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# gpt2_sm_gen1_large

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4824
- Accuracy: 0.8063
- Precision: 0.5094
- Recall: 0.3114
- F1: 0.3865
- D-index: 1.5483

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 96000
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     | D-index |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.5028        | 1.0   | 3000  | 0.5183          | 0.8039   | 0.4872    | 0.0162 | 0.0313 | 1.4419  |
| 0.4442        | 2.0   | 6000  | 0.4597          | 0.8113   | 0.6126    | 0.0995 | 0.1712 | 1.4819  |
| 0.415         | 3.0   | 9000  | 0.4217          | 0.8202   | 0.6309    | 0.1978 | 0.3012 | 1.5284  |
| 0.4047        | 4.0   | 12000 | 0.4365          | 0.8228   | 0.6682    | 0.1901 | 0.2960 | 1.5294  |
| 0.3827        | 5.0   | 15000 | 0.4141          | 0.8289   | 0.6502    | 0.2744 | 0.3859 | 1.5663  |
| 0.3527        | 6.0   | 18000 | 0.4357          | 0.8284   | 0.6320    | 0.2973 | 0.4044 | 1.5733  |
| 0.336         | 7.0   | 21000 | 0.4322          | 0.8285   | 0.6202    | 0.3216 | 0.4235 | 1.5815  |
| 0.3051        | 8.0   | 24000 | 0.4696          | 0.8259   | 0.6076    | 0.3148 | 0.4147 | 1.5758  |
| 0.2745        | 9.0   | 27000 | 0.4957          | 0.8164   | 0.5431    | 0.3969 | 0.4586 | 1.5903  |
| 0.2435        | 10.0  | 30000 | 0.5369          | 0.8151   | 0.5391    | 0.3871 | 0.4506 | 1.5853  |
| 0.2182        | 11.0  | 33000 | 0.6251          | 0.8176   | 0.5559    | 0.3428 | 0.4241 | 1.5740  |
| 0.2031        | 12.0  | 36000 | 0.6869          | 0.795    | 0.4760    | 0.4590 | 0.4673 | 1.5820  |
| 0.188         | 13.0  | 39000 | 0.8867          | 0.8147   | 0.5600    | 0.2522 | 0.3478 | 1.5396  |
| 0.1738        | 14.0  | 42000 | 1.0311          | 0.8077   | 0.5149    | 0.3152 | 0.3910 | 1.5514  |
| 0.1495        | 15.0  | 45000 | 1.2024          | 0.8053   | 0.5039    | 0.3815 | 0.4343 | 1.5703  |
| 0.1415        | 16.0  | 48000 | 1.3324          | 0.8045   | 0.5013    | 0.4015 | 0.4459 | 1.5759  |
| 0.1275        | 17.0  | 51000 | 1.5071          | 0.8051   | 0.5038    | 0.3416 | 0.4071 | 1.5568  |
| 0.1139        | 18.0  | 54000 | 1.4309          | 0.8053   | 0.5047    | 0.3177 | 0.3900 | 1.5490  |
| 0.1111        | 19.0  | 57000 | 1.5033          | 0.8082   | 0.5154    | 0.3496 | 0.4166 | 1.5636  |
| 0.1124        | 20.0  | 60000 | 1.4824          | 0.8063   | 0.5094    | 0.3114 | 0.3865 | 1.5483  |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "gpt2_sm_gen1_large", "results": []}]}
transformers pytorch tensorboard gpt2 text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gpt2\_sm\_gen1\_large
=====================


This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4824
* Accuracy: 0.8063
* Precision: 0.5094
* Recall: 0.3114
* F1: 0.3865
* D-index: 1.5483


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: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 96000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 96000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-classification #generated_from_trainer #license-mit #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: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 96000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3