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MayBashendy/Arabic_FineTuningAraBERT_AugV4-trial2_k1_task1_organization_fold1

sourceHugging Faceupdated 2y agoView on Hugging Face

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ArabicFineTuningAraBERTAugV4-trial2k1task1organizationfold1

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: 0.7333
—Qwk: 0.6729
—Mse: 0.7333
—Rmse: 0.8563

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

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.0714	2	3.8146	-0.0294	3.8146	1.9531
No log	0.1429	4	2.2548	0.0	2.2548	1.5016
No log	0.2143	6	1.1272	0.2119	1.1272	1.0617
No log	0.2857	8	0.7637	0.0978	0.7637	0.8739
No log	0.3571	10	0.6722	0.3824	0.6722	0.8199
No log	0.4286	12	0.6702	0.3689	0.6702	0.8187
No log	0.5	14	0.7288	0.4362	0.7288	0.8537
No log	0.5714	16	1.6902	0.1860	1.6902	1.3001
No log	0.6429	18	1.5456	0.1993	1.5456	1.2432
No log	0.7143	20	0.6476	0.3772	0.6476	0.8047
No log	0.7857	22	0.9943	0.4043	0.9943	0.9972
No log	0.8571	24	1.1152	0.2125	1.1152	1.0560
No log	0.9286	26	1.0173	0.3253	1.0173	1.0086
No log	1.0	28	0.9906	0.3253	0.9906	0.9953
No log	1.0714	30	0.7044	0.4	0.7044	0.8393
No log	1.1429	32	0.6103	0.4784	0.6103	0.7812
No log	1.2143	34	0.6634	0.4655	0.6634	0.8145
No log	1.2857	36	0.8933	0.3214	0.8933	0.9451
No log	1.3571	38	0.7858	0.5664	0.7858	0.8864
No log	1.4286	40	0.8052	0.5461	0.8052	0.8973
No log	1.5	42	0.7723	0.5509	0.7723	0.8788
No log	1.5714	44	0.6809	0.5103	0.6809	0.8251
No log	1.6429	46	0.6220	0.5154	0.6220	0.7887
No log	1.7143	48	0.6340	0.5509	0.6340	0.7963
No log	1.7857	50	0.7130	0.6889	0.7130	0.8444
No log	1.8571	52	0.7874	0.7482	0.7874	0.8873
No log	1.9286	54	0.7208	0.7260	0.7208	0.8490
No log	2.0	56	0.5464	0.6291	0.5464	0.7392
No log	2.0714	58	0.5596	0.5333	0.5596	0.7481
No log	2.1429	60	0.5768	0.6316	0.5768	0.7595
No log	2.2143	62	0.7518	0.6762	0.7518	0.8671
No log	2.2857	64	0.9367	0.6316	0.9367	0.9678
No log	2.3571	66	0.9494	0.7063	0.9494	0.9744
No log	2.4286	68	0.7175	0.7260	0.7175	0.8470
No log	2.5	70	0.4936	0.6715	0.4936	0.7026
No log	2.5714	72	0.4833	0.6316	0.4833	0.6952
No log	2.6429	74	0.6173	0.7442	0.6173	0.7857
No log	2.7143	76	0.7408	0.7390	0.7408	0.8607
No log	2.7857	78	0.6953	0.6776	0.6953	0.8338
No log	2.8571	80	0.6526	0.64	0.6526	0.8078
No log	2.9286	82	0.7279	0.5075	0.7279	0.8532
No log	3.0	84	0.7548	0.5075	0.7548	0.8688
No log	3.0714	86	0.7592	0.5764	0.7592	0.8713
No log	3.1429	88	0.9020	0.7423	0.9020	0.9498
No log	3.2143	90	0.9350	0.7423	0.9350	0.9670
No log	3.2857	92	0.8444	0.6189	0.8444	0.9189
No log	3.3571	94	0.7783	0.6089	0.7783	0.8822
No log	3.4286	96	0.7801	0.6089	0.7801	0.8832
No log	3.5	98	0.8015	0.7234	0.8015	0.8953
No log	3.5714	100	0.7534	0.6510	0.7534	0.8680
No log	3.6429	102	0.7209	0.6361	0.7209	0.8491
No log	3.7143	104	0.6355	0.6606	0.6355	0.7972
No log	3.7857	106	0.6019	0.64	0.6019	0.7758
No log	3.8571	108	0.6034	0.6077	0.6034	0.7768
No log	3.9286	110	0.5846	0.7016	0.5846	0.7646
No log	4.0	112	0.5726	0.6755	0.5726	0.7567
No log	4.0714	114	0.6283	0.6645	0.6283	0.7926
No log	4.1429	116	0.6493	0.7036	0.6493	0.8058
No log	4.2143	118	0.6553	0.6645	0.6553	0.8095
No log	4.2857	120	0.6989	0.6645	0.6989	0.8360
No log	4.3571	122	0.7614	0.6645	0.7614	0.8726
No log	4.4286	124	0.7895	0.6645	0.7895	0.8885
No log	4.5	126	0.7961	0.7601	0.7961	0.8922
No log	4.5714	128	0.7882	0.7601	0.7882	0.8878
No log	4.6429	130	0.8092	0.7556	0.8092	0.8996
No log	4.7143	132	0.7436	0.7601	0.7436	0.8623
