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# pegasus_tweet_sum

This model is a fine-tuned version of [transformersbook/pegasus-samsum](https://huggingface.co/transformersbook/pegasus-samsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3260
- Rouge1: 27.0091
- Rouge2: 9.1616
- Rougel: 19.9548
- Rougelsum: 23.608
- Gen Len: 50.6667

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 44   | 2.2587          | 26.8137 | 8.4052 | 18.7378 | 22.5031   | 48.25   |
| No log        | 2.0   | 88   | 2.3260          | 27.0091 | 9.1616 | 19.9548 | 23.608    | 50.6667 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3

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

pegasus\_tweet\_sum
===================


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


* Loss: 2.3260
* Rouge1: 27.0091
* Rouge2: 9.1616
* Rougel: 19.9548
* Rougelsum: 23.608
* Gen Len: 50.6667


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: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2### Training results### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Tokenizers 0.13.3