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# NERDE-base

This model is a fine-tuned version of [pierreguillou/bert-base-cased-pt-lenerbr](https://huggingface.co/pierreguillou/bert-base-cased-pt-lenerbr) on the nerde dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1246
- Precision: 0.9119
- Recall: 0.9153
- F1: 0.9136
- Accuracy: 0.9842

## 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: 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2466        | 1.0   | 541  | 0.1003          | 0.8515    | 0.8822 | 0.8666 | 0.9782   |
| 0.0608        | 2.0   | 1082 | 0.0855          | 0.8990    | 0.9083 | 0.9036 | 0.9837   |
| 0.0411        | 3.0   | 1623 | 0.1006          | 0.9078    | 0.9103 | 0.9090 | 0.9837   |
| 0.0266        | 4.0   | 2164 | 0.1052          | 0.9023    | 0.9163 | 0.9092 | 0.9828   |
| 0.0191        | 5.0   | 2705 | 0.1060          | 0.9112    | 0.9183 | 0.9147 | 0.9847   |
| 0.0153        | 6.0   | 3246 | 0.1152          | 0.9052    | 0.9098 | 0.9075 | 0.9831   |
| 0.0124        | 7.0   | 3787 | 0.1209          | 0.9029    | 0.9185 | 0.9107 | 0.9835   |
| 0.0083        | 8.0   | 4328 | 0.1176          | 0.9072    | 0.9163 | 0.9117 | 0.9844   |
| 0.0077        | 9.0   | 4869 | 0.1240          | 0.9080    | 0.9201 | 0.9140 | 0.9844   |
| 0.0051        | 10.0  | 5410 | 0.1246          | 0.9119    | 0.9153 | 0.9136 | 0.9842   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["nerde"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Considerando-se os argumentos elencados pela Peticion\u00e1ria, infere-se que a CNH Industrial det\u00e9m leg\u00edtimo interesse pelo caso em ep\u00edgrafe, visto que pode ser afetada pela decis\u00e3o a ser adotada pelo Cade sobre a Opera\u00e7\u00e3o, constata\u00e7\u00e3o que autoriza o enquadramento do pleito nas hip\u00f3teses previstas no artigo 50 da Lei n\u00ba 12.529/2011."}, {"text": "Em an\u00e1lise dos autos verifica-se a exist\u00eancia de documentos contra Aur\u00e9lio de Paula, datados de 04 de mar\u00e7o de 2010, 19 de mar\u00e7o de 2010 e 05 de outubro de 2010; contra Bianchini Ind\u00fastria de Pl\u00e1sticos Ltda., Igon Bernardelli, datados de 19 de mar\u00e7o de 2010; contra a Nasato Ind\u00fastria de Pl\u00e1sticos Eireli e Osmair Nasato, datados de 04 de mar\u00e7o de 2010 e 05 de outubro de 2010; contra TWB Ind\u00fastria e Com\u00e9rcio de Produtos Pl\u00e1sticos Ltda. e Waldir Dezotti, datados de 04 de mar\u00e7o de 2010 e 05 de outubro de 2010, podendo-se concluir que a conduta ocorreu de forma cont\u00ednua na maioria dos casos, pelo menos ao longo do ano de 2010, quest\u00f5es que ser\u00e3o melhor analisadas ap\u00f3s o fim da instru\u00e7\u00e3o processual."}], "inference": {"parameters": {"aggregation_strategy": "max"}}, "model-index": [{"name": "NERDE-base", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "nerde", "type": "nerde", "args": "NERDE"}, "metrics": [{"type": "precision", "value": 0.9118601747815231, "name": "Precision"}, {"type": "recall", "value": 0.9152882205513785, "name": "Recall"}, {"type": "f1", "value": 0.9135709818636648, "name": "F1"}, {"type": "accuracy", "value": 0.9841962132484992, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard safetensors bert token-classification generated_from_trainer dataset:nerde model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-nerde #model-index #autotrain_compatible #endpoints_compatible #region-us 

NERDE-base
==========


This model is a fine-tuned version of pierreguillou/bert-base-cased-pt-lenerbr on the nerde dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1246
* Precision: 0.9119
* Recall: 0.9153
* F1: 0.9136
* Accuracy: 0.9842


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: 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: 10


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### 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: 10 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-nerde #model-index #autotrain_compatible #endpoints_compatible #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: 10 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #dataset-nerde #model-index #autotrain_compatible #endpoints_compatible #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: 10### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1