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# mt5-small-news-final

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the hebrew_news dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.4946
- Rouge2: 0.0432
- Rougel: 0.4922
- Rougelsum: 0.4919
- Gen Len: 6.0117

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0           | 1.0   | 1029 | nan             | 0.4946 | 0.0432 | 0.4922 | 0.4919    | 6.0117  |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["hebrew_news"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-news-final", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "hebrew_news", "type": "hebrew_news", "config": "hebrew_news", "split": "train", "args": "hebrew_news"}, "metrics": [{"type": "rouge", "value": 0.4946, "name": "Rouge1"}]}]}]}
transformers pytorch mt5 text2text-generation generated_from_trainer dataset:hebrew_news license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #dataset-hebrew_news #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-small-news-final
====================


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


* Loss: nan
* Rouge1: 0.4946
* Rouge2: 0.0432
* Rougel: 0.4922
* Rougelsum: 0.4919
* Gen Len: 6.0117


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: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #dataset-hebrew_news #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: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.11.0