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# mt5-small-cnn-dm-kaggle-en

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail) dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2499
- Rouge1: 25.24
- Rouge2: 11.4243
- Rougel: 22.1837
- Rougelsum: 22.2212

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 3.7959        | 1.0   | 2668  | 3.3796          | 24.6486 | 10.95   | 21.6027 | 21.6371   |
| 3.6116        | 2.0   | 5336  | 3.2863          | 25.0485 | 11.2204 | 21.9543 | 21.9949   |
| 3.5264        | 3.0   | 8004  | 3.2426          | 25.1151 | 11.2913 | 22.0778 | 22.1108   |
| 3.4845        | 4.0   | 10672 | 3.2499          | 25.24   | 11.4243 | 22.1837 | 22.2212   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-cnn-dm-kaggle-en", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation summarization generated_from_trainer en dataset:cnn_dailymail license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #en #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-small-cnn-dm-kaggle-en
==========================


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


* Loss: 3.2499
* Rouge1: 25.24
* Rouge2: 11.4243
* Rougel: 22.1837
* Rougelsum: 22.2212


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #en #dataset-cnn_dailymail #license-apache-2.0 #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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3