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# mt5-small-finetuned-28jan-2

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5078
- Rouge1: 18.7485
- Rouge2: 5.8034
- Rougel: 18.5163
- Rougelsum: 18.4817

## 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: 0.0001
- train_batch_size: 9
- eval_batch_size: 9
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 5.9652        | 1.0   | 242  | 2.8048          | 14.036  | 4.037  | 13.7766 | 13.8254   |
| 3.474         | 2.0   | 484  | 2.7485          | 16.821  | 4.8051 | 16.6168 | 16.5782   |
| 3.2101        | 3.0   | 726  | 2.6444          | 17.2659 | 5.1077 | 16.9501 | 16.8998   |
| 3.0555        | 4.0   | 968  | 2.6408          | 17.3002 | 4.8657 | 17.0414 | 16.9794   |
| 2.9515        | 5.0   | 1210 | 2.5860          | 17.6468 | 5.3816 | 17.3755 | 17.3434   |
| 2.8694        | 6.0   | 1452 | 2.5586          | 18.3932 | 5.3896 | 18.2521 | 18.0748   |
| 2.7898        | 7.0   | 1694 | 2.5325          | 18.4954 | 5.5609 | 18.2994 | 18.2112   |
| 2.7436        | 8.0   | 1936 | 2.5431          | 18.8172 | 5.9338 | 18.4693 | 18.4324   |
| 2.6955        | 9.0   | 2178 | 2.5588          | 18.7895 | 6.1003 | 18.3593 | 18.3268   |
| 2.6571        | 10.0  | 2420 | 2.5079          | 19.2525 | 5.8268 | 19.0279 | 18.9846   |
| 2.629         | 11.0  | 2662 | 2.5118          | 18.9191 | 5.9877 | 18.6505 | 18.6      |
| 2.5998        | 12.0  | 2904 | 2.5070          | 18.7181 | 5.9061 | 18.4432 | 18.3931   |
| 2.5692        | 13.0  | 3146 | 2.5014          | 18.4412 | 6.1983 | 18.2394 | 18.1618   |
| 2.5751        | 14.0  | 3388 | 2.5125          | 18.7014 | 5.9729 | 18.4366 | 18.406    |
| 2.55          | 15.0  | 3630 | 2.5078          | 18.7485 | 5.8034 | 18.5163 | 18.4817   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-28jan-2", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation summarization generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-small-finetuned-28jan-2
===========================


This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 2.5078
* Rouge1: 18.7485
* Rouge2: 5.8034
* Rougel: 18.5163
* Rougelsum: 18.4817


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: 0.0001
* train\_batch\_size: 9
* eval\_batch\_size: 9
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 9
* eval\_batch\_size: 9
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #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: 0.0001
* train\_batch\_size: 9
* eval\_batch\_size: 9
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #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: 0.0001
* train\_batch\_size: 9
* eval\_batch\_size: 9
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2