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# mt5-teste-full-length

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: 0.5750
- Rouge1: 0.4784
- Rouge2: 0.3008
- Rougel: 0.4185
- Rougelsum: 0.4212

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.9357        | 0.16  | 100  | 2.5583          | 0.2654 | 0.0431 | 0.1946 | 0.1951    |
| 1.9974        | 0.33  | 200  | 1.7104          | 0.1803 | 0.0817 | 0.1712 | 0.1726    |
| 1.4803        | 0.49  | 300  | 1.4404          | 0.1770 | 0.0695 | 0.1707 | 0.1727    |
| 1.2432        | 0.65  | 400  | 1.0519          | 0.2809 | 0.1314 | 0.2509 | 0.2511    |
| 0.8186        | 0.82  | 500  | 0.7386          | 0.3487 | 0.1767 | 0.2894 | 0.2903    |
| 0.791         | 0.98  | 600  | 0.7135          | 0.3634 | 0.1912 | 0.3108 | 0.3108    |
| 0.6697        | 1.15  | 700  | 0.6835          | 0.3874 | 0.1900 | 0.3123 | 0.3131    |
| 0.7146        | 1.31  | 800  | 0.6657          | 0.3816 | 0.2209 | 0.3414 | 0.3428    |
| 0.6957        | 1.47  | 900  | 0.6498          | 0.3878 | 0.2045 | 0.3336 | 0.3339    |
| 0.6737        | 1.64  | 1000 | 0.6332          | 0.4094 | 0.2219 | 0.3524 | 0.3535    |
| 0.6537        | 1.8   | 1100 | 0.6369          | 0.4401 | 0.2621 | 0.3629 | 0.3630    |
| 0.6746        | 1.96  | 1200 | 0.6169          | 0.4369 | 0.2326 | 0.3566 | 0.3574    |
| 0.5961        | 2.13  | 1300 | 0.6171          | 0.4364 | 0.2464 | 0.3666 | 0.3670    |
| 0.5829        | 2.29  | 1400 | 0.6122          | 0.4539 | 0.2683 | 0.3813 | 0.3825    |
| 0.6336        | 2.45  | 1500 | 0.5993          | 0.4347 | 0.2548 | 0.3660 | 0.3689    |
| 0.5754        | 2.62  | 1600 | 0.5905          | 0.4575 | 0.2789 | 0.3856 | 0.3857    |
| 0.5984        | 2.78  | 1700 | 0.5872          | 0.4630 | 0.2768 | 0.3915 | 0.3929    |
| 0.5966        | 2.95  | 1800 | 0.5944          | 0.4605 | 0.2753 | 0.3822 | 0.3828    |
| 0.5288        | 3.11  | 1900 | 0.5955          | 0.4520 | 0.2651 | 0.3874 | 0.3887    |
| 0.5316        | 3.27  | 2000 | 0.5841          | 0.4649 | 0.2820 | 0.4052 | 0.4056    |
| 0.5332        | 3.44  | 2100 | 0.5765          | 0.4861 | 0.3046 | 0.4021 | 0.4050    |
| 0.5296        | 3.6   | 2200 | 0.5812          | 0.4610 | 0.2815 | 0.3976 | 0.4021    |
| 0.5215        | 3.76  | 2300 | 0.5757          | 0.4724 | 0.2947 | 0.4122 | 0.4164    |
| 0.5399        | 3.93  | 2400 | 0.5750          | 0.4784 | 0.3008 | 0.4185 | 0.4212    |


### 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-teste-full-length", "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-teste-full-length
=====================


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: 0.5750
* Rouge1: 0.4784
* Rouge2: 0.3008
* Rougel: 0.4185
* Rougelsum: 0.4212


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: 4


### 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: 4 ### 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: 4 ### Training results ### Framework versions


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