   | 0.5808          | 0.6907   |
| No log        | 3.33  | 80   | 0.5953          | 0.6915   |
| No log        | 3.54  | 85   | 0.5860          | 0.6994   |
| No log        | 3.75  | 90   | 0.5918          | 0.6947   |
| No log        | 3.96  | 95   | 0.5915          | 0.6797   |
| No log        | 4.17  | 100  | 0.5779          | 0.7041   |
| No log        | 4.38  | 105  | 0.5902          | 0.7151   |
| No log        | 4.58  | 110  | 0.5740          | 0.7080   |
| No log        | 4.79  | 115  | 0.5640          | 0.7088   |
| No log        | 5.0   | 120  | 0.5786          | 0.6947   |
| No log        | 5.21  | 125  | 0.5892          | 0.6978   |
| No log        | 5.42  | 130  | 0.5722          | 0.7096   |
| No log        | 5.62  | 135  | 0.5743          | 0.7064   |
| No log        | 5.83  | 140  | 0.5873          | 0.7057   |
| No log        | 6.04  | 145  | 0.5915          | 0.7033   |
| No log        | 6.25  | 150  | 0.5978          | 0.7009   |
| No log        | 6.46  | 155  | 0.6034          | 0.6931   |
| No log        | 6.67  | 160  | 0.5908          | 0.7111   |
| No log        | 6.88  | 165  | 0.5954          | 0.6947   |
| No log        | 7.08  | 170  | 0.5882          | 0.7033   |
| No log        | 7.29  | 175  | 0.5895          | 0.7151   |
| No log        | 7.5   | 180  | 0.6077          | 0.7104   |
| No log        | 7.71  | 185  | 0.6121          | 0.7151   |
| No log        | 7.92  | 190  | 0.6086          | 0.7151   |
| No log        | 8.12  | 195  | 0.6182          | 0.7127   |
| No log        | 8.33  | 200  | 0.6412          | 0.7072   |
| No log        | 8.54  | 205  | 0.6425          | 0.7049   |
| No log        | 8.75  | 210  | 0.6369          | 0.7135   |
| No log        | 8.96  | 215  | 0.6405          | 0.7111   |
| No log        | 9.17  | 220  | 0.6431          | 0.7135   |
| No log        | 9.38  | 225  | 0.6474          | 0.7127   |
| No log        | 9.58  | 230  | 0.6595          | 0.7041   |
| No log        | 9.79  | 235  | 0.6580          | 0.7041   |
| No log        | 10.0  | 240  | 0.6545          | 0.7088   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["stereoset"], "metrics": ["accuracy"], "model-index": [{"name": "gpt2_stereoset_finetuned", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "stereoset", "type": "stereoset", "config": "intersentence", "split": "validation", "args": "intersentence"}, "metrics": [{"type": "accuracy", "value": 0.7087912087912088, "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\_finetuned
==========================


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


* Loss: 0.6545
* Accuracy: 0.7088


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: 128
* 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 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 128
* 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 2.9.0
* 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: 5e-05
* train\_batch\_size: 128
* 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 2.9.0
* 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: 5e-05
* train\_batch\_size: 128
* 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 2.9.0
* Tokenizers 0.13.2