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# mt5-base-fce-e8-b16

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: 0.3758
- Rouge1: 84.5938
- Rouge2: 76.5987
- Rougel: 84.0063
- Rougelsum: 84.0286
- Gen Len: 15.4865

## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.5646        | 0.23  | 400   | 0.5403          | 83.2786 | 74.549  | 82.6906 | 82.6978   | 15.5126 |
| 0.6122        | 0.45  | 800   | 0.4896          | 84.3453 | 75.5159 | 83.7564 | 83.7691   | 15.4500 |
| 0.5041        | 0.68  | 1200  | 0.4294          | 84.2563 | 75.8731 | 83.6118 | 83.6071   | 15.4760 |
| 0.4594        | 0.9   | 1600  | 0.4136          | 84.7369 | 76.6048 | 84.1541 | 84.1573   | 15.4651 |
| 0.3861        | 1.13  | 2000  | 0.4121          | 84.6947 | 76.574  | 84.0885 | 84.095    | 15.4642 |
| 0.3382        | 1.35  | 2400  | 0.3899          | 84.5537 | 76.4381 | 83.9421 | 83.951    | 15.4651 |
| 0.3442        | 1.58  | 2800  | 0.3866          | 84.6272 | 76.6256 | 84.0616 | 84.0804   | 15.4674 |
| 0.3388        | 1.81  | 3200  | 0.3758          | 84.5938 | 76.5987 | 84.0063 | 84.0286   | 15.4865 |
| 0.3109        | 2.03  | 3600  | 0.3822          | 84.5223 | 76.5703 | 83.9217 | 83.9438   | 15.4710 |
| 0.2254        | 2.26  | 4000  | 0.3923          | 84.3225 | 76.4146 | 83.7686 | 83.7789   | 15.4596 |
| 0.236         | 2.48  | 4400  | 0.3932          | 84.4412 | 76.4434 | 83.8515 | 83.8815   | 15.4692 |
| 0.2395        | 2.71  | 4800  | 0.3849          | 84.2211 | 76.3678 | 83.6444 | 83.6462   | 15.4614 |
| 0.2458        | 2.93  | 5200  | 0.3850          | 84.3534 | 76.598  | 83.8321 | 83.8366   | 15.4587 |
| 0.1832        | 3.16  | 5600  | 0.3973          | 84.4197 | 76.7844 | 83.8758 | 83.8781   | 15.4678 |
| 0.1576        | 3.39  | 6000  | 0.4082          | 84.1841 | 76.4425 | 83.6272 | 83.618    | 15.4783 |
| 0.1635        | 3.61  | 6400  | 0.3996          | 84.2051 | 76.3261 | 83.6613 | 83.6599   | 15.4788 |
| 0.1667        | 3.84  | 6800  | 0.3940          | 84.4538 | 76.8139 | 83.8887 | 83.8886   | 15.4610 |
| 0.145         | 4.06  | 7200  | 0.4260          | 84.4028 | 76.8101 | 83.8844 | 83.8824   | 15.4628 |
| 0.107         | 4.29  | 7600  | 0.4403          | 84.3559 | 76.8066 | 83.8048 | 83.807    | 15.4587 |
| 0.1078        | 4.51  | 8000  | 0.4337          | 84.3045 | 76.8011 | 83.7587 | 83.7699   | 15.4742 |
| 0.1114        | 4.74  | 8400  | 0.4334          | 84.2865 | 76.5415 | 83.7221 | 83.718    | 15.4820 |
| 0.1104        | 4.97  | 8800  | 0.4273          | 84.3211 | 76.8211 | 83.7795 | 83.7726   | 15.4838 |
| 0.0732        | 5.19  | 9200  | 0.4787          | 84.3459 | 76.752  | 83.777  | 83.7552   | 15.4829 |
| 0.069         | 5.42  | 9600  | 0.4839          | 84.4351 | 76.8848 | 83.8682 | 83.8584   | 15.4811 |
| 0.0713        | 5.64  | 10000 | 0.4896          | 84.2962 | 76.7428 | 83.7387 | 83.7253   | 15.4829 |
| 0.0716        | 5.87  | 10400 | 0.4788          | 84.3068 | 76.7969 | 83.74   | 83.7402   | 15.4747 |
| 0.0613        | 6.09  | 10800 | 0.5252          | 84.4256 | 77.008  | 83.8688 | 83.8828   | 15.4815 |
| 0.0439        | 6.32  | 11200 | 0.5398          | 84.3753 | 76.8235 | 83.793  | 83.7986   | 15.4815 |
| 0.0452        | 6.55  | 11600 | 0.5377          | 84.4467 | 76.8923 | 83.8893 | 83.8818   | 15.4815 |
| 0.0434        | 6.77  | 12000 | 0.5347          | 84.3734 | 76.811  | 83.8108 | 83.8063   | 15.4843 |
| 0.0424        | 7.0   | 12400 | 0.5380          | 84.4558 | 76.9239 | 83.9033 | 83.9022   | 15.4751 |
| 0.0296        | 7.22  | 12800 | 0.5808          | 84.332  | 76.8729 | 83.7923 | 83.7826   | 15.4774 |
| 0.0287        | 7.45  | 13200 | 0.5956          | 84.4744 | 77.0945 | 83.9222 | 83.9228   | 15.4843 |
| 0.0283        | 7.67  | 13600 | 0.5966          | 84.4271 | 77.0661 | 83.877  | 83.8712   | 15.4829 |
| 0.0285        | 7.9   | 14000 | 0.5983          | 84.4562 | 77.0334 | 83.8987 | 83.8985   | 15.4824 |


### Framework versions

- Transformers 4.28.1
- Pytorch 1.11.0a0+b6df043
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-fce-e8-b16", "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-base-fce-e8-b16
===================


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: 0.3758
* Rouge1: 84.5938
* Rouge2: 76.5987
* Rougel: 84.0063
* Rougelsum: 84.0286
* Gen Len: 15.4865


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: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 8


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 1.11.0a0+b6df043
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 1.11.0a0+b6df043
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adafactor
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 1.11.0a0+b6df043
* Datasets 2.12.0
* Tokenizers 0.13.3