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# polish_transliterator_BART

This model is a fine-tuned version of [sshleifer/bart-tiny-random](https://huggingface.co/sshleifer/bart-tiny-random) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 9.5795
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 2.0

## 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: 3e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 10.3014       | 1.0   | 572   | 10.2707         | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 10.2465       | 2.0   | 1144  | 10.2013         | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 10.1717       | 3.0   | 1716  | 10.1342         | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 10.1086       | 4.0   | 2288  | 10.0704         | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 10.0524       | 5.0   | 2860  | 10.0102         | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.9976        | 6.0   | 3432  | 9.9539          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.8907        | 7.0   | 4004  | 9.9018          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.8424        | 8.0   | 4576  | 9.8536          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.8046        | 9.0   | 5148  | 9.8095          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.7581        | 10.0  | 5720  | 9.7693          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.7253        | 11.0  | 6292  | 9.7331          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.698         | 12.0  | 6864  | 9.7008          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.6611        | 13.0  | 7436  | 9.6723          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.6125        | 14.0  | 8008  | 9.6477          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.5928        | 15.0  | 8580  | 9.6269          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.5747        | 16.0  | 9152  | 9.6099          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.5613        | 17.0  | 9724  | 9.5966          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.5418        | 18.0  | 10296 | 9.5871          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.539         | 19.0  | 10868 | 9.5814          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |
| 9.5366        | 20.0  | 11440 | 9.5795          | 0.0    | 0.0    | 0.0    | 0.0       | 2.0     |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

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

polish\_transliterator\_BART
============================


This model is a fine-tuned version of sshleifer/bart-tiny-random on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 9.5795
* Rouge1: 0.0
* Rouge2: 0.0
* Rougel: 0.0
* Rougelsum: 0.0
* Gen Len: 2.0


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: 3e-06
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-06
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-06
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-06
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3