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# t5-large-finetune-keyword-to-text-generation

This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1471
- Rouge1: 2.175
- Rouge2: 0.3661
- Rougel: 1.7927
- Rougelsum: 1.7951
- Gen Len: 15.3252

## Model description

This model is designed to generate text from a single keyword. This project is intended to be used for generating vocabulary questions for ed-tech applications. 

NOTE!: Be sure to use the 'summarize: ' prefix before the word that you would like to un-summarize.

## 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: 8
- eval_batch_size: 8
- 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 | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 3.3083        | 1.0   | 3000  | 3.1706          | 2.1498 | 0.331  | 1.7579 | 1.761     | 16.6826 |
| 3.2121        | 2.0   | 6000  | 3.1403          | 2.1555 | 0.3409 | 1.7659 | 1.769     | 16.208  |
| 3.1286        | 3.0   | 9000  | 3.1300          | 2.1577 | 0.3511 | 1.7703 | 1.7733    | 15.9009 |
| 3.0567        | 4.0   | 12000 | 3.1282          | 2.183  | 0.3584 | 1.7895 | 1.7909    | 15.7135 |
| 2.9953        | 5.0   | 15000 | 3.1293          | 2.1589 | 0.3525 | 1.776  | 1.7781    | 15.678  |
| 2.9483        | 6.0   | 18000 | 3.1308          | 2.1645 | 0.3556 | 1.7824 | 1.784     | 15.425  |
| 2.9009        | 7.0   | 21000 | 3.1358          | 2.1622 | 0.3622 | 1.7848 | 1.7877    | 15.3348 |
| 2.8752        | 8.0   | 24000 | 3.1387          | 2.1716 | 0.36   | 1.7936 | 1.7963    | 15.5296 |
| 2.835         | 9.0   | 27000 | 3.1454          | 2.1806 | 0.3658 | 1.7941 | 1.7966    | 15.4625 |
| 2.8352        | 10.0  | 30000 | 3.1471          | 2.175  | 0.3661 | 1.7927 | 1.7951    | 15.3252 |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-large-finetune-keyword-to-text-generation", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-large-finetune-keyword-to-text-generation
============================================


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


* Loss: 3.1471
* Rouge1: 2.175
* Rouge2: 0.3661
* Rougel: 1.7927
* Rougelsum: 1.7951
* Gen Len: 15.3252


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


This model is designed to generate text from a single keyword. This project is intended to be used for generating vocabulary questions for ed-tech applications.


NOTE!: Be sure to use the 'summarize: ' prefix before the word that you would like to un-summarize.


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: 8
* eval\_batch\_size: 8
* 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 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* 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 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #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: 8
* eval\_batch\_size: 8
* 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 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #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: 8
* eval\_batch\_size: 8
* 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 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1