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# flan-t5-qg-tarek-test-SQUAD

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6101
- Rouge1: 51.7909
- Rouge2: 29.105
- Rougel: 47.9342
- Rougelsum: 47.9206
- Gen Len: 14.3335
- Meteor: {'meteor': 0.46880070390780776}
- Bleu: {'bleu': 0.20591622940642762, 'precisions': [0.5204716701541998, 0.2619394913622878, 0.1654269529647818, 0.10906716558470096], 'brevity_penalty': 0.9246257500068106, 'length_ratio': 0.927328752862794, 'translation_length': 111349, 'reference_length': 120075}

## 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: 0.0005
- 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                                                                                                                                                                                                                                                                 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.6576        | 1.0   | 10950 | 0.6301          | 50.2515 | 27.5194 | 46.5001 | 46.4993   | 14.0607 | {'meteor': 0.44978235923873977} | {'bleu': 0.19104342466719015, 'precisions': [0.5159747201089206, 0.2503337307531615, 0.15519111953679066, 0.10098320626550465], 'brevity_penalty': 0.9006709188377698, 'length_ratio': 0.9052925255048928, 'translation_length': 108703, 'reference_length': 120075} |
| 0.5315        | 2.0   | 21900 | 0.6101          | 51.7909 | 29.105  | 47.9342 | 47.9206   | 14.3335 | {'meteor': 0.46880070390780776} | {'bleu': 0.20591622940642762, 'precisions': [0.5204716701541998, 0.2619394913622878, 0.1654269529647818, 0.10906716558470096], 'brevity_penalty': 0.9246257500068106, 'length_ratio': 0.927328752862794, 'translation_length': 111349, 'reference_length': 120075}   |


### 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"], "datasets": ["squad"], "metrics": ["rouge", "bleu"], "model-index": [{"name": "flan-t5-qg-tarek-test-SQUAD", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation", "args": "plain_text"}, "metrics": [{"type": "rouge", "value": 51.7909, "name": "Rouge1"}, {"type": "bleu", "value": {"bleu": 0.20591622940642762, "precisions": [0.5204716701541998, 0.2619394913622878, 0.1654269529647818, 0.10906716558470096], "brevity_penalty": 0.9246257500068106, "length_ratio": 0.927328752862794, "translation_length": 111349, "reference_length": 120075}, "name": "Bleu"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:squad license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

flan-t5-qg-tarek-test-SQUAD
===========================


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


* Loss: 0.6101
* Rouge1: 51.7909
* Rouge2: 29.105
* Rougel: 47.9342
* Rougelsum: 47.9206
* Gen Len: 14.3335
* Meteor: {'meteor': 0.46880070390780776}
* Bleu: {'bleu': 0.20591622940642762, 'precisions': [0.5204716701541998, 0.2619394913622878, 0.1654269529647818, 0.10906716558470096], 'brevity\_penalty': 0.9246257500068106, 'length\_ratio': 0.927328752862794, 'translation\_length': 111349, 'reference\_length': 120075}


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: 0.0005
* 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: 0.0005
* 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 #dataset-squad #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: 0.0005
* 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