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# flan-t5-base-finetuned-length_control_token

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0276
- Sacrebleu: 16.2445

## Model description

This model was trained on a dataset called PWKP-GPT3-LENGTH-CONTROL-40BUCKETS.
The dataset contains 30k instances taken from PWKP, then processed through GPT3 to obtain simplifications.
The 30k instances come from: 10k which were supposed to generate very long simplifications, 
10k which were supposed to generate very short simplifications, and 10k without specifying the simplicity level.
The model does not sucessfuly work on these buckets.
There exists another dataset, the PWKP-GPT3-LENGTH-CONTROL-4BUCKETS, but it was never trained on something.
Those buckets are also rather unbalanced. 

The idea comes from 
Controllable Sentence Simplification
Louis Martin, https://arxiv.org/pdf/1910.02677.pdf

It was fine-tuned on the FLAN-T5-base model.

## 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: 5.6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Sacrebleu |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|
| 1.3257        | 1.0   | 1782  | 1.0906          | 15.4208   |
| 1.1718        | 2.0   | 3564  | 1.0648          | 15.5358   |
| 1.0972        | 3.0   | 5346  | 1.0484          | 15.8113   |
| 1.0472        | 4.0   | 7128  | 1.0394          | 16.0159   |
| 1.0092        | 5.0   | 8910  | 1.0305          | 16.1341   |
| 0.9858        | 6.0   | 10692 | 1.0276          | 16.2445   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["simplification", "generated_from_trainer"], "metrics": ["sacrebleu"], "model-index": [{"name": "flan-t5-base-finetuned-length_control_token", "results": []}]}
transformers pytorch t5 text2text-generation simplification generated_from_trainer arxiv:1910.02677 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #simplification #generated_from_trainer #arxiv-1910.02677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

flan-t5-base-finetuned-length\_control\_token
=============================================


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


* Loss: 1.0276
* Sacrebleu: 16.2445


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


This model was trained on a dataset called PWKP-GPT3-LENGTH-CONTROL-40BUCKETS.
The dataset contains 30k instances taken from PWKP, then processed through GPT3 to obtain simplifications.
The 30k instances come from: 10k which were supposed to generate very long simplifications,
10k which were supposed to generate very short simplifications, and 10k without specifying the simplicity level.
The model does not sucessfuly work on these buckets.
There exists another dataset, the PWKP-GPT3-LENGTH-CONTROL-4BUCKETS, but it was never trained on something.
Those buckets are also rather unbalanced.


The idea comes from
Controllable Sentence Simplification
Louis Martin, URL


It was fine-tuned on the FLAN-T5-base model.


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: 5.6e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #simplification #generated_from_trainer #arxiv-1910.02677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #simplification #generated_from_trainer #arxiv-1910.02677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2