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# gpt2_sm_cv_summarized_4

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: 2.4906
- Accuracy: 0.743
- Precision: 0.3038
- Recall: 0.2462
- F1: 0.2720
- D-index: 1.4383

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 8000
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log        | 1.0   | 250  | 1.0827          | 0.673    | 0.2155    | 0.2564 | 0.2342 | 1.3419  |
| 2.5863        | 2.0   | 500  | 0.6038          | 0.761    | 0.2381    | 0.1026 | 0.1434 | 1.4124  |
| 2.5863        | 3.0   | 750  | 0.5238          | 0.794    | 0.3333    | 0.0564 | 0.0965 | 1.4421  |
| 0.5205        | 4.0   | 1000 | 0.5206          | 0.798    | 0.3158    | 0.0308 | 0.0561 | 1.4384  |
| 0.5205        | 5.0   | 1250 | 0.5068          | 0.802    | 0.4286    | 0.0462 | 0.0833 | 1.4495  |
| 0.4619        | 6.0   | 1500 | 0.5153          | 0.795    | 0.4074    | 0.1128 | 0.1767 | 1.4637  |
| 0.4619        | 7.0   | 1750 | 0.5246          | 0.795    | 0.3611    | 0.0667 | 0.1126 | 1.4472  |
| 0.3988        | 8.0   | 2000 | 0.6671          | 0.797    | 0.3333    | 0.0410 | 0.0731 | 1.4407  |
| 0.3988        | 9.0   | 2250 | 0.6091          | 0.763    | 0.35      | 0.2513 | 0.2925 | 1.4680  |
| 0.3128        | 10.0  | 2500 | 0.7342          | 0.759    | 0.3284    | 0.2256 | 0.2675 | 1.4535  |
| 0.3128        | 11.0  | 2750 | 0.8236          | 0.741    | 0.3049    | 0.2564 | 0.2786 | 1.4391  |
| 0.2198        | 12.0  | 3000 | 0.9349          | 0.742    | 0.3297    | 0.3128 | 0.3211 | 1.4601  |
| 0.2198        | 13.0  | 3250 | 1.1979          | 0.764    | 0.3481    | 0.2410 | 0.2848 | 1.4658  |
| 0.1522        | 14.0  | 3500 | 1.3995          | 0.758    | 0.3464    | 0.2718 | 0.3046 | 1.4682  |
| 0.1522        | 15.0  | 3750 | 2.3304          | 0.779    | 0.3143    | 0.1128 | 0.1660 | 1.4414  |
| 0.1137        | 16.0  | 4000 | 2.0930          | 0.762    | 0.2991    | 0.1641 | 0.2119 | 1.4359  |
| 0.1137        | 17.0  | 4250 | 2.6869          | 0.787    | 0.3714    | 0.1333 | 0.1962 | 1.4598  |
| 0.0904        | 18.0  | 4500 | 2.2347          | 0.678    | 0.2818    | 0.4205 | 0.3374 | 1.4071  |
| 0.0904        | 19.0  | 4750 | 2.4580          | 0.752    | 0.3221    | 0.2462 | 0.2791 | 1.4509  |
| 0.0737        | 20.0  | 5000 | 2.4906          | 0.743    | 0.3038    | 0.2462 | 0.2720 | 1.4383  |


### 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_cv_summarized_4", "results": []}]}
transformers pytorch gpt2 text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gpt2\_sm\_cv\_summarized\_4
===========================


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


* Loss: 2.4906
* Accuracy: 0.743
* Precision: 0.3038
* Recall: 0.2462
* F1: 0.2720
* D-index: 1.4383


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: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 8000
* 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: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 8000
* 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 #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: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 8000
* 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