ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k7_task5_organization

This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7934
• Qwk: 0.7193
• Mse: 0.7934
• Rmse: 0.8907

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
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.0690	2	2.4237	0.0218	2.4237	1.5568
No log	0.1379	4	1.6356	0.1531	1.6356	1.2789
No log	0.2069	6	1.7021	-0.0198	1.7021	1.3047
No log	0.2759	8	1.9767	-0.0458	1.9767	1.4059
No log	0.3448	10	1.6797	0.0477	1.6797	1.2960
No log	0.4138	12	1.7713	0.1804	1.7713	1.3309
No log	0.4828	14	1.6965	0.2549	1.6965	1.3025
No log	0.5517	16	1.4704	0.1022	1.4704	1.2126
No log	0.6207	18	1.3690	0.2247	1.3690	1.1701
No log	0.6897	20	1.2419	0.2828	1.2419	1.1144
No log	0.7586	22	1.2231	0.3051	1.2231	1.1059
No log	0.8276	24	1.3204	0.3975	1.3204	1.1491
No log	0.8966	26	1.3899	0.4345	1.3899	1.1789
No log	0.9655	28	1.2591	0.4514	1.2591	1.1221
No log	1.0345	30	1.1682	0.4543	1.1682	1.0808
No log	1.1034	32	1.0887	0.3441	1.0887	1.0434
No log	1.1724	34	1.0401	0.3388	1.0401	1.0199
No log	1.2414	36	1.0121	0.4677	1.0121	1.0061
No log	1.3103	38	0.9846	0.5064	0.9846	0.9923
No log	1.3793	40	0.9986	0.5534	0.9986	0.9993
No log	1.4483	42	1.1755	0.5146	1.1755	1.0842
No log	1.5172	44	1.2066	0.5320	1.2066	1.0985
No log	1.5862	46	0.9554	0.5259	0.9554	0.9775
No log	1.6552	48	0.9105	0.5267	0.9105	0.9542
No log	1.7241	50	0.9738	0.5933	0.9738	0.9868
No log	1.7931	52	1.1649	0.5737	1.1649	1.0793
No log	1.8621	54	1.3403	0.5388	1.3403	1.1577
No log	1.9310	56	1.2097	0.5559	1.2097	1.0998
No log	2.0	58	0.8857	0.6040	0.8857	0.9411
No log	2.0690	60	0.8336	0.5618	0.8336	0.9130
No log	2.1379	62	0.9106	0.6245	0.9106	0.9543
No log	2.2069	64	1.0864	0.6001	1.0864	1.0423
No log	2.2759	66	1.2740	0.5290	1.2740	1.1287
No log	2.3448	68	1.3788	0.4896	1.3788	1.1742
No log	2.4138	70	1.2829	0.5593	1.2829	1.1326
No log	2.4828	72	0.9299	0.6442	0.9299	0.9643
No log	2.5517	74	0.7051	0.6158	0.7051	0.8397
No log	2.6207	76	0.6648	0.6418	0.6648	0.8153
No log	2.6897	78	0.6938	0.7149	0.6938	0.8330
No log	2.7586	80	0.9464	0.6906	0.9464	0.9728
No log	2.8276	82	1.0435	0.6688	1.0435	1.0215
No log	2.8966	84	0.7632	0.7162	0.7632	0.8736
No log	2.9655	86	0.6370	0.7423	0.6370	0.7981
No log	3.0345	88	0.6074	0.7486	0.6074	0.7793
No log	3.1034	90	0.6258	0.7240	0.6258	0.7911
No log	3.1724	92	0.8455	0.7142	0.8455	0.9195
No log	3.2414	94	1.0873	0.6864	1.0873	1.0427
No log	3.3103	96	0.9572	0.6982	0.9572	0.9784
No log	3.3793	98	0.6723	0.7369	0.6723	0.8200
No log	3.4483	100	0.6277	0.7322	0.6277	0.7923
No log	3.5172	102	0.6738	0.7250	0.6738	0.8208
No log	3.5862	104	0.9134	0.7065	0.9134	0.9557
No log	3.6552	106	1.1224	0.6354	1.1224	1.0595
No log	3.7241	108	1.0948	0.6600	1.0948	1.0463
No log	3.7931	110	0.9453	0.6768	0.9453	0.9722
No log	3.8621	112	0.8189	0.6982	0.8189	0.9049
No log	3.9310	114	0.7611	0.7156	0.7611	0.8724
No log	4.0	116	0.7537	0.7214	0.7537	0.8681
No log	4.0690	118	0.8941	0.7082	0.8941	0.9456
No log	4.1379	120	1.0579	0.6827	1.0579	1.0285
No log	4.2069	122	0.9839	0.6857	0.9839	0.9919
No log	4.2759	124	0.8662	0.7132	0.8662	0.9307
No log	4.3448	126	0.7625	0.7118	0.7625	0.8732
No log	4.4138	128	0.7985	0.7024	0.7985	0.8936
No log	4.4828	130	0.7693	0.7113	0.7693	0.8771
No log	4.5517	132	0.7209	0.7262	0.7209	0.8491
No log	4.6207	134	0.6816	0.7389	0.6816	0.8256
No log	4.6897	136	0.7249	0.7318	0.7249	0.8514
No log	4.7586	138	0.7139	0.7268	0.7139	0.8449
No log	4.8276	140	0.6701	0.7236	0.6701	0.8186
No log	4.8966	142	0.6117	0.7499	0.6117	0.7821
