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# gpt_16_5_5.6e-5

This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0127
- Rouge1: 0.6626
- Rouge2: 0.1168
- Rougel: 0.6456
- Bleu1: 0.0
- Bleu2: 0.0
- Bleu3: 0.0
- Bleu4: 0.0
- Gen Len: 1.0

## 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: 5.6e-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_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:-----:|:-----:|:-----:|:-----:|:-------:|
| 3.1501        | 0.38  | 1000  | 4.1034          | 0.6908 | 0.1016 | 0.6736 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
| 3.1688        | 0.76  | 2000  | 4.0936          | 0.6447 | 0.1117 | 0.6372 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
| 2.8769        | 1.13  | 3000  | 4.0668          | 0.7186 | 0.1365 | 0.7012 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
| 2.8633        | 1.51  | 4000  | 4.0363          | 0.625  | 0.1187 | 0.6116 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
| 2.8928        | 1.89  | 5000  | 4.0112          | 0.6599 | 0.1237 | 0.6476 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
| 2.6186        | 2.27  | 6000  | 4.0204          | 0.6953 | 0.1259 | 0.6761 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
| 2.6653        | 2.64  | 7000  | 4.0044          | 0.6795 | 0.1163 | 0.6627 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
| 2.5418        | 3.02  | 8000  | 4.0183          | 0.602  | 0.1154 | 0.5883 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
| 2.4621        | 3.4   | 9000  | 4.0149          | 0.5889 | 0.101  | 0.578  | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
| 2.4651        | 3.78  | 10000 | 3.9979          | 0.604  | 0.1098 | 0.5943 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
| 2.3334        | 4.15  | 11000 | 4.0226          | 0.6352 | 0.0993 | 0.6211 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
| 2.2859        | 4.53  | 12000 | 4.0152          | 0.6782 | 0.1125 | 0.6593 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
| 2.314         | 4.91  | 13000 | 4.0127          | 0.6626 | 0.1168 | 0.6456 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.9.0+cu102
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "gpt_16_5_5.6e-5", "results": []}]}
transformers pytorch tensorboard gpt2 text-generation generated_from_trainer license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gpt\_16\_5\_5.6e-5
==================


This model is a fine-tuned version of skt/kogpt2-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 4.0127
* Rouge1: 0.6626
* Rouge2: 0.1168
* Rougel: 0.6456
* Bleu1: 0.0
* Bleu2: 0.0
* Bleu3: 0.0
* Bleu4: 0.0
* Gen Len: 1.0


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: 5.6e-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\_ratio: 0.1
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.9.0+cu102
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-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\_ratio: 0.1
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.9.0+cu102
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-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\_ratio: 0.1
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.9.0+cu102
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-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\_ratio: 0.1
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.9.0+cu102
* Datasets 2.8.0
* Tokenizers 0.13.2