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# mt5-small-multi-news

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2170
- Rouge1: 22.03
- Rouge2: 6.95
- Rougel: 18.41
- Rougelsum: 18.72

## Intended uses & limitations

Text summarization is the inteded use of this model. With further training the model could achieve better results.

## Training and evaluation data

For the training data we used 10000 samples from the multi-news train dataset.
For the evaluation data we used 500 samples from the multi-news evaluation dataset.

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 5.2732        | 1.0   | 1250 | 3.2170          | 22.03  | 6.95   | 18.41  | 18.72     |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["multi_news"], "metrics": ["rouge"], "base_model": "google/mt5-small", "model-index": [{"name": "mt5-small-multi-news", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "multi_news", "type": "multi_news", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "rouge", "value": 22.03, "name": "Rouge1"}, {"type": "rouge", "value": 6.95, "name": "Rouge2"}, {"type": "rouge", "value": 18.41, "name": "Rougel"}, {"type": "rouge", "value": 18.72, "name": "Rougelsum"}]}]}]}
transformers pytorch safetensors mt5 text2text-generation summarization generated_from_trainer en dataset:multi_news base_model:google/mt5-small license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #generated_from_trainer #en #dataset-multi_news #base_model-google/mt5-small #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-small-multi-news
====================


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


* Loss: 3.2170
* Rouge1: 22.03
* Rouge2: 6.95
* Rougel: 18.41
* Rougelsum: 18.72


Intended uses & limitations
---------------------------


Text summarization is the inteded use of this model. With further training the model could achieve better results.


Training and evaluation data
----------------------------


For the training data we used 10000 samples from the multi-news train dataset.
For the evaluation data we used 500 samples from the multi-news evaluation dataset.


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: 1


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 1 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #summarization #generated_from_trainer #en #dataset-multi_news #base_model-google/mt5-small #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: 1 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3