ArabicNewSplits7_B_usingALLEssays_FineTuningAraBERT_run2_AugV5_k1_task1_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: 1.5413
• Qwk: 0.3692
• Mse: 1.5413
• Rmse: 1.2415

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.6667	2	6.8044	0.0308	6.8044	2.6085
No log	1.3333	4	4.5217	0.0690	4.5217	2.1264
No log	2.0	6	3.5573	0.0208	3.5573	1.8861
No log	2.6667	8	2.9714	0.1290	2.9714	1.7238
No log	3.3333	10	2.1139	0.0480	2.1139	1.4539
No log	4.0	12	1.6465	0.2222	1.6465	1.2831
No log	4.6667	14	1.5619	0.2545	1.5619	1.2497
No log	5.3333	16	1.5593	0.3036	1.5593	1.2487
No log	6.0	18	1.6967	0.2946	1.6967	1.3026
No log	6.6667	20	1.7692	0.3088	1.7692	1.3301
No log	7.3333	22	1.5189	0.3793	1.5189	1.2324
No log	8.0	24	1.5067	0.2727	1.5067	1.2275
No log	8.6667	26	1.4816	0.2727	1.4816	1.2172
No log	9.3333	28	1.4768	0.3478	1.4768	1.2152
No log	10.0	30	1.6865	0.3556	1.6865	1.2986
No log	10.6667	32	1.7541	0.2774	1.7541	1.3244
No log	11.3333	34	1.5862	0.3906	1.5862	1.2594
No log	12.0	36	1.4758	0.3932	1.4758	1.2148
No log	12.6667	38	1.4511	0.3333	1.4511	1.2046
No log	13.3333	40	1.4501	0.3540	1.4501	1.2042
No log	14.0	42	1.4379	0.3540	1.4379	1.1991
No log	14.6667	44	1.4436	0.4132	1.4436	1.2015
No log	15.3333	46	1.5147	0.4031	1.5147	1.2307
No log	16.0	48	1.3497	0.4333	1.3497	1.1618
No log	16.6667	50	1.2875	0.3826	1.2875	1.1347
No log	17.3333	52	1.2797	0.4407	1.2797	1.1312
No log	18.0	54	1.2983	0.4320	1.2983	1.1394
No log	18.6667	56	1.9118	0.2628	1.9118	1.3827
No log	19.3333	58	2.0877	0.2446	2.0877	1.4449
No log	20.0	60	1.7373	0.3134	1.7373	1.3181
No log	20.6667	62	1.6054	0.3359	1.6054	1.2671
No log	21.3333	64	1.3947	0.4355	1.3947	1.1810
No log	22.0	66	1.3700	0.4390	1.3700	1.1705
No log	22.6667	68	1.6099	0.3359	1.6099	1.2688
No log	23.3333	70	1.9907	0.2090	1.9907	1.4109
No log	24.0	72	1.7926	0.3134	1.7926	1.3389
No log	24.6667	74	1.3751	0.4882	1.3751	1.1727
No log	25.3333	76	1.2065	0.4538	1.2065	1.0984
No log	26.0	78	1.2112	0.4959	1.2112	1.1005
No log	26.6667	80	1.4162	0.4154	1.4162	1.1901
No log	27.3333	82	1.7146	0.3134	1.7146	1.3094
No log	28.0	84	1.6997	0.3134	1.6997	1.3037
No log	28.6667	86	1.4579	0.4688	1.4579	1.2075
No log	29.3333	88	1.3185	0.4677	1.3185	1.1482
No log	30.0	90	1.3637	0.4677	1.3637	1.1678
No log	30.6667	92	1.5068	0.4882	1.5068	1.2275
No log	31.3333	94	1.7880	0.3030	1.7880	1.3372
No log	32.0	96	1.8048	0.3030	1.8048	1.3434
No log	32.6667	98	1.6583	0.3511	1.6583	1.2878
No log	33.3333	100	1.6070	0.3538	1.6070	1.2677
No log	34.0	102	1.5419	0.4252	1.5419	1.2417
No log	34.6667	104	1.6052	0.3538	1.6052	1.2669
No log	35.3333	106	1.6799	0.3511	1.6799	1.2961
No log	36.0	108	1.8604	0.2941	1.8604	1.3640
No log	36.6667	110	1.9515	0.2609	1.9515	1.3970
No log	37.3333	112	1.7449	0.3134	1.7449	1.3210
No log	38.0	114	1.4711	0.4219	1.4711	1.2129
No log	38.6667	116	1.3607	0.4516	1.3607	1.1665
No log	39.3333	118	1.2944	0.4677	1.2944	1.1377
No log	40.0	120	1.3580	0.4516	1.3580	1.1653
No log	40.6667	122	1.5139	0.3538	1.5138	1.2304
No log	41.3333	124	1.5385	0.3538	1.5385	1.2403
No log	42.0	126	1.5725	0.3333	1.5725	1.2540
No log	42.6667	128	1.5189	0.3538	1.5189	1.2324
No log	43.3333	130	1.5333	0.3333	1.5333	1.2383
No log	44.0	132	1.5712	0.3333	1.5712	1.2535
No log	44.6667	134	1.5484	0.3333	1.5484	1.2444
No log	45.3333	136	1.5097	0.4	1.5097	1.2287
No log	46.0	138	1.4721	0.4409	1.4721	1.2133
No log	46.6667	140	1.5021	0.4219	1.5021	1.2256
No log	47.3333	142	1.5517	0.4031	1.5517	1.2457
No log	48.0	144	1.5098	0.4409	1.5098	1.2287
No log	48.6667	146	1.4954	0.4409	1.4954	1.2229
No log	49.3333	148	1.4558	0.4444	1.4558	1.2066
