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# bert-base-portuguese-cased-finetuned-tcu-acordaos

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5765

## 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: 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: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7308        | 1.0   | 1383 | 0.6286          |
| 0.6406        | 2.0   | 2766 | 0.5947          |
| 0.6033        | 3.0   | 4149 | 0.5881          |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.2
- Tokenizers 0.10.3

{"language": ["pt"], "license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "Com efeito, se tal fosse poss\u00edvel, o Poder [MASK] \u2013 que n\u00e3o disp\u00f5e de fun\u00e7\u00e3o legislativa \u2013 passaria a desempenhar atribui\u00e7\u00e3o que lhe \u00e9 institucionalmente estranha (a de legislador positivo), usurpando, desse modo, no contexto de um sistema de poderes essencialmente limitados, compet\u00eancia que n\u00e3o lhe pertence, com evidente transgress\u00e3o ao princ\u00edpio constitucional da separa\u00e7\u00e3o de poderes."}], "model-index": [{"name": "bert-base-portuguese-cased-finetuned-tcu-acordaos", "results": []}]}
transformers pytorch tensorboard safetensors bert fill-mask generated_from_trainer pt license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #pt #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bert-base-portuguese-cased-finetuned-tcu-acordaos
=================================================


This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5765


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: 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: 3.0


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.13.2
* Tokenizers 0.10.3



### 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: 3.0 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.13.2
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #pt #license-mit #autotrain_compatible #endpoints_compatible #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: 3.0 ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.13.2
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #pt #license-mit #autotrain_compatible #endpoints_compatible #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: 3.0### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.13.2
* Tokenizers 0.10.3