
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# mt5-small-test-ged-mlsum_max_target_length_10

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the mlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3341
- Rouge1: 74.8229
- Rouge2: 68.1808
- Rougel: 74.8297
- Rougelsum: 74.8414

## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.5565        | 1.0   | 33296  | 0.3827          | 69.9041 | 62.821  | 69.8709 | 69.8924   |
| 0.2636        | 2.0   | 66592  | 0.3552          | 72.0701 | 65.4937 | 72.0787 | 72.091    |
| 0.2309        | 3.0   | 99888  | 0.3525          | 72.5071 | 65.8026 | 72.5132 | 72.512    |
| 0.2109        | 4.0   | 133184 | 0.3346          | 74.0842 | 67.4776 | 74.0887 | 74.0968   |
| 0.1972        | 5.0   | 166480 | 0.3398          | 74.6051 | 68.6024 | 74.6177 | 74.6365   |
| 0.1867        | 6.0   | 199776 | 0.3283          | 74.9022 | 68.2146 | 74.9023 | 74.926    |
| 0.1785        | 7.0   | 233072 | 0.3325          | 74.8631 | 68.2468 | 74.8843 | 74.9026   |
| 0.1725        | 8.0   | 266368 | 0.3341          | 74.8229 | 68.1808 | 74.8297 | 74.8414   |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["mlsum"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-test-ged-mlsum_max_target_length_10", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "mlsum", "type": "mlsum", "args": "es"}, "metrics": [{"type": "rouge", "value": 74.8229, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard mt5 text2text-generation summarization generated_from_trainer dataset:mlsum license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-mlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-small-test-ged-mlsum\_max\_target\_length\_10
=================================================


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


* Loss: 0.3341
* Rouge1: 74.8229
* Rouge2: 68.1808
* Rougel: 74.8297
* Rougelsum: 74.8414


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: 5.6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8


### Training results



### Framework versions


* Transformers 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-mlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8 ### Training results ### Framework versions


* Transformers 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-mlsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8### Training results### Framework versions


* Transformers 4.20.0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1