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# punctuation-tedtalk2012-t5-base

This model is a fine-tuned version of [unicamp-dl/ptt5-base-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-base-portuguese-vocab) on the tiagoblima/punctuation-tedtalk2012-t5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0399

## 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: 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: 1.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0348        | 1.0   | 77894 | 0.0399          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["tiagoblima/punctuation-tedtalk2012-t5"], "model-index": [{"name": "punctuation-tedtalk2012-t5-base", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer dataset:tiagoblima/punctuation-tedtalk2012-t5 license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-tiagoblima/punctuation-tedtalk2012-t5 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

punctuation-tedtalk2012-t5-base
===============================


This model is a fine-tuned version of unicamp-dl/ptt5-base-portuguese-vocab on the tiagoblima/punctuation-tedtalk2012-t5 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0399


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: 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: 1.0


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.11.0+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 1.0 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.11.0+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-tiagoblima/punctuation-tedtalk2012-t5 #license-mit #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: 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: 1.0 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.11.0+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-tiagoblima/punctuation-tedtalk2012-t5 #license-mit #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: 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: 1.0### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.11.0+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2