ArabicNewSplits2_FineTuningAraBERT_run1_AugV4_k4_task1_organization

MayBashendy/ArabicNewSplits2_FineTuningAraBERT_run1_AugV4_k4_task1_organization

ArabicNewSplits2_FineTuningAraBERT_run1_AugV4_k4_task1_organization is a text classification model from MayBashendy. Use it when you need a label for a piece of text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
Model type: bert
Access: Public
Created: Dec 4, 2024
Updated: Dec 4, 2024
SHA: 49bd0206

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Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits2_FineTuningAraBERT_run1_AugV4_k4_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: 0.8513
Qwk: 0.6345
Mse: 0.8513
Rmse: 0.9227

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	0.0541	2	4.9072	-0.0514	4.9072	2.2152
No log	0.1081	4	2.8079	0.0441	2.8079	1.6757
No log	0.1622	6	1.5588	0.0669	1.5588	1.2485
No log	0.2162	8	1.2903	0.0284	1.2903	1.1359
No log	0.2703	10	1.2087	0.2560	1.2087	1.0994
No log	0.3243	12	1.4200	0.1877	1.4200	1.1916
No log	0.3784	14	1.3632	0.1573	1.3632	1.1676
No log	0.4324	16	1.4668	-0.0035	1.4668	1.2111
No log	0.4865	18	1.2858	0.3026	1.2858	1.1339
No log	0.5405	20	1.8937	0.2380	1.8937	1.3761
No log	0.5946	22	2.2672	0.1681	2.2672	1.5057
No log	0.6486	24	2.2417	0.1353	2.2417	1.4972
No log	0.7027	26	2.0951	0.1635	2.0951	1.4474
No log	0.7568	28	1.9069	0.2573	1.9069	1.3809
No log	0.8108	30	1.4860	0.2346	1.4860	1.2190
No log	0.8649	32	1.1346	0.3234	1.1346	1.0652
No log	0.9189	34	1.1003	0.3736	1.1003	1.0490
No log	0.9730	36	1.1218	0.3438	1.1218	1.0592
No log	1.0270	38	1.1775	0.3689	1.1775	1.0851
No log	1.0811	40	1.0553	0.4490	1.0553	1.0273
No log	1.1351	42	1.0050	0.4783	1.0050	1.0025
No log	1.1892	44	0.9563	0.55	0.9563	0.9779
No log	1.2432	46	0.9613	0.4697	0.9613	0.9804
No log	1.2973	48	0.8628	0.4505	0.8628	0.9289
No log	1.3514	50	1.0144	0.4792	1.0144	1.0072
No log	1.4054	52	1.6226	0.3830	1.6226	1.2738
No log	1.4595	54	1.8132	0.3392	1.8132	1.3466
No log	1.5135	56	1.4484	0.4251	1.4484	1.2035
No log	1.5676	58	0.9854	0.3512	0.9854	0.9927
No log	1.6216	60	0.8273	0.5583	0.8273	0.9095
No log	1.6757	62	0.8593	0.5757	0.8593	0.9270
No log	1.7297	64	0.9464	0.5972	0.9464	0.9728
No log	1.7838	66	1.1634	0.5233	1.1634	1.0786
No log	1.8378	68	1.0499	0.6082	1.0499	1.0246
No log	1.8919	70	0.7921	0.7022	0.7921	0.8900
No log	1.9459	72	0.6684	0.6913	0.6684	0.8175
No log	2.0	74	0.6532	0.6441	0.6532	0.8082
No log	2.0541	76	0.6807	0.7024	0.6807	0.8251
No log	2.1081	78	0.8256	0.6447	0.8256	0.9086
No log	2.1622	80	0.8863	0.5720	0.8863	0.9414
No log	2.2162	82	0.7524	0.6601	0.7524	0.8674
No log	2.2703	84	0.7082	0.5693	0.7082	0.8416
No log	2.3243	86	0.7631	0.5194	0.7631	0.8735
No log	2.3784	88	0.7661	0.5385	0.7661	0.8753
No log	2.4324	90	0.6968	0.5861	0.6968	0.8348
No log	2.4865	92	0.8035	0.6511	0.8035	0.8964
No log	2.5405	94	0.9418	0.6226	0.9418	0.9705
No log	2.5946	96	1.1314	0.6792	1.1314	1.0637
