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# IndoBert-base-ler

This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0348
- Overall Precision: 0.8392
- Overall Recall: 0.8292
- Overall F1: 0.8342
- Overall Accuracy: 0.9961
- Jenis amar F1: 0.9381
- Jenis dakwaan F1: 0.8202
- Jenis perkara F1: 0.7895
- Melanggar uu (dakwaan) F1: 0.6704
- Melanggar uu (pertimbangan hukum) F1: 0.5885
- Melanggar uu (tuntutan) F1: 0.7783
- Nama hakim anggota F1: 0.9045
- Nama hakim ketua F1: 0.8854
- Nama jaksa F1: 0.8905
- Nama panitera F1: 0.9056
- Nama pengacara F1: 0.8288
- Nama pengadilan F1: 0.9964
- Nama saksi F1: 0.8385
- Nama terdakwa F1: 0.8264
- Nomor putusan F1: 0.9359
- Putusan hukuman F1: 0.6659
- Tanggal kejadian F1: 0.3870
- Tanggal putusan F1: 0.9430
- Tingkat kasus F1: 0.9817
- Tuntutan hukuman F1: 0.8348

## 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
- max_sequence_length: 128
- stride: 25% (32)
- decay_rate: 0.01
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Jenis amar F1 | Jenis dakwaan F1 | Jenis perkara F1 | Melanggar uu (dakwaan) F1 | Melanggar uu (pertimbangan hukum) F1 | Melanggar uu (tuntutan) F1 | Nama hakim anggota F1 | Nama hakim ketua F1 | Nama jaksa F1 | Nama panitera F1 | Nama pengacara F1 | Nama pengadilan F1 | Nama saksi F1 | Nama terdakwa F1 | Nomor putusan F1 | Putusan hukuman F1 | Tanggal kejadian F1 | Tanggal putusan F1 | Tingkat kasus F1 | Tuntutan hukuman F1 |
|:-------------:|:-----:|:------:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------------:|:----------------:|:----------------:|:-------------------------:|:------------------------------------:|:--------------------------:|:---------------------:|:-------------------:|:-------------:|:----------------:|:-----------------:|:------------------:|:-------------:|:----------------:|:----------------:|:------------------:|:-------------------:|:------------------:|:----------------:|:-------------------:|
| 0.0214        | 1.0   | 7322   | 0.0174          | 0.7920            | 0.7699         | 0.7808     | 0.9956           | 0.8514        | 0.6352           | 0.6728           | 0.5940                    | 0.5044                               | 0.7329                     | 0.8602                | 0.7357              | 0.8160        | 0.8355           | 0.6585            | 0.9661             | 0.8130        | 0.8053           | 0.9248           | 0.5354             | 0.3278              | 0.9372             | 0.9173           | 0.7353              |
| 0.0127        | 2.0   | 14644  | 0.0162          | 0.7760            | 0.7960         | 0.7859     | 0.9955           | 0.8982        | 0.6809           | 0.7038           | 0.5598                    | 0.4747                               | 0.6963                     | 0.8733                | 0.8681              | 0.8604        | 0.8858           | 0.7114            | 0.9803             | 0.7601        | 0.8242           | 0.9318           | 0.6187             | 0.3976              | 0.9351             | 0.9560           | 0.7015              |
| 0.0117        | 3.0   | 21966  | 0.0172          | 0.7830            | 0.7701         | 0.7765     | 0.9953           | 0.8487        | 0.6657           | 0.7051           | 0.5092                    | 0.5336                               | 0.7518                     | 0.8460                | 0.8093              | 0.7043        | 0.6803           | 0.7242            | 0.9802             | 0.8191        | 0.8039           | 0.9346           | 0.5290             | 0.3797              | 0.9312             | 0.9564           | 0.7534              |
| 0.0088        | 4.0   | 29288  | 0.0175          | 0.8086            | 0.8019         | 0.8052     | 0.9960           | 0.9093        | 0.7876           | 0.7571           | 0.6362                    | 0.5500                               | 0.7384                     | 0.8832                | 0.8440              | 0.7949        | 0.8913           | 0.6986            | 0.9874             | 0.8193        | 0.8378           | 0.9089           | 0.5590             | 0.3968              | 0.9534             | 0.9640           | 0.7724              |
| 0.0092        | 5.0   | 36610  | 0.0171          | 0.8070            | 0.8035         | 0.8053     | 0.9958           | 0.8686        | 0.6188           | 0.7521           | 0.5808                    | 0.5625                               | 0.7645                     | 0.8825                | 0.8168              | 0.8656        | 0.8557           | 0.7155            | 0.9803             | 0.8242        | 0.8132           | 0.9323           | 0.6011             | 0.3756              | 0.9211             | 0.9653           | 0.7570              |
| 0.0057        | 6.0   | 43932  | 0.0184          | 0.8157            | 0.8077         | 0.8117     | 0.9958           | 0.9050        | 0.8299           | 0.7505           | 0.6424                    | 0.4908                               | 0.7571                     | 0.8822                | 0.8740              | 0.8625        | 0.8970           | 0.7475            | 0.9802             | 0.8158        | 0.8327           | 0.9389           | 0.5801             | 0.3892              | 0.944              | 0.9635           | 0.8073              |
