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# T5 (small) finetuned-turk-text-simplification

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1001
- Rouge2 Precision: 0.6825
- Rouge2 Recall: 0.4542
- Rouge2 Fmeasure: 0.5221

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.4318        | 1.0   | 500  | 0.1053          | 0.682            | 0.4533        | 0.5214          |
| 0.0977        | 2.0   | 1000 | 0.1019          | 0.683            | 0.4545        | 0.5225          |
| 0.0938        | 3.0   | 1500 | 0.1010          | 0.6828           | 0.4547        | 0.5226          |
| 0.0916        | 4.0   | 2000 | 0.1003          | 0.6829           | 0.4545        | 0.5225          |
| 0.0906        | 5.0   | 2500 | 0.1001          | 0.6825           | 0.4542        | 0.5221          |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "widget": [{"text": "simplify: the incident has been the subject of numerous reports as to ethics in scholarship ."}, {"text": "simplify: the historical method comprises the techniques and guidelines by which historians use primary sources and other evidence to research and then to write history ."}, {"text": "simplify: none of the authors , contributors , sponsors , administrators , vandals , or anyone else connected with wikipedia , in any way whatsoever , can be responsible for your use of the information contained in or linked from these web pages ."}, {"text": "simplify: oregano is an indispensable ingredient in greek cuisine ."}], "inference": {"parameters": {"temperature": 1.5, "max_length": 256, "do_sample": true, "num_beams": 3}}, "model-index": [{"name": "t5-small-finetuned-turk-text-simplification", "results": []}]}
transformers pytorch tensorboard safetensors t5 text2text-generation generated_from_trainer en license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

T5 (small) finetuned-turk-text-simplification
=============================================


This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1001
* Rouge2 Precision: 0.6825
* Rouge2 Recall: 0.4542
* Rouge2 Fmeasure: 0.5221


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: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1