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# NASca-finetuned-diego-2

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: 1.9197
- Rouge1: 0.2930
- Rouge2: 0.1226
- Rougel: 0.2327
- Rougelsum: 0.2373

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|
| 1.4353        | 0.09  | 15000  | 1.7848          | 0.2816 | 0.1157 | 0.2258 | 0.2305    |
| 1.3237        | 1.0   | 159149 | 1.7707          | 0.2831 | 0.1171 | 0.2267 | 0.2318    |
| 1.3836        | 2.0   | 318298 | 1.7924          | 0.2899 | 0.1211 | 0.2316 | 0.2361    |
| 1.274         | 3.0   | 477447 | 1.8809          | 0.2921 | 0.1225 | 0.2323 | 0.2368    |
| 1.2289        | 4.0   | 636596 | 1.9128          | 0.2926 | 0.1223 | 0.2322 | 0.2368    |
| 1.2424        | 5.0   | 795745 | 1.9197          | 0.2930 | 0.1226 | 0.2327 | 0.2373    |


### 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-2", "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-2
=======================


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


* Loss: 1.9197
* Rouge1: 0.2930
* Rouge2: 0.1226
* Rougel: 0.2327
* Rougelsum: 0.2373


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