| 0.0057        | 7.0   | 51254  | 0.0203          | 0.7988            | 0.8277         | 0.8130     | 0.9959           | 0.9273        | 0.7900           | 0.7673           | 0.5932                    | 0.5577                               | 0.7811                     | 0.8863                | 0.8553              | 0.8743        | 0.8945           | 0.7176            | 0.9681             | 0.8316        | 0.8231           | 0.9374           | 0.5983             | 0.4006              | 0.9110             | 0.9620           | 0.8203              |
| 0.0054        | 8.0   | 58576  | 0.0209          | 0.8263            | 0.8058         | 0.8159     | 0.9959           | 0.8996        | 0.8097           | 0.7661           | 0.6445                    | 0.5613                               | 0.7778                     | 0.9079                | 0.7732              | 0.8783        | 0.8968           | 0.7080            | 0.9910             | 0.8227        | 0.8355           | 0.9401           | 0.5395             | 0.3542              | 0.9279             | 0.9706           | 0.7937              |
| 0.003         | 9.0   | 65898  | 0.0244          | 0.8255            | 0.8096         | 0.8175     | 0.9956           | 0.9277        | 0.7944           | 0.7146           | 0.6556                    | 0.5502                               | 0.7842                     | 0.8564                | 0.8798              | 0.8813        | 0.8955           | 0.7547            | 0.9857             | 0.8221        | 0.8270           | 0.9399           | 0.6681             | 0.3873              | 0.9468             | 0.9654           | 0.7912              |
| 0.0031        | 10.0  | 73220  | 0.0256          | 0.8297            | 0.8206         | 0.8251     | 0.9959           | 0.9103        | 0.8239           | 0.7598           | 0.6639                    | 0.5665                               | 0.7609                     | 0.9008                | 0.8765              | 0.8867        | 0.9002           | 0.7590            | 0.9982             | 0.8359        | 0.8322           | 0.9409           | 0.5965             | 0.3774              | 0.9402             | 0.9635           | 0.8070              |
| 0.0021        | 11.0  | 80542  | 0.0259          | 0.8365            | 0.8238         | 0.8301     | 0.9960           | 0.9191        | 0.8383           | 0.7966           | 0.6644                    | 0.5874                               | 0.7530                     | 0.8944                | 0.8675              | 0.8878        | 0.9041           | 0.7500            | 0.9964             | 0.8319        | 0.8307           | 0.9332           | 0.6536             | 0.3909              | 0.9316             | 0.9670           | 0.8496              |
| 0.0015        | 12.0  | 87864  | 0.0267          | 0.8344            | 0.8204         | 0.8273     | 0.9960           | 0.9270        | 0.8141           | 0.7881           | 0.6816                    | 0.5730                               | 0.7855                     | 0.8964                | 0.8745              | 0.8926        | 0.8913           | 0.7805            | 0.9946             | 0.8291        | 0.8275           | 0.9332           | 0.6376             | 0.3753              | 0.9273             | 0.9761           | 0.8035              |
| 0.001         | 13.0  | 95186  | 0.0297          | 0.8316            | 0.8201         | 0.8258     | 0.9960           | 0.9339        | 0.8373           | 0.7351           | 0.6392                    | 0.5955                               | 0.7816                     | 0.9022                | 0.8763              | 0.8968        | 0.8861           | 0.7826            | 0.9964             | 0.8408        | 0.8296           | 0.9223           | 0.6689             | 0.3906              | 0.9404             | 0.9762           | 0.8070              |
| 0.0007        | 14.0  | 102508 | 0.0317          | 0.8299            | 0.8211         | 0.8254     | 0.9959           | 0.9387        | 0.8462           | 0.7520           | 0.6820                    | 0.5964                               | 0.7791                     | 0.9010                | 0.8770              | 0.8932        | 0.9039           | 0.8142            | 0.9964             | 0.8325        | 0.8262           | 0.9171           | 0.6637             | 0.3807              | 0.9316             | 0.9799           | 0.8450              |
| 0.0003        | 15.0  | 109830 | 0.0334          | 0.8340            | 0.8274         | 0.8307     | 0.9960           | 0.9368        | 0.8222           | 0.7744           | 0.6737                    | 0.5977                               | 0.7877                     | 0.9053                | 0.8817              | 0.8745        | 0.9038           | 0.8083            | 0.9964             | 0.8345        | 0.8335           | 0.9311           | 0.6681             | 0.3793              | 0.9406             | 0.9762           | 0.8436              |
| 0.0001        | 16.0  | 117152 | 0.0348          | 0.8392            | 0.8292         | 0.8342     | 0.9961           | 0.9381        | 0.8202           | 0.7895           | 0.6704                    | 0.5885                               | 0.7783                     | 0.9045                | 0.8854              | 0.8905        | 0.9056           | 0.8288            | 0.9964             | 0.8385        | 0.8264           | 0.9359           | 0.6659             | 0.3870              | 0.9430             | 0.9817           | 0.8348              |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "IndoBert-base-ler", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

