
<!-- 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. -->

# kobart_8_5.6e-5_min30_lp4_sample

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: 2.8230
- Rouge1: 36.1016
- Rouge2: 12.8106
- Rougel: 23.6405
- Bleu1: 30.2521
- Bleu2: 17.5293
- Bleu3: 10.3861
- Bleu4: 5.7474
- Gen Len: 50.6713

## 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: 8
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:|
| 2.527         | 0.19  | 1000  | 3.0014          | 31.7677 | 9.8681  | 20.6405 | 26.0107 | 13.8904 | 7.2892  | 3.6709 | 48.8228 |
| 2.4185        | 0.38  | 2000  | 2.8850          | 32.5931 | 10.727  | 21.3403 | 26.4666 | 14.6488 | 8.0738  | 4.2365 | 44.3497 |
| 2.3546        | 0.57  | 3000  | 2.8285          | 32.7686 | 11.0203 | 21.8204 | 26.9575 | 15.0974 | 8.365   | 4.6586 | 45.0956 |
| 2.2617        | 0.76  | 4000  | 2.7775          | 34.1375 | 12.1264 | 22.506  | 28.082  | 16.2606 | 9.486   | 5.3768 | 49.4872 |
| 2.2106        | 0.94  | 5000  | 2.7396          | 33.4733 | 11.2845 | 22.5126 | 27.3856 | 15.3472 | 8.567   | 4.6969 | 44.2401 |
| 2.0022        | 1.13  | 6000  | 2.7534          | 33.9237 | 11.84   | 22.5473 | 27.5719 | 15.8555 | 9.0337  | 5.1397 | 45.2611 |
| 1.9749        | 1.32  | 7000  | 2.7258          | 35.1741 | 12.4088 | 22.8272 | 29.4193 | 17.0056 | 9.9196  | 5.5038 | 50.3124 |
| 1.993         | 1.51  | 8000  | 2.7026          | 35.8572 | 13.2373 | 23.5429 | 30.1024 | 17.7802 | 10.6998 | 6.133  | 51.9953 |
| 1.9461        | 1.7   | 9000  | 2.6379          | 35.0541 | 12.4639 | 23.4095 | 28.7614 | 16.7411 | 9.5243  | 5.3422 | 45.7319 |
| 1.9159        | 1.89  | 10000 | 2.6071          | 35.3005 | 13.0834 | 23.5232 | 29.2371 | 17.3405 | 10.0603 | 5.9913 | 46.3846 |
| 1.6347        | 2.08  | 11000 | 2.6773          | 35.6737 | 12.7968 | 23.5884 | 30.0898 | 17.5699 | 10.0439 | 5.984  | 51.4755 |
| 1.6179        | 2.27  | 12000 | 2.6652          | 35.6258 | 13.0066 | 24.1646 | 29.4431 | 17.3774 | 10.4055 | 6.0368 | 47.2121 |
| 1.613         | 2.45  | 13000 | 2.6667          | 35.6093 | 12.3267 | 23.4513 | 29.6818 | 17.0819 | 9.7674  | 5.4192 | 48.1632 |
| 1.6642        | 2.64  | 14000 | 2.6516          | 36.1341 | 12.9256 | 23.6283 | 30.3579 | 17.689  | 10.3152 | 5.6037 | 47.9534 |
| 1.6432        | 2.83  | 15000 | 2.6498          | 37.3996 | 14.1165 | 24.4384 | 31.3868 | 18.8878 | 11.6758 | 7.0218 | 51.0769 |
| 1.371         | 3.02  | 16000 | 2.7315          | 36.2931 | 13.1544 | 23.6259 | 30.5586 | 17.9341 | 10.2612 | 5.6973 | 53.0606 |
| 1.374         | 3.21  | 17000 | 2.7438          | 36.2938 | 13.3253 | 23.8868 | 30.2665 | 17.9543 | 10.6402 | 6.1801 | 48.0303 |
| 1.3962        | 3.4   | 18000 | 2.7682          | 35.8607 | 12.9747 | 23.7071 | 30.0202 | 17.573  | 10.256  | 5.8021 | 49.8578 |
| 1.3699        | 3.59  | 19000 | 2.7530          | 36.1645 | 12.8211 | 23.5026 | 30.2944 | 17.6159 | 10.18   | 5.6959 | 51.3846 |
| 1.3552        | 3.78  | 20000 | 2.7558          | 36.1135 | 12.6383 | 23.1973 | 30.2234 | 17.3569 | 9.9499  | 5.577  | 50.2098 |
| 1.37          | 3.97  | 21000 | 2.7441          | 35.9377 | 12.744  | 23.3985 | 30.1982 | 17.5623 | 10.1743 | 5.8601 | 51.704  |
| 1.1739        | 4.15  | 22000 | 2.8335          | 36.126  | 12.8817 | 23.4948 | 30.259  | 17.6231 | 10.2709 | 5.772  | 51.5035 |
| 1.1966        | 4.34  | 23000 | 2.8219          | 36.3689 | 12.7938 | 23.7675 | 30.5862 | 17.6182 | 10.3642 | 6.0505 | 49.5664 |
| 1.1812        | 4.53  | 24000 | 2.8206          | 36.3009 | 13.2677 | 23.65   | 30.5531 | 18.0616 | 10.7975 | 6.3877 | 51.6783 |
| 1.1885        | 4.72  | 25000 | 2.8247          | 36.0696 | 13.0568 | 23.7406 | 30.4063 | 17.8602 | 10.7829 | 6.0939 | 50.4499 |
| 1.165         | 4.91  | 26000 | 2.8230          | 36.1016 | 12.8106 | 23.6405 | 30.2521 | 17.5293 | 10.3861 | 5.7474 | 50.6713 |


### 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_8_5.6e-5_min30_lp4_sample", "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\_8\_5.6e-5\_min30\_lp4\_sample
=====================================


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: 2.8230
* Rouge1: 36.1016
* Rouge2: 12.8106
* Rougel: 23.6405
* Bleu1: 30.2521
* Bleu2: 17.5293
* Bleu3: 10.3861
* Bleu4: 5.7474
* Gen Len: 50.6713


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: 8
* 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: 8
* 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: 8
* 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: 8
* 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