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# kobart_4_5.6e-5_datav2_min30_lp5.0_temperature1.0

This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9891
- Rouge1: 35.4597
- Rouge2: 12.0824
- Rougel: 23.0161
- Bleu1: 29.793
- Bleu2: 16.882
- Bleu3: 9.6468
- Bleu4: 5.3654
- Gen Len: 50.6014

## 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: 4
- eval_batch_size: 128
- 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

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Bleu1   | Bleu2   | Bleu3   | Bleu4  | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:|
| 2.3968        | 0.47  | 5000  | 2.9096          | 32.7469 | 10.9679 | 21.4954 | 27.0594 | 15.1133 | 8.4503  | 4.564  | 48.5501 |
| 2.2338        | 0.94  | 10000 | 2.8002          | 33.2148 | 11.5121 | 22.7066 | 26.4886 | 15.0125 | 8.5792  | 4.8523 | 41.1049 |
| 1.9652        | 1.42  | 15000 | 2.7699          | 34.4269 | 11.8551 | 22.8478 | 28.2628 | 16.0909 | 9.0427  | 4.9254 | 46.9744 |
| 2.001         | 1.89  | 20000 | 2.7201          | 34.157  | 11.8683 | 22.6775 | 28.3593 | 16.1361 | 9.221   | 4.8616 | 46.979  |
| 1.6433        | 2.36  | 25000 | 2.7901          | 33.6354 | 11.5761 | 22.6878 | 27.6475 | 15.6571 | 8.8372  | 4.8672 | 43.9953 |
| 1.6204        | 2.83  | 30000 | 2.7724          | 34.9611 | 12.1606 | 23.0246 | 29.1014 | 16.6689 | 9.3661  | 5.1916 | 48.8811 |
| 1.2955        | 3.3   | 35000 | 2.8970          | 35.896  | 12.7037 | 23.3781 | 29.9701 | 17.3963 | 10.2978 | 5.9339 | 49.5921 |
| 1.3501        | 3.78  | 40000 | 2.8854          | 35.2981 | 12.1133 | 23.1845 | 29.483  | 16.7795 | 9.4124  | 5.2042 | 48.5897 |
| 1.0865        | 4.25  | 45000 | 2.9912          | 35.581  | 12.5145 | 23.2262 | 29.9364 | 17.2064 | 10.0427 | 5.62   | 48.31   |
| 1.052         | 4.72  | 50000 | 2.9891          | 35.4597 | 12.0824 | 23.0161 | 29.793  | 16.882  | 9.6468  | 5.3654 | 50.6014 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "kobart_4_5.6e-5_datav2_min30_lp5.0_temperature1.0", "results": []}]}
transformers pytorch tensorboard bart text2text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

kobart\_4\_5.6e-5\_datav2\_min30\_lp5.0\_temperature1.0
=======================================================


This model is a fine-tuned version of gogamza/kobart-base-v2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.9891
* Rouge1: 35.4597
* Rouge2: 12.0824
* Rougel: 23.0161
* Bleu1: 29.793
* Bleu2: 16.882
* Bleu3: 9.6468
* Bleu4: 5.3654
* Gen Len: 50.6014


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: 4
* eval\_batch\_size: 128
* 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


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 4
* eval\_batch\_size: 128
* 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 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 4
* eval\_batch\_size: 128
* 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 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 4
* eval\_batch\_size: 128
* 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### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2