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

# gbharathi80/mt5-small-finetuned-amazon-en-es

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an amazon reviews dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.2325
- Validation Loss: 3.4452
- Epoch: 7

## Model description

This is a fine-tuned version of the google/mt5-small model for translation tasks from English to Spanish for text summarization
## Intended uses & limitations

multi lingual text summarization. model trained using spanish and english revirwes
## Training and evaluation data

DatasetDict({
    train: Dataset({
        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],
        num_rows: 200000
    })
    validation: Dataset({
        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],
        num_rows: 5000
    })
    test: Dataset({
        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],
        num_rows: 5000
    })
})

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 9672, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.7747    | 4.7510          | 0     |
| 6.3001     | 4.0096          | 1     |
| 5.4388     | 3.7376          | 2     |
| 4.9710     | 3.6136          | 3     |
| 4.6689     | 3.5349          | 4     |
| 4.4622     | 3.4885          | 5     |
| 4.3101     | 3.4537          | 6     |
| 4.2325     | 3.4452          | 7     |


### Framework versions

- Transformers 4.21.1
- TensorFlow 2.9.1
- Datasets 2.4.0
- Tokenizers 0.12.1
{"language": ["es", "en"], "license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["amazon_reviews_multi"], "metrics": ["bleu", "rouge"], "pipeline_tag": "summarization", "model-index": [{"name": "gbharathi80/mt5-small-finetuned-amazon-en-es", "results": []}]}
transformers tf mt5 text2text-generation generated_from_keras_callback summarization es en dataset:amazon_reviews_multi license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #summarization #es #en #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

gbharathi80/mt5-small-finetuned-amazon-en-es
============================================


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


* Train Loss: 4.2325
* Validation Loss: 3.4452
* Epoch: 7


Model description
-----------------


This is a fine-tuned version of the google/mt5-small model for translation tasks from English to Spanish for text summarization


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


multi lingual text summarization. model trained using spanish and english revirwes


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


DatasetDict({
train: Dataset({
features: ['review\_id', 'product\_id', 'reviewer\_id', 'stars', 'review\_body', 'review\_title', 'language', 'product\_category'],
num\_rows: 200000
})
validation: Dataset({
features: ['review\_id', 'product\_id', 'reviewer\_id', 'stars', 'review\_body', 'review\_title', 'language', 'product\_category'],
num\_rows: 5000
})
test: Dataset({
features: ['review\_id', 'product\_id', 'reviewer\_id', 'stars', 'review\_body', 'review\_title', 'language', 'product\_category'],
num\_rows: 5000
})
})


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5.6e-05, 'decay\_steps': 9672, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16


### Training results



### Framework versions


* Transformers 4.21.1
* TensorFlow 2.9.1
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5.6e-05, 'decay\_steps': 9672, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.21.1
* TensorFlow 2.9.1
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #summarization #es #en #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5.6e-05, 'decay\_steps': 9672, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.21.1
* TensorFlow 2.9.1
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #summarization #es #en #dataset-amazon_reviews_multi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5.6e-05, 'decay\_steps': 9672, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16### Training results### Framework versions


* Transformers 4.21.1
* TensorFlow 2.9.1
* Datasets 2.4.0
* Tokenizers 0.12.1