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# summarization_t5base_en_to_kjven

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8324
- Bleu: 21.2143
- Gen Len: 18.1685

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.0735        | 1.0   | 2860  | 0.9479          | 21.3913 | 18.1219 |
| 0.9776        | 2.0   | 5720  | 0.8750          | 22.1711 | 18.1307 |
| 0.918         | 3.0   | 8580  | 0.8317          | 22.6915 | 18.1381 |
| 0.8741        | 4.0   | 11440 | 0.8039          | 23.0856 | 18.1468 |
| 0.8489        | 5.0   | 14300 | 0.7841          | 23.3573 | 18.1455 |
| 0.8169        | 6.0   | 17160 | 0.7664          | 23.5073 | 18.1493 |
| 0.7965        | 7.0   | 20020 | 0.7532          | 23.6919 | 18.1495 |
| 0.78          | 8.0   | 22880 | 0.7411          | 23.8445 | 18.1461 |
| 0.7568        | 9.0   | 25740 | 0.7338          | 23.86   | 18.155  |
| 0.7496        | 10.0  | 28600 | 0.7228          | 23.953  | 18.1511 |
| 0.7411        | 11.0  | 31460 | 0.7175          | 24.0327 | 18.1511 |
| 0.8376        | 12.0  | 34320 | 0.8114          | 23.311  | 18.1319 |
| 1.1918        | 13.0  | 37180 | 0.9686          | 21.5339 | 18.1185 |
| 1.0929        | 14.0  | 40040 | 0.8978          | 21.561  | 18.1455 |
| 1.0373        | 15.0  | 42900 | 0.8617          | 21.4942 | 18.1542 |
| 1.0165        | 16.0  | 45760 | 0.8432          | 21.3962 | 18.1595 |
| 0.9973        | 17.0  | 48620 | 0.8340          | 21.2558 | 18.166  |
| 0.9889        | 18.0  | 51480 | 0.8326          | 21.2238 | 18.1687 |
| 0.9909        | 19.0  | 54340 | 0.8325          | 21.2216 | 18.1688 |
| 0.9942        | 20.0  | 57200 | 0.8324          | 21.2143 | 18.1685 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "summarization_t5base_en_to_kjven", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

summarization\_t5base\_en\_to\_kjven
====================================


This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8324
* Bleu: 21.2143
* Gen Len: 18.1685


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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3