IndoBert-base-ler
=================


This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0348
* Overall Precision: 0.8392
* Overall Recall: 0.8292
* Overall F1: 0.8342
* Overall Accuracy: 0.9961
* Jenis amar F1: 0.9381
* Jenis dakwaan F1: 0.8202
* Jenis perkara F1: 0.7895
* Melanggar uu (dakwaan) F1: 0.6704
* Melanggar uu (pertimbangan hukum) F1: 0.5885
* Melanggar uu (tuntutan) F1: 0.7783
* Nama hakim anggota F1: 0.9045
* Nama hakim ketua F1: 0.8854
* Nama jaksa F1: 0.8905
* Nama panitera F1: 0.9056
* Nama pengacara F1: 0.8288
* Nama pengadilan F1: 0.9964
* Nama saksi F1: 0.8385
* Nama terdakwa F1: 0.8264
* Nomor putusan F1: 0.9359
* Putusan hukuman F1: 0.6659
* Tanggal kejadian F1: 0.3870
* Tanggal putusan F1: 0.9430
* Tingkat kasus F1: 0.9817
* Tuntutan hukuman F1: 0.8348


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
* max\_sequence\_length: 128
* stride: 25% (32)
* decay\_rate: 0.01
* num\_epochs: 16


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* max\_sequence\_length: 128
* stride: 25% (32)
* decay\_rate: 0.01
* num\_epochs: 16 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* max\_sequence\_length: 128
* stride: 25% (32)
* decay\_rate: 0.01
* num\_epochs: 16 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3