No log	4.7857	134	0.6792	0.6645	0.6792	0.8242
No log	4.8571	136	0.6995	0.6645	0.6995	0.8364
No log	4.9286	138	0.8225	0.6729	0.8225	0.9069
No log	5.0	140	0.8711	0.7308	0.8711	0.9333
No log	5.0714	142	0.7923	0.6729	0.7923	0.8901
No log	5.1429	144	0.7059	0.6387	0.7059	0.8402
No log	5.2143	146	0.6977	0.6387	0.6977	0.8353
No log	5.2857	148	0.7675	0.6387	0.7675	0.8761
No log	5.3571	150	0.7971	0.6387	0.7971	0.8928
No log	5.4286	152	0.7856	0.6316	0.7856	0.8863
No log	5.5	154	0.7008	0.6387	0.7008	0.8371
No log	5.5714	156	0.6644	0.6522	0.6644	0.8151
No log	5.6429	158	0.6607	0.6077	0.6607	0.8128
No log	5.7143	160	0.6623	0.6270	0.6623	0.8138
No log	5.7857	162	0.6595	0.6182	0.6595	0.8121
No log	5.8571	164	0.6661	0.7601	0.6661	0.8162
No log	5.9286	166	0.6810	0.7799	0.6810	0.8252
No log	6.0	168	0.7289	0.7308	0.7289	0.8537
No log	6.0714	170	0.7186	0.7308	0.7186	0.8477
No log	6.1429	172	0.6959	0.7358	0.6959	0.8342
No log	6.2143	174	0.7044	0.7358	0.7044	0.8393
No log	6.2857	176	0.7061	0.7358	0.7061	0.8403
No log	6.3571	178	0.6975	0.7358	0.6975	0.8352
No log	6.4286	180	0.7205	0.7358	0.7205	0.8488
No log	6.5	182	0.6922	0.7601	0.6922	0.8320
No log	6.5714	184	0.6710	0.7601	0.6710	0.8191
No log	6.6429	186	0.6405	0.6975	0.6405	0.8003
No log	6.7143	188	0.6146	0.6645	0.6146	0.7840
No log	6.7857	190	0.6002	0.6899	0.6002	0.7748
No log	6.8571	192	0.6031	0.6899	0.6031	0.7766
No log	6.9286	194	0.6268	0.6645	0.6268	0.7917
No log	7.0	196	0.6957	0.7358	0.6957	0.8341
No log	7.0714	198	0.7721	0.7556	0.7721	0.8787
No log	7.1429	200	0.8589	0.7390	0.8589	0.9268
No log	7.2143	202	0.8665	0.7390	0.8665	0.9309
No log	7.2857	204	0.7894	0.7556	0.7894	0.8885
No log	7.3571	206	0.7372	0.7358	0.7372	0.8586
No log	7.4286	208	0.7042	0.7358	0.7042	0.8392
No log	7.5	210	0.6753	0.6729	0.6753	0.8218
No log	7.5714	212	0.6791	0.6387	0.6791	0.8241
No log	7.6429	214	0.6934	0.6387	0.6934	0.8327
No log	7.7143	216	0.7315	0.7358	0.7315	0.8553
No log	7.7857	218	0.7816	0.7308	0.7816	0.8841
No log	7.8571	220	0.8662	0.7308	0.8662	0.9307
No log	7.9286	222	0.8856	0.7308	0.8856	0.9411
No log	8.0	224	0.8401	0.7308	0.8401	0.9166
No log	8.0714	226	0.7693	0.7308	0.7693	0.8771
No log	8.1429	228	0.7047	0.7556	0.7047	0.8395
No log	8.2143	230	0.6567	0.6645	0.6567	0.8104
No log	8.2857	232	0.6280	0.6645	0.6280	0.7925
No log	8.3571	234	0.6228	0.6645	0.6228	0.7892
No log	8.4286	236	0.6332	0.6645	0.6332	0.7957
No log	8.5	238	0.6486	0.6645	0.6486	0.8054
No log	8.5714	240	0.6679	0.6645	0.6679	0.8172
No log	8.6429	242	0.7122	0.7308	0.7122	0.8439
No log	8.7143	244	0.7546	0.7308	0.7546	0.8687
No log	8.7857	246	0.7864	0.7308	0.7864	0.8868
No log	8.8571	248	0.8107	0.7308	0.8107	0.9004
No log	8.9286	250	0.8133	0.7308	0.8133	0.9018
No log	9.0	252	0.7984	0.7308	0.7984	0.8935
No log	9.0714	254	0.7749	0.6667	0.7749	0.8803
No log	9.1429	256	0.7676	0.6478	0.7676	0.8761
No log	9.2143	258	0.7525	0.6729	0.7525	0.8675
No log	9.2857	260	0.7398	0.6729	0.7398	0.8601
No log	9.3571	262	0.7280	0.6729	0.7280	0.8532
No log	9.4286	264	0.7182	0.6729	0.7182	0.8475
No log	9.5	266	0.7180	0.6729	0.7180	0.8473
No log	9.5714	268	0.7218	0.6729	0.7218	0.8496
No log	9.6429	270	0.7256	0.6729	0.7256	0.8518
No log	9.7143	272	0.7262	0.6729	0.7262	0.8522
No log	9.7857	274	0.7289	0.6729	0.7289	0.8537
No log	9.8571	276	0.7296	0.6729	0.7296	0.8542
No log	9.9286	278	0.7316	0.6729	0.7316	0.8553
No log	10.0	280	0.7333	0.6729	0.7333	0.8563

Framework versions

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