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# NASca-finetuned-diego-4-amb-metriques-anonim

This model is a fine-tuned version of [ELiRF/NASCA](https://huggingface.co/ELiRF/NASCA) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8164
- Rouge1: 0.3868
- Rouge2: 0.3690
- Rougel: 0.3815
- Rougelsum: 0.3820
- Classificacio Format Correcte: 0.0
- Classificacio Recall: 0.0
- Classificacio Precision: 0.0
- Classificacio F1: 0.0

## 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: 1.3739167643078955e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Classificacio Format Correcte | Classificacio Recall | Classificacio Precision | Classificacio F1 |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-----------------------------:|:--------------------:|:-----------------------:|:----------------:|
| 1.3365        | 1.0   | 159149 | 1.7901          | 0.3859 | 0.3681 | 0.3806 | 0.3812    | 0.0                           | 0.0                  | 0.0                     | 0.0              |
| 1.3045        | 2.0   | 318298 | 1.7997          | 0.3864 | 0.3686 | 0.3811 | 0.3816    | 0.0                           | 0.0                  | 0.0                     | 0.0              |
| 1.3045        | 3.0   | 477447 | 1.8067          | 0.3866 | 0.3688 | 0.3812 | 0.3818    | 0.0                           | 0.0                  | 0.0                     | 0.0              |
| 1.2534        | 4.0   | 636596 | 1.8133          | 0.3869 | 0.3692 | 0.3816 | 0.3822    | 0.0                           | 0.0                  | 0.0                     | 0.0              |
| 1.3103        | 5.0   | 795745 | 1.8164          | 0.3868 | 0.3690 | 0.3815 | 0.3820    | 0.0                           | 0.0                  | 0.0                     | 0.0              |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "NASca-finetuned-diego-4-amb-metriques-anonim", "results": []}]}
transformers pytorch bart text2text-generation summarization generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

NASca-finetuned-diego-4-amb-metriques-anonim
============================================


This model is a fine-tuned version of ELiRF/NASCA on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8164
* Rouge1: 0.3868
* Rouge2: 0.3690
* Rougel: 0.3815
* Rougelsum: 0.3820
* Classificacio Format Correcte: 0.0
* Classificacio Recall: 0.0
* Classificacio Precision: 0.0
* Classificacio F1: 0.0


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: 1.3739167643078955e-06
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* 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 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.3739167643078955e-06
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* 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 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.3739167643078955e-06
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* 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 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.3739167643078955e-06
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* 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 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2