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# IndoBert-large-ler

This model is a fine-tuned version of [indobenchmark/indobert-large-p1](https://huggingface.co/indobenchmark/indobert-large-p1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0280
- Overall Precision: 0.8514
- Overall Recall: 0.8391
- Overall F1: 0.8452
- Overall Accuracy: 0.9965
- Jenis amar F1: 0.9373
- Jenis dakwaan F1: 0.8619
- Jenis perkara F1: 0.8023
- Melanggar uu (dakwaan) F1: 0.6952
- Melanggar uu (pertimbangan hukum) F1: 0.5805
- Melanggar uu (tuntutan) F1: 0.8052
- Nama hakim anggota F1: 0.9106
- Nama hakim ketua F1: 0.8938
- Nama jaksa F1: 0.9034
- Nama panitera F1: 0.9078
- Nama pengacara F1: 0.7839
- Nama pengadilan F1: 0.9964
- Nama saksi F1: 0.8391
- Nama terdakwa F1: 0.8208
- Nomor putusan F1: 0.9346
- Putusan hukuman F1: 0.7023
- Tanggal kejadian F1: 0.4252
- Tanggal putusan F1: 0.9267
- Tingkat kasus F1: 0.9725
- Tuntutan hukuman F1: 0.8329

## 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
- max_sequence_length: 128
- stride: 0%
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------------:|:----------------:|:----------------:|:-------------------------:|:------------------------------------:|:--------------------------:|:---------------------:|:-------------------:|:-------------:|:----------------:|:-----------------:|:------------------:|:-------------:|:----------------:|:----------------:|:------------------:|:-------------------:|:------------------:|:----------------:|:-------------------:|
| 0.0166        | 1.0   | 2748  | 0.0249          | 0.7758            | 0.7067         | 0.7396     | 0.9936           | 0.8423        | 0.1685           | 0.5932           | 0.5248                    | 0.5367                               | 0.6792                     | 0.8378                | 0.8500              | 0.8651        | 0.8257           | 0.1235            | 0.9474             | 0.7851        | 0.8169           | 0.8446           | 0.5196             | 0.3771              | 0.6203             | 0.8691           | 0.4773              |
| 0.0142        | 2.0   | 5496  | 0.0174          | 0.7433            | 0.7866         | 0.7643     | 0.9953           | 0.8768        | 0.7126           | 0.7329           | 0.6098                    | 0.5700                               | 0.7270                     | 0.6293                | 0.3585              | 0.8772        | 0.8900           | 0.6534            | 0.9964             | 0.8461        | 0.8220           | 0.9333           | 0.5072             | 0.3842              | 0.6283             | 0.9580           | 0.7159              |
| 0.0107        | 3.0   | 8244  | 0.0209          | 0.7960            | 0.8236         | 0.8096     | 0.9948           | 0.8627        | 0.7920           | 0.4613           | 0.6199                    | 0.5762                               | 0.6600                     | 0.8954                | 0.8941              | 0.8941        | 0.8980           | 0.6855            | 0.9856             | 0.8192        | 0.7853           | 0.9395           | 0.6171             | 0.4233              | 0.9384             | 0.9708           | 0.7573              |
| 0.0084        | 4.0   | 10992 | 0.0148          | 0.8054            | 0.8351         | 0.8200     | 0.9962           | 0.8912        | 0.7805           | 0.7989           | 0.6511                    | 0.5696                               | 0.7557                     | 0.88                  | 0.8964              | 0.8900        | 0.8820           | 0.4533            | 0.9821             | 0.8314        | 0.7595           | 0.9331           | 0.5484             | 0.4451              | 0.8849             | 0.9668           | 0.7673              |
| 0.0069        | 5.0   | 13740 | 0.0155          | 0.8370            | 0.8155         | 0.8261     | 0.9963           | 0.9191        | 0.8512           | 0.7623           | 0.6756                    | 0.5732                               | 0.7616                     | 0.8632                | 0.8410              | 0.8791        | 0.8608           | 0.6745            | 0.9910             | 0.8442        | 0.7191           | 0.9303           | 0.6130             | 0.4351              | 0.9225             | 0.9761           | 0.7955              |
| 0.0059        | 6.0   | 16488 | 0.0175          | 0.8275            | 0.8302         | 0.8288     | 0.9960           | 0.9276        | 0.8049           | 0.7938           | 0.6219                    | 0.5088                               | 0.7215                     | 0.8839                | 0.8760              | 0.8831        | 0.9057           | 0.7259            | 0.9910             | 0.8389        | 0.8276           | 0.9410           | 0.5837             | 0.3982              | 0.9328             | 0.9779           | 0.8022              |
| 0.0052        | 7.0   | 19236 | 0.0171          | 0.8260            | 0.8216         | 0.8238     | 0.9963           | 0.9171        | 0.8367           | 0.7810           | 0.6305                    | 0.5604                               | 0.7232                     | 0.8284                | 0.8767              | 0.8149        | 0.8513           | 0.6970            | 0.9964             | 0.8430        | 0.8277           | 0.9390           | 0.5832             | 0.4070              | 0.9403             | 0.9761           | 0.7783              |
| 0.1431        | 8.0   | 21984 | 0.0192          | 0.8253            | 0.8308         | 0.8281     | 0.9961           | 0.8596        | 0.8175           | 0.7848           | 0.6045                    | 0.5592                               | 0.6472                     | 0.8952                | 0.88                | 0.8824        | 0.8912           | 0.7492            | 0.9731             | 0.8562        | 0.8538           | 0.9379           | 0.5667             | 0.3996              | 0.9265             | 0.9761           | 0.7778              |
| 0.0036        | 9.0   | 24732 | 0.0164          | 0.8209            | 0.8462         | 0.8334     | 0.9961           | 0.9193        | 0.8456           | 0.8104           | 0.6787                    | 0.5545                               | 0.7774                     | 0.9022                | 0.8822              | 0.8929        | 0.9006           | 0.7464            | 0.9910             | 0.8549        | 0.8479           | 0.9415           | 0.6494             | 0.3990              | 0.9149             | 0.9798           | 0.6811              |
| 0.0032        | 10.0  | 27480 | 0.0194          | 0.8392            | 0.8437         | 0.8414     | 0.9964           | 0.9257        | 0.8246           | 0.8007           | 0.6742                    | 0.5632                               | 0.7942                     | 0.9032                | 0.8925              | 0.8934        | 0.8966           | 0.7579            | 0.9964             | 0.8432        | 0.8340           | 0.9445           | 0.6418             | 0.4387              | 0.9474             | 0.9761           | 0.8386              |
| 0.0032        | 11.0  | 30228 | 0.0216          | 0.8442            | 0.8332         | 0.8387     | 0.9965           | 0.9040        | 0.7774           | 0.8063           | 0.6756                    | 0.5577                               | 0.7815                     | 0.9117                | 0.8760              | 0.9000        | 0.9019           | 0.7518            | 0.9856             | 0.8491        | 0.8318           | 0.9313           | 0.6316             | 0.4012              | 0.9286             | 0.9725           | 0.8297              |
| 0.0022        | 12.0  | 32976 | 0.0224          | 0.8353            | 0.8356         | 0.8354     | 0.9964           | 0.9298        | 0.8646           | 0.7923           | 0.6704                    | 0.5808                               | 0.7862                     | 0.9123                | 0.8811              | 0.8913        | 0.8894           | 0.7801            | 0.9964             | 0.8345        | 0.7984           | 0.9282           | 0.6599             | 0.4072              | 0.9403             | 0.9688           | 0.8069              |
| 0.0013        | 13.0  | 35724 | 0.0229          | 0.8367            | 0.8435         | 0.8401     | 0.9963           | 0.9308        | 0.8629           | 0.7861           | 0.6681                    | 0.5662                               | 0.8152                     | 0.9166                | 0.8932              | 0.8986        | 0.9019           | 0.7917            | 0.9875             | 0.8378        | 0.7869           | 0.9381           | 0.6543             | 0.4279              | 0.9242             | 0.9744           | 0.8398              |
| 0.0007        | 14.0  | 38472 | 0.0262          | 0.8474            | 0.8372         | 0.8423     | 0.9965           | 0.9373        | 0.8619           | 0.7910           | 0.6689                    | 0.5752                               | 0.7948                     | 0.9111                | 0.8897              | 0.9038        | 0.9103           | 0.7758            | 0.9964             | 0.8438        | 0.8213           | 0.9333           | 0.6619             | 0.4290              | 0.9288             | 0.9670           | 0.8046              |
| 0.0005        | 15.0  | 41220 | 0.0270          | 0.8464            | 0.8400         | 0.8432     | 0.9964           | 0.9357        | 0.8609           | 0.7948           | 0.6794                    | 0.5756                               | 0.7987                     | 0.9067                | 0.8915              | 0.9054        | 0.9076           | 0.7692            | 0.9964             | 0.8400        | 0.8275           | 0.9356           | 0.7157             | 0.4253              | 0.9217             | 0.9744           | 0.8161              |
| 0.0004        | 16.0  | 43968 | 0.0280          | 0.8514            | 0.8391         | 0.8452     | 0.9965           | 0.9373        | 0.8619           | 0.8023           | 0.6952                    | 0.5805                               | 0.8052                     | 0.9106                | 0.8938              | 0.9034        | 0.9078           | 0.7839            | 0.9964             | 0.8391        | 0.8208           | 0.9346           | 0.7023             | 0.4252              | 0.9267             | 0.9725           | 0.8329              |


