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# pegasus-newsroom-cnn1_50k

This model is a fine-tuned version of [oMateos2020/pegasus-newsroom-cnn1_50k](https://huggingface.co/oMateos2020/pegasus-newsroom-cnn1_50k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1267
- Rouge1: 38.0081
- Rouge2: 16.5536
- Rougel: 26.4916
- Rougelsum: 35.1349
- Gen Len: 59.4912

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.144         | 0.26  | 100  | 3.0323          | 38.3168 | 16.7528 | 26.2646 | 35.2447   | 66.2372 |
| 3.0556        | 0.51  | 200  | 3.0351          | 38.39   | 16.8027 | 26.3412 | 35.37     | 67.4676 |
| 3.0701        | 0.77  | 300  | 3.0345          | 38.5742 | 16.922  | 26.3568 | 35.51     | 68.662  |
| 3.1679        | 1.03  | 400  | 3.0321          | 38.5319 | 16.8049 | 26.4933 | 35.4775   | 65.976  |
| 3.1041        | 1.28  | 500  | 3.0246          | 38.1381 | 16.63   | 26.2484 | 35.0999   | 64.6896 |
| 3.0352        | 1.54  | 600  | 3.0206          | 38.9063 | 17.0281 | 27.0288 | 35.9175   | 59.0668 |
| 3.0894        | 1.79  | 700  | 3.0251          | 38.4461 | 16.7732 | 26.4394 | 35.4807   | 63.2792 |
| 3.0529        | 2.05  | 800  | 3.0400          | 38.5088 | 16.8921 | 26.5526 | 35.5236   | 64.294  |
| 3.0002        | 2.31  | 900  | 3.0394          | 38.6899 | 16.8703 | 26.6771 | 35.6207   | 62.8004 |
| 3.0167        | 2.56  | 1000 | 3.0394          | 38.3532 | 16.6176 | 26.5433 | 35.3282   | 61.63   |
| 3.0168        | 2.82  | 1100 | 3.0421          | 38.7613 | 17.0107 | 26.8424 | 35.7508   | 62.67   |
| 3.0412        | 3.08  | 1200 | 3.0491          | 38.6132 | 16.8046 | 26.61   | 35.6002   | 61.7924 |
| 3.1273        | 3.33  | 1300 | 3.0823          | 38.5498 | 16.795  | 26.5569 | 35.613    | 60.6872 |
| 3.0634        | 3.59  | 1400 | 3.1010          | 38.0927 | 16.4367 | 26.2315 | 35.1311   | 59.252  |
| 3.097         | 3.84  | 1500 | 3.1147          | 37.7644 | 16.3156 | 26.2674 | 34.8315   | 59.7592 |
| 3.1287        | 4.1   | 1600 | 3.1267          | 38.0081 | 16.5536 | 26.4916 | 35.1349   | 59.4912 |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-newsroom-cnn1_50k", "results": []}]}
transformers pytorch pegasus text2text-generation generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

pegasus-newsroom-cnn1\_50k
==========================


This model is a fine-tuned version of oMateos2020/pegasus-newsroom-cnn1\_50k on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 3.1267
* Rouge1: 38.0081
* Rouge2: 16.5536
* Rougel: 26.4916
* Rougelsum: 35.1349
* Gen Len: 59.4912


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: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.1


### Training results



### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.1 ### Training results ### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
* label\_smoothing\_factor: 0.1### Training results### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1