
<!-- 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. -->

# n3wtou/mt5-small-finedtuned-4-swahili

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on csebuetnlp/xlsum dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.4419
- Validation Loss: 2.4809
- Epoch: 9

## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0003, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0003, 'decay_steps': 19900, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 100, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: mixed_float16

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 5.6636     | 2.9818          | 0     |
| 3.7789     | 2.7822          | 1     |
| 3.3841     | 2.6840          | 2     |
| 3.1496     | 2.6238          | 3     |
| 2.9656     | 2.5816          | 4     |
| 2.8134     | 2.5522          | 5     |
| 2.6914     | 2.5315          | 6     |
| 2.5935     | 2.4980          | 7     |
| 2.5056     | 2.4764          | 8     |
| 2.4419     | 2.4809          | 9     |


### Framework versions

- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
{"language": ["sw"], "license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["csebuetnlp/xlsum"], "metrics": ["f1", "rouge"], "model-index": [{"name": "n3wtou/mt5-small-finedtuned-4-swahili", "results": []}]}
transformers tf mt5 text2text-generation generated_from_keras_callback sw dataset:csebuetnlp/xlsum license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #sw #dataset-csebuetnlp/xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

n3wtou/mt5-small-finedtuned-4-swahili
=====================================


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


* Train Loss: 2.4419
* Validation Loss: 2.4809
* Epoch: 9


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:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 0.0003, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 0.0003, 'decay\_steps': 19900, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 100, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.001}
* training\_precision: mixed\_float16


### Training results



### Framework versions


* Transformers 4.30.2
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 0.0003, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 0.0003, 'decay\_steps': 19900, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 100, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.001}
* training\_precision: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.30.2
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #sw #dataset-csebuetnlp/xlsum #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': 'WarmUp', 'config': {'initial\_learning\_rate': 0.0003, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 0.0003, 'decay\_steps': 19900, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 100, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.001}
* training\_precision: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.30.2
* TensorFlow 2.12.0
* Datasets 2.12.0
* Tokenizers 0.13.3