
# flan-t5-base-text_summarization_data_6_epochs

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base).
It achieves the following results on the evaluation set:
- Loss: 1.6783
- Rouge1: 43.5994
- Rouge2: 20.4446
- Rougel: 40.132
- Rougelsum: 40.1692
- Gen Len: 14.5837

## Model description

This is a text summarization model.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Text%20Summarization/Text-Summarized%20Data%20-%20Comparison/Flan-T5%20-%20Text%20Summarization%20-%206%20Epochs.ipynb

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/cuitengfeui/textsummarization-data

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | RougeL  | RougeLsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.0079        | 1.0   | 1174 | 1.7150          | 43.4218 | 19.8984 | 40.0059 | 40.0582   | 14.5011 |
| 1.9122        | 2.0   | 2348 | 1.7020          | 44.0374 | 20.5756 | 40.5915 | 40.5683   | 14.617  |
| 1.8588        | 3.0   | 3522 | 1.6881          | 43.9498 | 20.5633 | 40.4656 | 40.5116   | 14.4528 |
| 1.8243        | 4.0   | 4696 | 1.6812          | 43.6024 | 20.4845 | 40.1784 | 40.2211   | 14.5075 |
| 1.7996        | 5.0   | 5870 | 1.6780          | 43.6652 | 20.553  | 40.2209 | 40.2651   | 14.5236 |
| 1.7876        | 6.0   | 7044 | 1.6783          | 43.5994 | 20.4446 | 40.132  | 40.1692   | 14.5837 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "pipeline_tag": "summarization", "model-index": [{"name": "flan-t5-base-text_summarization_data_6_epochs", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer summarization en license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #summarization #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

flan-t5-base-text\_summarization\_data\_6\_epochs
=================================================


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


* Loss: 1.6783
* Rouge1: 43.5994
* Rouge2: 20.4446
* Rougel: 40.132
* Rougelsum: 40.1692
* Gen Len: 14.5837


Model description
-----------------


This is a text summarization model.


For more information on how it was created, check out the following link: URL


Intended uses & limitations
---------------------------


This model is intended to demonstrate my ability to solve a complex problem using technology.


Training and evaluation data
----------------------------


Dataset Source: URL


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #summarization #en #license-apache-2.0 #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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #summarization #en #license-apache-2.0 #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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2