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# BART_corrector

This model is a fine-tuned version of [ainize/bart-base-cnn](https://huggingface.co/ainize/bart-base-cnn) on a homemade dataset. Each sample of the dataset is an english sentence that has been duplicated 10 times and where random errors (7%) were added.

It achieves the following results on the evaluation set:
- Loss: 0.0025
- Rouge1: 81.4214
- Rouge2: 80.2027
- Rougel: 81.4202
- Rougelsum: 81.4241
- Gen Len: 19.3962

## Model description

More information needed

## Intended uses & limitations

The goal of this model is to correct a sentence, given several versions of it with various mistakes.

Text sample :
_TheIdeSbgn of thh Eiffel Toweg is aYtribeted to Ma. . ahd design of The Eijfel Tower is attribQtedBto ta. . The designYof the EifZel Tower Vs APtWibuteQ to Ma. . The xeQign oC the EiffelXTower ik attributed to Ma. . ghebFesign of theSbiffel TJwer is atMributed to Ma. . The desOBn of thQ Eiffel ToweP isfattributnd toBMa. . The design of the EBfUel Fower is JtAriOuted tx Ma. . The design of Jhe ENffel LoweF is aptrVbuted Lo Ma. . The deslgX of the lPffel Towermis attributedhtohMa. . The desRgn of thekSuffel Tower is Ttkribufed to Ma. ._

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.0071        | 1.0   | 2365 | 0.0039          | 81.3664 | 80.0861 | 81.3601 | 81.3667   | 19.3967 |
| 0.0033        | 2.0   | 4730 | 0.0029          | 81.3937 | 80.1548 | 81.3902 | 81.3974   | 19.3961 |
| 0.0018        | 3.0   | 7095 | 0.0029          | 81.3838 | 80.1404 | 81.385  | 81.3878   | 19.3965 |
| 0.001         | 4.0   | 9460 | 0.0025          | 81.4214 | 80.2027 | 81.4202 | 81.4241   | 19.3962 |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "BART_corrector", "results": []}]}
transformers pytorch tensorboard bart text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

BART\_corrector
===============


This model is a fine-tuned version of ainize/bart-base-cnn on a homemade dataset. Each sample of the dataset is an english sentence that has been duplicated 10 times and where random errors (7%) were added.


It achieves the following results on the evaluation set:


* Loss: 0.0025
* Rouge1: 81.4214
* Rouge2: 80.2027
* Rougel: 81.4202
* Rougelsum: 81.4241
* Gen Len: 19.3962


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


More information needed


Intended uses & limitations
---------------------------


The goal of this model is to correct a sentence, given several versions of it with various mistakes.


Text sample :
*TheIdeSbgn of thh Eiffel Toweg is aYtribeted to Ma. . ahd design of The Eijfel Tower is attribQtedBto ta. . The designYof the EifZel Tower Vs APtWibuteQ to Ma. . The xeQign oC the EiffelXTower ik attributed to Ma. . ghebFesign of theSbiffel TJwer is atMributed to Ma. . The desOBn of thQ Eiffel ToweP isfattributnd toBMa. . The design of the EBfUel Fower is JtAriOuted tx Ma. . The design of Jhe ENffel LoweF is aptrVbuted Lo Ma. . The deslgX of the lPffel Towermis attributedhtohMa. . The desRgn of thekSuffel Tower is Ttkribufed to Ma. .*


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 4
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 4
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1