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# flan-t5-qg-LQ-tarek-test

This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5322
- Rouge1: 23.9922
- Rouge2: 6.7099
- Rougel: 21.5985
- Rougelsum: 21.5946
- Gen Len: 16.1304
- Meteor: {'meteor': 0.17861183292998817}
- Bleu: {'bleu': 0.032560412750946656, 'precisions': [0.31838712287431764, 0.07483151167147811, 0.02936351414577522, 0.011773119505596449], 'brevity_penalty': 0.6077920327728761, 'length_ratio': 0.6675912775198823, 'translation_length': 210109, 'reference_length': 314727}

## 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: 0.0003
- 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: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len | Meteor                          | Bleu                                                                                                                                                                                                                                                                     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|:-------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 1.565         | 1.0   | 23583 | 1.5526          | 23.7548 | 6.5396 | 21.3855 | 21.3784   | 16.1645 | {'meteor': 0.17704033979826234} | {'bleu': 0.03244683754192353, 'precisions': [0.31640932313437786, 0.07308847939823084, 0.029126556774398793, 0.011756263748055154], 'brevity_penalty': 0.6116573626703378, 'length_ratio': 0.6704286572172073, 'translation_length': 211002, 'reference_length': 314727} |
| 1.4823        | 2.0   | 47166 | 1.5322          | 23.9922 | 6.7099 | 21.5985 | 21.5946   | 16.1304 | {'meteor': 0.17861183292998817} | {'bleu': 0.032560412750946656, 'precisions': [0.31838712287431764, 0.07483151167147811, 0.02936351414577522, 0.011773119505596449], 'brevity_penalty': 0.6077920327728761, 'length_ratio': 0.6675912775198823, 'translation_length': 210109, 'reference_length': 314727} |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge", "bleu"], "model-index": [{"name": "flan-t5-qg-LQ-tarek-test", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

flan-t5-qg-LQ-tarek-test
========================


This model is a fine-tuned version of google/flan-t5-small on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.5322
* Rouge1: 23.9922
* Rouge2: 6.7099
* Rougel: 21.5985
* Rougelsum: 21.5946
* Gen Len: 16.1304
* Meteor: {'meteor': 0.17861183292998817}
* Bleu: {'bleu': 0.032560412750946656, 'precisions': [0.31838712287431764, 0.07483151167147811, 0.02936351414577522, 0.011773119505596449], 'brevity\_penalty': 0.6077920327728761, 'length\_ratio': 0.6675912775198823, 'translation\_length': 210109, 'reference\_length': 314727}


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: 0.0003
* 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: 2


### 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: 0.0003
* 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: 2 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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: 2 ### Training results ### Framework versions


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