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# mt5-summarize-sum

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3984
- Rouge1: 0.5736
- Rouge2: 0.3783
- Rougel: 0.4855
- Rougelsum: 0.4844

## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 90
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 13.8551       | 0.16  | 100  | 5.4672          | 0.2389 | 0.0546 | 0.2119 | 0.2110    |
| 1.0762        | 0.33  | 200  | 0.5982          | 0.3774 | 0.2199 | 0.3493 | 0.3470    |
| 0.8077        | 0.49  | 300  | 0.4999          | 0.4929 | 0.3195 | 0.4349 | 0.4312    |
| 0.7772        | 0.65  | 400  | 0.4652          | 0.4715 | 0.3296 | 0.4431 | 0.4409    |
| 0.7771        | 0.82  | 500  | 0.4402          | 0.4881 | 0.3356 | 0.4433 | 0.4412    |
| 0.713         | 0.98  | 600  | 0.4500          | 0.4990 | 0.3291 | 0.4550 | 0.4525    |
| 0.65          | 1.15  | 700  | 0.4335          | 0.5522 | 0.3633 | 0.4930 | 0.4909    |
| 0.7035        | 1.31  | 800  | 0.4278          | 0.5227 | 0.3470 | 0.4781 | 0.4772    |
| 0.6818        | 1.47  | 900  | 0.4202          | 0.5325 | 0.3585 | 0.4759 | 0.4744    |
| 0.6643        | 1.64  | 1000 | 0.4113          | 0.5326 | 0.3486 | 0.4678 | 0.4641    |
| 0.6007        | 1.8   | 1100 | 0.4122          | 0.5152 | 0.3260 | 0.4572 | 0.4547    |
| 0.5866        | 1.96  | 1200 | 0.4158          | 0.5538 | 0.3680 | 0.4910 | 0.4903    |
| 0.5563        | 2.13  | 1300 | 0.4051          | 0.5433 | 0.3371 | 0.4685 | 0.4672    |
| 0.5727        | 2.29  | 1400 | 0.4089          | 0.5447 | 0.3619 | 0.4711 | 0.4695    |
| 0.5859        | 2.45  | 1500 | 0.4033          | 0.5464 | 0.3411 | 0.4688 | 0.4662    |
| 0.5783        | 2.62  | 1600 | 0.3997          | 0.5667 | 0.3595 | 0.4825 | 0.4787    |
| 0.5673        | 2.78  | 1700 | 0.3992          | 0.5759 | 0.3882 | 0.4911 | 0.4891    |
| 0.57          | 2.95  | 1800 | 0.3984          | 0.5736 | 0.3783 | 0.4855 | 0.4844    |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-summarize-sum", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-summarize-sum
=================


This model is a fine-tuned version of google/mt5-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3984
* Rouge1: 0.5736
* Rouge2: 0.3783
* Rougel: 0.4855
* Rougelsum: 0.4844


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: 0.0005
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 90
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 90
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mt5 #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: 0.0005
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 90
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2