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# indonesian-roberta-base-ler

This model is a fine-tuned version of [flax-community/indonesian-roberta-base](https://huggingface.co/flax-community/indonesian-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0291
- Overall Precision: 0.9294
- Overall Recall: 0.9191
- Overall F1: 0.9242
- Overall Accuracy: 0.9968
- Jenis amar F1: 0.9379
- Jenis dakwaan F1: 0.8644
- Jenis perkara F1: 0.9096
- Melanggar uu (dakwaan) F1: 0.8062
- Melanggar uu (pertimbangan hukum) F1: 0.6441
- Melanggar uu (tuntutan) F1: 0.9248
- Nama hakim anggota F1: 0.9640
- Nama hakim ketua F1: 0.9741
- Nama jaksa F1: 0.9614
- Nama panitera F1: 0.9756
- Nama pengacara F1: 0.9000
- Nama pengadilan F1: 0.9982
- Nama saksi F1: 0.9386
- Nama terdakwa F1: 0.9786
- Nomor putusan F1: 0.9963
- Putusan hukuman F1: 0.9433
- Tanggal kejadian F1: 0.3988
- Tanggal putusan F1: 0.9680
- Tingkat kasus F1: 0.9853
- Tuntutan hukuman F1: 0.8867

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------------:|:----------------:|:----------------:|:-------------------------:|:------------------------------------:|:--------------------------:|:---------------------:|:-------------------:|:-------------:|:----------------:|:-----------------:|:------------------:|:-------------:|:----------------:|:----------------:|:------------------:|:-------------------:|:------------------:|:----------------:|:-------------------:|
| 0.0204        | 1.0   | 5641  | 0.0163          | 0.8647            | 0.8564         | 0.8605     | 0.9960           | 0.8723        | 0.5028           | 0.7307           | 0.6945                    | 0.5383                               | 0.8472                     | 0.9192                | 0.9389              | 0.9086        | 0.9449           | 0.7881            | 0.9821             | 0.8989        | 0.9423           | 0.9530           | 0.7655             | 0.3135              | 0.9630             | 0.9575           | 0.7803              |
| 0.0133        | 2.0   | 11282 | 0.0193          | 0.8305            | 0.8274         | 0.8289     | 0.9945           | 0.8316        | 0.6958           | 0.6978           | 0.6186                    | 0.3940                               | 0.8116                     | 0.8620                | 0.8495              | 0.8338        | 0.8849           | 0.5220            | 0.9690             | 0.9036        | 0.9532           | 0.9927           | 0.1196             | 0.3154              | 0.9290             | 0.8864           | 0.6835              |
| 0.0099        | 3.0   | 16923 | 0.0163          | 0.8455            | 0.8801         | 0.8624     | 0.9960           | 0.9           | 0.7671           | 0.7539           | 0.5686                    | 0.4050                               | 0.4949                     | 0.9267                | 0.9168              | 0.9281        | 0.9353           | 0.7831            | 0.9910             | 0.8946        | 0.9722           | 0.9895           | 0.8827             | 0.3423              | 0.9474             | 0.9610           | 0.8459              |
| 0.0079        | 4.0   | 22564 | 0.0164          | 0.8627            | 0.9019         | 0.8819     | 0.9958           | 0.9022        | 0.7602           | 0.7336           | 0.7157                    | 0.5674                               | 0.8599                     | 0.9550                | 0.9515              | 0.9631        | 0.9695           | 0.8184            | 0.9679             | 0.9131        | 0.9780           | 0.9963           | 0.8650             | 0.3234              | 0.9564             | 0.9722           | 0.8262              |
| 0.0059        | 5.0   | 28205 | 0.0179          | 0.9157            | 0.8947         | 0.9050     | 0.9968           | 0.9017        | 0.7932           | 0.8425           | 0.7648                    | 0.5989                               | 0.8992                     | 0.9531                | 0.9373              | 0.9560        | 0.9660           | 0.8232            | 0.9784             | 0.9136        | 0.9642           | 0.9898           | 0.9051             | 0.3933              | 0.9645             | 0.9630           | 0.8470              |
| 0.0052        | 6.0   | 33846 | 0.0183          | 0.8523            | 0.8960         | 0.8736     | 0.9960           | 0.8923        | 0.8015           | 0.8443           | 0.7440                    | 0.5949                               | 0.8528                     | 0.9339                | 0.8898              | 0.9348        | 0.9620           | 0.8814            | 1.0                | 0.9156        | 0.9613           | 0.9936           | 0.8604             | 0.2037              | 0.8600             | 0.9646           | 0.8483              |
| 0.005         | 7.0   | 39487 | 0.0183          | 0.8901            | 0.9073         | 0.8986     | 0.9965           | 0.9150        | 0.7942           | 0.8355           | 0.7872                    | 0.6258                               | 0.8641                     | 0.9514                | 0.9573              | 0.9665        | 0.9676           | 0.8746            | 0.9964             | 0.9223        | 0.9680           | 0.9945           | 0.8970             | 0.3249              | 0.9354             | 0.9759           | 0.8407              |
| 0.0039        | 8.0   | 45128 | 0.0197          | 0.8915            | 0.9016         | 0.8965     | 0.9962           | 0.9125        | 0.7638           | 0.7435           | 0.7406                    | 0.5828                               | 0.8394                     | 0.9562                | 0.9683              | 0.9456        | 0.9702           | 0.7469            | 1.0                | 0.8969        | 0.9595           | 0.9969           | 0.9067             | 0.3916              | 0.9404             | 0.9722           | 0.8621              |
| 0.0031        | 9.0   | 50769 | 0.0225          | 0.8661            | 0.9179         | 0.8913     | 0.9959           | 0.9306        | 0.7714           | 0.7939           | 0.7900                    | 0.6084                               | 0.9049                     | 0.9591                | 0.9643              | 0.9457        | 0.9527           | 0.8127            | 0.9964             | 0.9080        | 0.9716           | 0.9970           | 0.9064             | 0.3388              | 0.8412             | 0.9593           | 0.8727              |
| 0.0022        | 10.0  | 56410 | 0.0232          | 0.9254            | 0.9111         | 0.9182     | 0.9967           | 0.9212        | 0.8411           | 0.9080           | 0.8044                    | 0.6126                               | 0.9243                     | 0.9560                | 0.9741              | 0.9591        | 0.9642           | 0.9102            | 0.9874             | 0.9240        | 0.9734           | 0.9941           | 0.9351             | 0.4186              | 0.9626             | 0.9779           | 0.8687              |
| 0.0023        | 11.0  | 62051 | 0.0209          | 0.9289            | 0.9114         | 0.9201     | 0.9969           | 0.9297        | 0.8423           | 0.8843           | 0.7986                    | 0.6318                               | 0.8808                     | 0.9645                | 0.9624              | 0.9585        | 0.9674           | 0.8963            | 0.9946             | 0.9309        | 0.9752           | 0.9966           | 0.9320             | 0.4092              | 0.9697             | 0.9871           | 0.8790              |
| 0.001         | 12.0  | 67692 | 0.0230          | 0.9279            | 0.9075         | 0.9176     | 0.9968           | 0.9377        | 0.8665           | 0.8771           | 0.7951                    | 0.6213                               | 0.9079                     | 0.9611                | 0.9768              | 0.9576        | 0.9638           | 0.9174            | 0.9964             | 0.9353        | 0.9621           | 0.9967           | 0.9391             | 0.3735              | 0.9665             | 0.9703           | 0.8666              |
| 0.0007        | 13.0  | 73333 | 0.0244          | 0.9095            | 0.9190         | 0.9142     | 0.9965           | 0.9400        | 0.8610           | 0.8974           | 0.8030                    | 0.6337                               | 0.9338                     | 0.9660                | 0.9712              | 0.9565        | 0.9668           | 0.9181            | 0.9964             | 0.9273        | 0.9640           | 0.9961           | 0.9233             | 0.3664              | 0.9697             | 0.9668           | 0.8845              |
| 0.0006        | 14.0  | 78974 | 0.0258          | 0.9213            | 0.9186         | 0.9200     | 0.9967           | 0.9315        | 0.8533           | 0.9119           | 0.7934                    | 0.6453                               | 0.9311                     | 0.9617                | 0.9749              | 0.9614        | 0.9702           | 0.8718            | 0.9910             | 0.9320        | 0.9726           | 0.9966           | 0.9249             | 0.3936              | 0.9680             | 0.9871           | 0.8728              |
| 0.0003        | 15.0  | 84615 | 0.0281          | 0.9260            | 0.9208         | 0.9234     | 0.9969           | 0.9313        | 0.8463           | 0.9150           | 0.7996                    | 0.6601                               | 0.9176                     | 0.9677                | 0.9712              | 0.9599        | 0.9749           | 0.8928            | 0.9946             | 0.9351        | 0.9793           | 0.9963           | 0.9347             | 0.3956              | 0.9680             | 0.9852           | 0.8854              |
| 0.0001        | 16.0  | 90256 | 0.0291          | 0.9294            | 0.9191         | 0.9242     | 0.9968           | 0.9379        | 0.8644           | 0.9096           | 0.8062                    | 0.6441                               | 0.9248                     | 0.9640                | 0.9741              | 0.9614        | 0.9756           | 0.9000            | 0.9982             | 0.9386        | 0.9786           | 0.9963           | 0.9433             | 0.3988              | 0.9680             | 0.9853           | 0.8867              |


