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# ssr-base-finetuned-samsum-en

This model is a fine-tuned version of [microsoft/ssr-base](https://huggingface.co/microsoft/ssr-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6231
- Rouge1: 46.7505
- Rouge2: 22.3968
- Rougel: 37.1784
- Rougelsum: 42.891

## 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: 5.6e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 1.9682        | 1.0   | 300  | 1.6432          | 44.2182 | 20.8486 | 35.0914 | 40.9852   |
| 1.6475        | 2.0   | 600  | 1.5946          | 45.3919 | 21.6955 | 36.2411 | 41.8532   |
| 1.5121        | 3.0   | 900  | 1.5737          | 46.1769 | 22.4178 | 36.9762 | 42.6614   |
| 1.4112        | 4.0   | 1200 | 1.5774          | 46.6047 | 22.8227 | 37.2457 | 43.1935   |
| 1.323         | 5.0   | 1500 | 1.5825          | 46.6162 | 22.485  | 37.2846 | 42.9834   |
| 1.2613        | 6.0   | 1800 | 1.5883          | 46.4253 | 22.1199 | 37.0491 | 42.5189   |
| 1.2077        | 7.0   | 2100 | 1.5965          | 46.485  | 22.3636 | 37.2677 | 42.7499   |
| 1.1697        | 8.0   | 2400 | 1.6174          | 46.8654 | 22.6291 | 37.4201 | 43.0875   |
| 1.1367        | 9.0   | 2700 | 1.6188          | 46.707  | 22.305  | 37.156  | 42.9087   |
| 1.118         | 10.0  | 3000 | 1.6231          | 46.7505 | 22.3968 | 37.1784 | 42.891    |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

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transformers pytorch tensorboard t5 text2text-generation summarization generated_from_trainer dataset:samsum model-index autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-samsum #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

ssr-base-finetuned-samsum-en
============================


This model is a fine-tuned version of microsoft/ssr-base on the samsum dataset.
It achieves the following results on the evaluation set:


* Loss: 1.6231
* Rouge1: 46.7505
* Rouge2: 22.3968
* Rougel: 37.1784
* Rougelsum: 42.891


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: 5.6e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-samsum #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-samsum #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5.6e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1