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# t5-base-samsum

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6172
- Rouge1: 48.9131
- Rouge2: 25.4942
- Rougel: 41.2363
- Rougelsum: 45.3434

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.7606        | 1.0   | 3683  | 0.6254          | 46.9778 | 23.8245 | 39.8294 | 43.4639   |
| 0.6273        | 2.0   | 7366  | 0.6119          | 48.2515 | 24.7534 | 40.4415 | 44.5567   |
| 0.5769        | 3.0   | 11049 | 0.6116          | 48.228  | 24.7865 | 40.7537 | 44.4026   |
| 0.5412        | 4.0   | 14732 | 0.6145          | 48.8563 | 25.356  | 41.1913 | 45.186    |
| 0.5199        | 5.0   | 18415 | 0.6172          | 48.9131 | 25.4942 | 41.2363 | 45.3434   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["samsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-samsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "samsum", "type": "samsum", "config": "samsum", "split": "validation", "args": "samsum"}, "metrics": [{"type": "rouge", "value": 48.9131, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation summarization generated_from_trainer dataset:samsum license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-base-samsum
==============


This model is a fine-tuned version of t5-base on the samsum dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6172
* Rouge1: 48.9131
* Rouge2: 25.4942
* Rougel: 41.2363
* Rougelsum: 45.3434


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: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #summarization #generated_from_trainer #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3