### 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": "indonesian-roberta-base-ler", "results": []}]}
transformers pytorch roberta token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

indonesian-roberta-base-ler
===========================


This model is a fine-tuned version of flax-community/indonesian-roberta-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0291
* Overall Precision: 0.9294
* Overall Recall: 0.9191
* Overall F1: 0.9242
* Overall Accuracy: 0.9968
* Jenis amar F1: 0.9379
* Jenis dakwaan F1: 0.8644
* Jenis perkara F1: 0.9096
* Melanggar uu (dakwaan) F1: 0.8062
* Melanggar uu (pertimbangan hukum) F1: 0.6441
* Melanggar uu (tuntutan) F1: 0.9248
* Nama hakim anggota F1: 0.9640
* Nama hakim ketua F1: 0.9741
* Nama jaksa F1: 0.9614
* Nama panitera F1: 0.9756
* Nama pengacara F1: 0.9000
* Nama pengadilan F1: 0.9982
* Nama saksi F1: 0.9386
* Nama terdakwa F1: 0.9786
* Nomor putusan F1: 0.9963
* Putusan hukuman F1: 0.9433
* Tanggal kejadian F1: 0.3988
* Tanggal putusan F1: 0.9680
* Tingkat kasus F1: 0.9853
* Tuntutan hukuman F1: 0.8867


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: 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: 8
* eval\_batch\_size: 8
* 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 #roberta #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: 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