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# pegasus-multi_news-finetuned-weaksup-1000-pegasus

This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1309
- Rouge1: 23.342
- Rouge2: 8.67
- Rougel: 17.2865
- Rougelsum: 19.8228
- Gen Len: 69.79

## 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: 2e-05
- 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:|
| 2.4526        | 1.0   | 1000 | 2.1309          | 23.342 | 8.67   | 17.2865 | 19.8228   | 69.79   |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-multi_news-finetuned-weaksup-1000-pegasus", "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-multi\_news-finetuned-weaksup-1000-pegasus
==================================================


This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 2.1309
* Rouge1: 23.342
* Rouge2: 8.67
* Rougel: 17.2865
* Rougelsum: 19.8228
* Gen Len: 69.79


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: 2e-05
* 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: 1


### Training results



### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.2
* Datasets 1.18.3
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 1 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.2
* Datasets 1.18.3
* Tokenizers 0.11.0
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: 2e-05
* 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: 1 ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.2
* Datasets 1.18.3
* Tokenizers 0.11.0
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: 2e-05
* 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: 1### Training results### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.2
* Datasets 1.18.3
* Tokenizers 0.11.0