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# bertimbau-large-lener_br

This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1271
- Precision: 0.8965
- Recall: 0.9198
- F1: 0.9080
- Accuracy: 0.9801

## 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0674        | 1.0   | 1957  | 0.1349          | 0.7617    | 0.8710 | 0.8127 | 0.9594   |
| 0.0443        | 2.0   | 3914  | 0.1867          | 0.6862    | 0.9194 | 0.7858 | 0.9575   |
| 0.0283        | 3.0   | 5871  | 0.1185          | 0.8206    | 0.8766 | 0.8477 | 0.9678   |
| 0.0226        | 4.0   | 7828  | 0.1405          | 0.8072    | 0.8978 | 0.8501 | 0.9708   |
| 0.0141        | 5.0   | 9785  | 0.1898          | 0.7224    | 0.9194 | 0.8090 | 0.9629   |
| 0.01          | 6.0   | 11742 | 0.1655          | 0.9062    | 0.8856 | 0.8958 | 0.9741   |
| 0.012         | 7.0   | 13699 | 0.1271          | 0.8965    | 0.9198 | 0.9080 | 0.9801   |
| 0.0091        | 8.0   | 15656 | 0.1919          | 0.8890    | 0.8886 | 0.8888 | 0.9719   |
| 0.0042        | 9.0   | 17613 | 0.1725          | 0.8977    | 0.8985 | 0.8981 | 0.9744   |
| 0.0043        | 10.0  | 19570 | 0.1530          | 0.8878    | 0.9034 | 0.8955 | 0.9761   |
| 0.0042        | 11.0  | 21527 | 0.1635          | 0.8792    | 0.9108 | 0.8947 | 0.9774   |
| 0.0033        | 12.0  | 23484 | 0.2009          | 0.8155    | 0.9138 | 0.8619 | 0.9719   |
| 0.0008        | 13.0  | 25441 | 0.1766          | 0.8737    | 0.9135 | 0.8932 | 0.9755   |
| 0.0005        | 14.0  | 27398 | 0.1868          | 0.8616    | 0.9129 | 0.8865 | 0.9743   |
| 0.0014        | 15.0  | 29355 | 0.1910          | 0.8694    | 0.9101 | 0.8893 | 0.9746   |


### Framework versions

- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.0
- Tokenizers 0.10.3

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transformers pytorch tensorboard safetensors bert token-classification generated_from_trainer pt dataset:lener_br base_model:neuralmind/bert-large-portuguese-cased license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #pt #dataset-lener_br #base_model-neuralmind/bert-large-portuguese-cased #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

bertimbau-large-lener\_br
=========================


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


* Loss: 0.1271
* Precision: 0.8965
* Recall: 0.9198
* F1: 0.9080
* Accuracy: 0.9801


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 4.8.2
* Pytorch 1.9.0+cu102
* Datasets 1.9.0
* Tokenizers 0.10.3



### 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 4.8.2
* Pytorch 1.9.0+cu102
* Datasets 1.9.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #pt #dataset-lener_br #base_model-neuralmind/bert-large-portuguese-cased #license-mit #model-index #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 4.8.2
* Pytorch 1.9.0+cu102
* Datasets 1.9.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #pt #dataset-lener_br #base_model-neuralmind/bert-large-portuguese-cased #license-mit #model-index #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 4.8.2
* Pytorch 1.9.0+cu102
* Datasets 1.9.0
* Tokenizers 0.10.3