No log	2.6486	98	1.0476	0.6805	1.0476	1.0235
No log	2.7027	100	0.9412	0.6481	0.9412	0.9702
No log	2.7568	102	0.7937	0.6779	0.7937	0.8909
No log	2.8108	104	0.7427	0.6795	0.7427	0.8618
No log	2.8649	106	0.7332	0.6795	0.7332	0.8563
No log	2.9189	108	0.8393	0.6486	0.8393	0.9161
No log	2.9730	110	1.2809	0.5876	1.2809	1.1318
No log	3.0270	112	1.5748	0.5670	1.5748	1.2549
No log	3.0811	114	1.4808	0.5318	1.4808	1.2169
No log	3.1351	116	1.0983	0.5361	1.0983	1.0480
No log	3.1892	118	0.7504	0.7109	0.7504	0.8663
No log	3.2432	120	0.6786	0.6695	0.6786	0.8238
No log	3.2973	122	0.6770	0.6831	0.6770	0.8228
No log	3.3514	124	0.7048	0.6951	0.7048	0.8395
No log	3.4054	126	0.8334	0.6278	0.8334	0.9129
No log	3.4595	128	0.9750	0.6272	0.9750	0.9874
No log	3.5135	130	0.9468	0.6138	0.9468	0.9730
No log	3.5676	132	0.8057	0.6443	0.8057	0.8976
No log	3.6216	134	0.8015	0.6867	0.8015	0.8953
No log	3.6757	136	0.8635	0.6547	0.8635	0.9292
No log	3.7297	138	0.8144	0.6538	0.8144	0.9024
No log	3.7838	140	0.8067	0.6462	0.8067	0.8982
No log	3.8378	142	1.1704	0.6160	1.1704	1.0818
No log	3.8919	144	1.5277	0.5454	1.5277	1.2360
No log	3.9459	146	1.4901	0.5074	1.4901	1.2207
No log	4.0	148	1.2437	0.5845	1.2437	1.1152
No log	4.0541	150	0.9480	0.6068	0.9480	0.9737
No log	4.1081	152	0.8108	0.6622	0.8108	0.9005
No log	4.1622	154	0.7805	0.6358	0.7805	0.8835
No log	4.2162	156	0.8186	0.6205	0.8186	0.9047
No log	4.2703	158	0.8378	0.6585	0.8378	0.9153
No log	4.3243	160	0.8001	0.6233	0.8001	0.8945
No log	4.3784	162	0.8157	0.6292	0.8157	0.9031
No log	4.4324	164	0.8417	0.6292	0.8417	0.9174
No log	4.4865	166	0.8383	0.6310	0.8383	0.9156
No log	4.5405	168	0.7585	0.6671	0.7585	0.8709
No log	4.5946	170	0.7386	0.6377	0.7386	0.8594
No log	4.6486	172	0.7745	0.6608	0.7745	0.8801
No log	4.7027	174	0.9242	0.6268	0.9242	0.9613
No log	4.7568	176	1.0222	0.6242	1.0222	1.0110
No log	4.8108	178	0.9829	0.6327	0.9829	0.9914
No log	4.8649	180	0.8903	0.6405	0.8903	0.9436
No log	4.9189	182	0.8294	0.6405	0.8294	0.9107
No log	4.9730	184	0.7896	0.6447	0.7896	0.8886
No log	5.0270	186	0.8217	0.6405	0.8217	0.9065
No log	5.0811	188	0.9582	0.5641	0.9582	0.9789
No log	5.1351	190	1.0650	0.5013	1.0650	1.0320
No log	5.1892	192	1.0543	0.4780	1.0543	1.0268
No log	5.2432	194	1.1525	0.5239	1.1525	1.0736
No log	5.2973	196	1.1381	0.5239	1.1381	1.0668
No log	5.3514	198	0.9584	0.5828	0.9584	0.9790
No log	5.4054	200	0.7995	0.6654	0.7995	0.8941
No log	5.4595	202	0.7256	0.6769	0.7256	0.8518
No log	5.5135	204	0.7013	0.6875	0.7013	0.8375
No log	5.5676	206	0.6973	0.6838	0.6973	0.8351
No log	5.6216	208	0.7052	0.6838	0.7052	0.8397
No log	5.6757	210	0.7756	0.6625	0.7756	0.8807
No log	5.7297	212	0.8535	0.6366	0.8535	0.9239
No log	5.7838	214	0.9035	0.625	0.9035	0.9505
No log	5.8378	216	0.9910	0.5349	0.9910	0.9955
No log	5.8919	218	1.0380	0.5109	1.0380	1.0188
No log	5.9459	220	1.0723	0.5145	1.0723	1.0355
No log	6.0	222	1.1212	0.5361	1.1212	1.0589
