Modelpublic

jafarabdurrohman/indonesian-roberta-base-ler

sourceHugging Facemitupdated 3y agoView on Hugging Face

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Model Card

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
—trainbatchsize: 8
—evalbatchsize: 8
—maxsequencelength: 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