# grizzled-interest-2023-03-29

This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the mtc/newsum2021 dataset.
It achieves the following results on the test set:
- Loss: 3.5178
- Rouge1: 31.4512
- Rouge2: 11.0965
- Rougel: 21.5021
- Rougelsum: 28.634
- Gen Len: 75.755

## 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-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.6815        | 0.89  | 500  | 3.5617          | 29.5414 | 10.5201 | 20.056  | 27.2581   | 86.07   |
| 3.4132        | 1.79  | 1000 | 3.4133          | 29.6393 | 9.9545  | 19.6903 | 27.0861   | 96.545  |
| 3.198         | 2.68  | 1500 | 3.3693          | 29.8614 | 10.4517 | 20.1728 | 27.3879   | 94.31   |
| 3.0292        | 3.58  | 2000 | 3.3370          | 30.6444 | 11.5935 | 21.1955 | 28.2699   | 87.355  |
| 2.901         | 4.47  | 2500 | 3.3440          | 30.7453 | 11.111  | 21.2076 | 28.269    | 88.365  |
| 2.7832        | 5.37  | 3000 | 3.3758          | 30.4995 | 10.9025 | 20.6601 | 28.0575   | 104.655 |
| 2.6965        | 6.26  | 3500 | 3.3793          | 31.2287 | 11.5544 | 21.1909 | 28.738    | 88.47   |
| 2.6475        | 7.16  | 4000 | 3.4083          | 32.0341 | 11.9417 | 22.2785 | 29.2495   | 84.095  |
| 2.6196        | 8.05  | 4500 | 3.4007          | 30.8963 | 11.3811 | 21.3146 | 28.3222   | 90.875  |
| 2.5574        | 8.94  | 5000 | 3.4104          | 32.3867 | 12.0469 | 21.9831 | 29.5205   | 87.46   |
| 2.4977        | 9.84  | 5500 | 3.4340          | 32.5857 | 12.5072 | 22.6288 | 30.1168   | 79.87   |
| 2.4362        | 10.73 | 6000 | 3.4626          | 31.9121 | 11.8577 | 22.3647 | 29.3822   | 85.17   |
| 2.3977        | 11.63 | 6500 | 3.4737          | 32.0202 | 12.0413 | 22.5237 | 29.5166   | 77.905  |
| 2.369         | 12.52 | 7000 | 3.4890          | 31.2516 | 11.3416 | 21.5711 | 28.5465   | 85.605  |
| 2.3446        | 13.42 | 7500 | 3.4949          | 32.1277 | 11.6876 | 22.0244 | 29.2239   | 83.895  |
| 2.3295        | 14.31 | 8000 | 3.4976          | 31.8729 | 11.629  | 21.9629 | 28.9948   | 84.47   |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.0.dev20230220+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
{}
transformers pytorch mbart text2text-generation autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us 

grizzled-interest-2023-03-29
============================


This model is a fine-tuned version of facebook/mbart-large-cc25 on the mtc/newsum2021 dataset.
It achieves the following results on the test set:


* Loss: 3.5178
* Rouge1: 31.4512
* Rouge2: 11.0965
* Rougel: 21.5021
* Rougelsum: 28.634
* Gen Len: 75.755


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-05
* train\_batch\_size: 2
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 8000
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.1


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0.dev20230220+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 2
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 8000
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0.dev20230220+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 2
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 8000
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0.dev20230220+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 2
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: polynomial
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 8000
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.1### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0.dev20230220+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2