No log	4.9655	144	0.6388	0.7467	0.6388	0.7993
No log	5.0345	146	0.7752	0.7218	0.7752	0.8804
No log	5.1034	148	0.9248	0.6999	0.9248	0.9617
No log	5.1724	150	0.9205	0.6953	0.9205	0.9594
No log	5.2414	152	0.8355	0.7106	0.8355	0.9140
No log	5.3103	154	0.7413	0.7183	0.7413	0.8610
No log	5.3793	156	0.6418	0.7183	0.6418	0.8011
No log	5.4483	158	0.6179	0.7123	0.6179	0.7861
No log	5.5172	160	0.6459	0.7145	0.6459	0.8037
No log	5.5862	162	0.7252	0.7093	0.7252	0.8516
No log	5.6552	164	0.7932	0.7242	0.7932	0.8906
No log	5.7241	166	0.8840	0.6943	0.8840	0.9402
No log	5.7931	168	0.8298	0.7173	0.8298	0.9109
No log	5.8621	170	0.7177	0.7361	0.7177	0.8471
No log	5.9310	172	0.6996	0.7361	0.6996	0.8364
No log	6.0	174	0.7218	0.7339	0.7218	0.8496
No log	6.0690	176	0.7826	0.7251	0.7826	0.8847
No log	6.1379	178	0.8148	0.7178	0.8148	0.9027
No log	6.2069	180	0.8451	0.7229	0.8451	0.9193
No log	6.2759	182	0.9488	0.6738	0.9488	0.9741
No log	6.3448	184	0.9824	0.6426	0.9824	0.9911
No log	6.4138	186	0.8886	0.7029	0.8886	0.9426
No log	6.4828	188	0.7471	0.7430	0.7471	0.8644
No log	6.5517	190	0.6096	0.7390	0.6096	0.7808
No log	6.6207	192	0.5688	0.7246	0.5688	0.7542
No log	6.6897	194	0.5694	0.7159	0.5694	0.7546
No log	6.7586	196	0.5940	0.7331	0.5940	0.7707
No log	6.8276	198	0.6617	0.7504	0.6617	0.8135
No log	6.8966	200	0.7658	0.7400	0.7658	0.8751
No log	6.9655	202	0.8488	0.7015	0.8488	0.9213
No log	7.0345	204	0.9101	0.6798	0.9101	0.9540
No log	7.1034	206	0.8836	0.6960	0.8836	0.9400
No log	7.1724	208	0.7926	0.7211	0.7926	0.8903
No log	7.2414	210	0.7514	0.7295	0.7514	0.8668
No log	7.3103	212	0.7475	0.7223	0.7475	0.8646
No log	7.3793	214	0.7629	0.7256	0.7629	0.8735
No log	7.4483	216	0.8083	0.7112	0.8083	0.8991
No log	7.5172	218	0.8610	0.7022	0.8610	0.9279
No log	7.5862	220	0.8850	0.6937	0.8850	0.9408
No log	7.6552	222	0.8562	0.7012	0.8562	0.9253
No log	7.7241	224	0.8093	0.7083	0.8093	0.8996
No log	7.7931	226	0.7987	0.7148	0.7987	0.8937
No log	7.8621	228	0.8105	0.7083	0.8105	0.9003
No log	7.9310	230	0.8310	0.7044	0.8310	0.9116
No log	8.0	232	0.8891	0.6777	0.8891	0.9429
No log	8.0690	234	0.9351	0.6642	0.9351	0.9670
No log	8.1379	236	0.9719	0.6720	0.9719	0.9859
No log	8.2069	238	0.9786	0.6720	0.9786	0.9892
No log	8.2759	240	0.9981	0.6720	0.9981	0.9990
No log	8.3448	242	0.9913	0.6720	0.9913	0.9956
No log	8.4138	244	0.9554	0.6720	0.9554	0.9774
No log	8.4828	246	0.9107	0.6867	0.9107	0.9543
No log	8.5517	248	0.8319	0.7044	0.8319	0.9121
No log	8.6207	250	0.7460	0.7249	0.7460	0.8637
No log	8.6897	252	0.6954	0.7267	0.6954	0.8339
No log	8.7586	254	0.6815	0.7267	0.6815	0.8255
No log	8.8276	256	0.6963	0.7267	0.6963	0.8344
No log	8.8966	258	0.7274	0.7262	0.7274	0.8529
No log	8.9655	260	0.7600	0.7218	0.7600	0.8718
No log	9.0345	262	0.7957	0.7083	0.7957	0.8920
No log	9.1034	264	0.8408	0.7083	0.8408	0.9169
No log	9.1724	266	0.8613	0.6994	0.8613	0.9281
No log	9.2414	268	0.8672	0.7030	0.8672	0.9312
No log	9.3103	270	0.8832	0.7033	0.8832	0.9398
No log	9.3793	272	0.8913	0.7033	0.8913	0.9441
No log	9.4483	274	0.8777	0.7033	0.8777	0.9369
No log	9.5172	276	0.8656	0.7071	0.8656	0.9304
No log	9.5862	278	0.8491	0.7156	0.8491	0.9214
No log	9.6552	280	0.8320	0.7083	0.8320	0.9121
No log	9.7241	282	0.8167	0.7083	0.8167	0.9037
No log	9.7931	284	0.8053	0.7083	0.8053	0.8974
No log	9.8621	286	0.7969	0.7083	0.7969	0.8927
No log	9.9310	288	0.7948	0.7083	0.7948	0.8915
No log	10.0	290	0.7934	0.7193	0.7934	0.8907

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu118
• Datasets 2.21.0
• Tokenizers 0.19.1