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# t5-base-finetuned-pubmed

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the pub_med_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6311
- Rouge1: 9.3771
- Rouge2: 3.7042
- Rougel: 8.4912
- Rougelsum: 9.0013
- Gen Len: 19.0

## 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: 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.0957        | 1.0   | 4000  | 1.9006          | 8.6968 | 3.2473 | 7.9565 | 8.3224    | 19.0    |
| 2.0489        | 2.0   | 8000  | 1.8571          | 8.6877 | 3.2461 | 7.9311 | 8.2991    | 19.0    |
| 2.7345        | 3.0   | 12000 | 2.6112          | 9.585  | 3.0129 | 8.4729 | 9.1109    | 19.0    |
| 3.0585        | 4.0   | 16000 | 2.7222          | 9.7011 | 3.3549 | 8.6588 | 9.2646    | 19.0    |
| 2.9437        | 5.0   | 20000 | 2.6311          | 9.3771 | 3.7042 | 8.4912 | 9.0013    | 19.0    |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["pub_med_summarization_dataset"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-finetuned-pubmed", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "pub_med_summarization_dataset", "type": "pub_med_summarization_dataset", "args": "document"}, "metrics": [{"type": "rouge", "value": 9.3771, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:pub_med_summarization_dataset license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-base-finetuned-pubmed
========================


This model is a fine-tuned version of t5-base on the pub\_med\_summarization\_dataset dataset.
It achieves the following results on the evaluation set:


* Loss: 2.6311
* Rouge1: 9.3771
* Rouge2: 3.7042
* Rougel: 8.4912
* Rougelsum: 9.0013
* Gen Len: 19.0


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: 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: 5
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #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: 2e-05
* 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: 5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-pub_med_summarization_dataset #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: 2e-05
* 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: 5
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6