### 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-large-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-large-ler
==================


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


* Loss: 0.0280
* Overall Precision: 0.8514
* Overall Recall: 0.8391
* Overall F1: 0.8452
* Overall Accuracy: 0.9965
* Jenis amar F1: 0.9373
* Jenis dakwaan F1: 0.8619
* Jenis perkara F1: 0.8023
* Melanggar uu (dakwaan) F1: 0.6952
* Melanggar uu (pertimbangan hukum) F1: 0.5805
* Melanggar uu (tuntutan) F1: 0.8052
* Nama hakim anggota F1: 0.9106
* Nama hakim ketua F1: 0.8938
* Nama jaksa F1: 0.9034
* Nama panitera F1: 0.9078
* Nama pengacara F1: 0.7839
* Nama pengadilan F1: 0.9964
* Nama saksi F1: 0.8391
* Nama terdakwa F1: 0.8208
* Nomor putusan F1: 0.9346
* Putusan hukuman F1: 0.7023
* Tanggal kejadian F1: 0.4252
* Tanggal putusan F1: 0.9267
* Tingkat kasus F1: 0.9725
* Tuntutan hukuman F1: 0.8329


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
* max\_sequence\_length: 128
* stride: 0%
* 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: 16
* eval\_batch\_size: 16
* max\_sequence\_length: 128
* stride: 0%
* 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: 16
* eval\_batch\_size: 16
* max\_sequence\_length: 128
* stride: 0%
* 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