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# bertimbau-base-finetuned-lener-br

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the [Luciano/lener_br_text_to_lm](https://huggingface.co/datasets/Luciano/lener_br_text_to_lm) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8132

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.3167        | 1.0   | 2079  | 1.1163          |
| 1.1683        | 2.0   | 4158  | 1.0594          |
| 1.0648        | 3.0   | 6237  | 1.0501          |
| 1.0228        | 4.0   | 8316  | 0.9693          |
| 0.9662        | 5.0   | 10395 | 0.9847          |
| 0.9422        | 6.0   | 12474 | 0.9556          |
| 0.8696        | 7.0   | 14553 | 0.8978          |
| 0.7856        | 8.0   | 16632 | nan             |
| 0.7849        | 9.0   | 18711 | 0.9192          |
| 0.7559        | 10.0  | 20790 | 0.8536          |
| 0.7564        | 11.0  | 22869 | 0.9230          |
| 0.7641        | 12.0  | 24948 | 0.8852          |
| 0.7007        | 13.0  | 27027 | 0.8616          |
| 0.7139        | 14.0  | 29106 | 0.8419          |
| 0.6543        | 15.0  | 31185 | 0.8460          |


### Framework versions

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

{"language": ["pt"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["Luciano/lener_br_text_to_lm"], "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."}, {"text": "O autor sustenta que a lei \u00e9 formal e materialmente inconstitucional, em viola\u00e7\u00e3o aos arts. 15, XIV e XV, 19, caput, 53, 71, \u00a71\u00ba, I , e 100, VI e X, da Lei Org\u00e2nica do DF, uma vez que, ( i ) originou-se de iniciativa parlamentar quando necess\u00e1riainiciativa privativa do Chefe do Poder Executivo, suscitando, inclusive, viola\u00e7\u00e3o ao postulado constitucional da `` reserva de administra\u00e7\u00e3o '', a qual impede a inger\u00eancia normativa do Poder [MASK] em mat\u00e9rias de compet\u00eancia executiva ; ( ii ) autoriza a delega\u00e7\u00e3o de poder de pol\u00edcia , atividade t\u00edpica e exclusiva de Estado , na medida em que permite ao Distrito Federal firmar conv\u00eanios com o Conselho Regional de Engenharia e Agronomia do Distrito Federal - CREA-DF e com o Conselho de Arquitetura e Urbanismo do Distrito Federal - CAU-DF para, por meio do seu corpo t\u00e9cnico e credenciados, atuarem na an\u00e1lise de processos de concess\u00e3o de Alvar\u00e1 de Constru\u00e7\u00e3o e de Carta de Habite-se."}], "base_model": "neuralmind/bert-base-portuguese-cased", "model-index": [{"name": "bertimbau-base-finetuned-lener-br", "results": []}]}
transformers pytorch tensorboard safetensors bert fill-mask generated_from_trainer pt dataset:Luciano/lener_br_text_to_lm base_model:neuralmind/bert-base-portuguese-cased license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #pt #dataset-Luciano/lener_br_text_to_lm #base_model-neuralmind/bert-base-portuguese-cased #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bertimbau-base-finetuned-lener-br
=================================


This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the Luciano/lener\_br\_text\_to\_lm dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8132


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: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### 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: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### 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 #safetensors #bert #fill-mask #generated_from_trainer #pt #dataset-Luciano/lener_br_text_to_lm #base_model-neuralmind/bert-base-portuguese-cased #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### 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 #safetensors #bert #fill-mask #generated_from_trainer #pt #dataset-Luciano/lener_br_text_to_lm #base_model-neuralmind/bert-base-portuguese-cased #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15### Training results### Framework versions


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