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# gq-indo-k

This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7905
- Rouge1: 22.5734
- Rouge2: 6.555
- Rougel: 20.9491
- Rougelsum: 20.9509
- Gen Len: 12.0767

## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.9355        | 1.0   | 13032 | 2.8563          | 22.4828 | 6.5456 | 20.8782 | 20.8772   | 11.915  |
| 2.825         | 2.0   | 26064 | 2.7993          | 22.547  | 6.5815 | 20.8937 | 20.8973   | 12.0886 |
| 2.7631        | 3.0   | 39096 | 2.7905          | 22.5734 | 6.555  | 20.9491 | 20.9509   | 12.0767 |


### Framework versions

- Transformers 4.6.1
- Pytorch 1.7.0
- Datasets 1.11.0
- Tokenizers 0.10.3

{"metrics": ["rouge"]}
transformers pytorch t5 text2text-generation autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gq-indo-k
=========


This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:


* Loss: 2.7905
* Rouge1: 22.5734
* Rouge2: 6.555
* Rougel: 20.9491
* Rougelsum: 20.9509
* Gen Len: 12.0767


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: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.6.1
* Pytorch 1.7.0
* Datasets 1.11.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.6.1
* Pytorch 1.7.0
* Datasets 1.11.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #t5 #text2text-generation #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: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.6.1
* Pytorch 1.7.0
* Datasets 1.11.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #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: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.6.1
* Pytorch 1.7.0
* Datasets 1.11.0
* Tokenizers 0.10.3