No log	50.0	150	1.4918	0.4409	1.4918	1.2214
No log	50.6667	152	1.5462	0.4031	1.5462	1.2435
No log	51.3333	154	1.6084	0.3664	1.6084	1.2682
No log	52.0	156	1.7196	0.3308	1.7196	1.3113
No log	52.6667	158	1.7832	0.3134	1.7832	1.3354
No log	53.3333	160	1.6784	0.3308	1.6784	1.2955
No log	54.0	162	1.5110	0.4219	1.5110	1.2292
No log	54.6667	164	1.4306	0.4516	1.4306	1.1961
No log	55.3333	166	1.4501	0.4286	1.4501	1.2042
No log	56.0	168	1.5362	0.4219	1.5362	1.2395
No log	56.6667	170	1.7092	0.3308	1.7092	1.3073
No log	57.3333	172	1.7808	0.3134	1.7808	1.3345
No log	58.0	174	1.7406	0.3308	1.7406	1.3193
No log	58.6667	176	1.5834	0.3538	1.5834	1.2583
No log	59.3333	178	1.4712	0.4480	1.4712	1.2129
No log	60.0	180	1.4765	0.4480	1.4765	1.2151
No log	60.6667	182	1.5256	0.4409	1.5256	1.2352
No log	61.3333	184	1.5528	0.4409	1.5528	1.2461
No log	62.0	186	1.6353	0.3511	1.6353	1.2788
No log	62.6667	188	1.6728	0.3308	1.6728	1.2934
No log	63.3333	190	1.6290	0.3511	1.6290	1.2763
No log	64.0	192	1.5647	0.3876	1.5647	1.2509
No log	64.6667	194	1.5215	0.4409	1.5215	1.2335
No log	65.3333	196	1.5091	0.4409	1.5091	1.2284
No log	66.0	198	1.5103	0.4409	1.5103	1.2289
No log	66.6667	200	1.5688	0.3876	1.5688	1.2525
No log	67.3333	202	1.6414	0.3485	1.6414	1.2812
No log	68.0	204	1.6851	0.3308	1.6851	1.2981
No log	68.6667	206	1.6995	0.3308	1.6995	1.3036
No log	69.3333	208	1.8091	0.2963	1.8091	1.3450
No log	70.0	210	1.8417	0.2963	1.8417	1.3571
No log	70.6667	212	1.8807	0.2963	1.8807	1.3714
No log	71.3333	214	1.8149	0.2963	1.8149	1.3472
No log	72.0	216	1.7117	0.3308	1.7117	1.3083
No log	72.6667	218	1.6261	0.3308	1.6261	1.2752
No log	73.3333	220	1.6122	0.3636	1.6122	1.2697
No log	74.0	222	1.5870	0.3664	1.5870	1.2598
No log	74.6667	224	1.5415	0.4031	1.5415	1.2416
No log	75.3333	226	1.4789	0.4409	1.4789	1.2161
No log	76.0	228	1.4281	0.4409	1.4281	1.1950
No log	76.6667	230	1.4268	0.4409	1.4268	1.1945
No log	77.3333	232	1.4253	0.4409	1.4253	1.1938
No log	78.0	234	1.4482	0.4409	1.4482	1.2034
No log	78.6667	236	1.4855	0.4219	1.4855	1.2188
No log	79.3333	238	1.5544	0.3333	1.5544	1.2468
No log	80.0	240	1.6063	0.3333	1.6063	1.2674
No log	80.6667	242	1.6422	0.3308	1.6422	1.2815
No log	81.3333	244	1.6364	0.3308	1.6364	1.2792
No log	82.0	246	1.6518	0.3308	1.6518	1.2852
No log	82.6667	248	1.6591	0.3308	1.6591	1.2880
No log	83.3333	250	1.6242	0.3333	1.6242	1.2744
No log	84.0	252	1.5570	0.3359	1.5570	1.2478
No log	84.6667	254	1.5106	0.3692	1.5106	1.2291
No log	85.3333	256	1.4844	0.4219	1.4844	1.2183
No log	86.0	258	1.4611	0.4409	1.4611	1.2088
No log	86.6667	260	1.4518	0.4409	1.4518	1.2049
No log	87.3333	262	1.4631	0.4409	1.4631	1.2096
No log	88.0	264	1.4874	0.4219	1.4874	1.2196
No log	88.6667	266	1.5221	0.3692	1.5221	1.2337
No log	89.3333	268	1.5481	0.3359	1.5481	1.2442
No log	90.0	270	1.5660	0.3359	1.5660	1.2514
No log	90.6667	272	1.5825	0.3359	1.5825	1.2580
No log	91.3333	274	1.5897	0.3359	1.5897	1.2608
No log	92.0	276	1.5819	0.3359	1.5819	1.2577
No log	92.6667	278	1.5815	0.3359	1.5815	1.2576
No log	93.3333	280	1.5851	0.3359	1.5851	1.2590
No log	94.0	282	1.5754	0.3359	1.5754	1.2551
No log	94.6667	284	1.5615	0.3359	1.5615	1.2496
No log	95.3333	286	1.5437	0.4031	1.5437	1.2425
No log	96.0	288	1.5379	0.4031	1.5379	1.2401
No log	96.6667	290	1.5383	0.4031	1.5383	1.2403
No log	97.3333	292	1.5422	0.4031	1.5422	1.2419
No log	98.0	294	1.5447	0.3692	1.5447	1.2429
No log	98.6667	296	1.5455	0.3692	1.5455	1.2432
No log	99.3333	298	1.5431	0.3692	1.5431	1.2422
No log	100.0	300	1.5413	0.3692	1.5413	1.2415

Framework versions

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