No log	6.0541	224	1.0156	0.5361	1.0156	1.0078
No log	6.1081	226	0.9072	0.5406	0.9072	0.9525
No log	6.1622	228	0.8043	0.6684	0.8043	0.8968
No log	6.2162	230	0.7326	0.7105	0.7326	0.8559
No log	6.2703	232	0.7089	0.6992	0.7089	0.8420
No log	6.3243	234	0.7342	0.6992	0.7342	0.8569
No log	6.3784	236	0.7668	0.6855	0.7668	0.8757
No log	6.4324	238	0.8392	0.6806	0.8392	0.9161
No log	6.4865	240	0.8643	0.6826	0.8643	0.9297
No log	6.5405	242	0.9213	0.6588	0.9213	0.9598
No log	6.5946	244	0.9132	0.6354	0.9132	0.9556
No log	6.6486	246	0.8956	0.5929	0.8956	0.9464
No log	6.7027	248	0.8602	0.6040	0.8602	0.9275
No log	6.7568	250	0.8734	0.6040	0.8734	0.9346
No log	6.8108	252	0.9407	0.6113	0.9407	0.9699
No log	6.8649	254	0.9737	0.6113	0.9737	0.9868
No log	6.9189	256	0.9366	0.6113	0.9366	0.9678
No log	6.9730	258	0.8414	0.6280	0.8414	0.9173
No log	7.0270	260	0.7334	0.6918	0.7334	0.8564
No log	7.0811	262	0.6911	0.6918	0.6911	0.8313
No log	7.1351	264	0.6796	0.6803	0.6796	0.8244
No log	7.1892	266	0.6798	0.6897	0.6798	0.8245
No log	7.2432	268	0.6684	0.6897	0.6684	0.8175
No log	7.2973	270	0.6576	0.7081	0.6576	0.8109
No log	7.3514	272	0.6899	0.6688	0.6899	0.8306
No log	7.4054	274	0.7571	0.6875	0.7571	0.8701
No log	7.4595	276	0.8063	0.6717	0.8063	0.8979
No log	7.5135	278	0.8148	0.6806	0.8148	0.9027
No log	7.5676	280	0.7799	0.6492	0.7799	0.8831
No log	7.6216	282	0.7408	0.6922	0.7408	0.8607
No log	7.6757	284	0.7422	0.6885	0.7422	0.8615
No log	7.7297	286	0.7741	0.7218	0.7741	0.8798
No log	7.7838	288	0.8437	0.6748	0.8437	0.9185
No log	7.8378	290	0.9028	0.5986	0.9028	0.9502
No log	7.8919	292	1.0123	0.5392	1.0123	1.0061
No log	7.9459	294	1.0648	0.5392	1.0648	1.0319
No log	8.0	296	1.1055	0.5509	1.1055	1.0514
No log	8.0541	298	1.0695	0.5547	1.0695	1.0342
No log	8.1081	300	0.9920	0.5392	0.9920	0.9960
No log	8.1622	302	0.8946	0.6240	0.8946	0.9459
No log	8.2162	304	0.8196	0.6585	0.8196	0.9053
No log	8.2703	306	0.7813	0.6959	0.7813	0.8839
No log	8.3243	308	0.7292	0.7033	0.7292	0.8539
No log	8.3784	310	0.7114	0.6918	0.7114	0.8435
No log	8.4324	312	0.7236	0.6918	0.7236	0.8506
No log	8.4865	314	0.7575	0.7033	0.7575	0.8703
No log	8.5405	316	0.8107	0.6959	0.8107	0.9004
No log	8.5946	318	0.8304	0.6744	0.8304	0.9113
No log	8.6486	320	0.8473	0.6744	0.8473	0.9205
No log	8.7027	322	0.8387	0.6744	0.8387	0.9158
No log	8.7568	324	0.8098	0.6744	0.8098	0.8999
No log	8.8108	326	0.7926	0.6869	0.7926	0.8903
No log	8.8649	328	0.7905	0.6869	0.7905	0.8891
No log	8.9189	330	0.7971	0.6869	0.7971	0.8928
No log	8.9730	332	0.8015	0.6818	0.8015	0.8953
No log	9.0270	334	0.8151	0.6696	0.8151	0.9029
No log	9.0811	336	0.8415	0.6748	0.8415	0.9174
No log	9.1351	338	0.8706	0.6425	0.8706	0.9331
No log	9.1892	340	0.8767	0.6425	0.8767	0.9363
No log	9.2432	342	0.8603	0.6345	0.8603	0.9275
No log	9.2973	344	0.8303	0.6696	0.8303	0.9112
No log	9.3514	346	0.8090	0.6905	0.8090	0.8994
No log	9.4054	348	0.7946	0.7032	0.7946	0.8914
No log	9.4595	350	0.7860	0.7218	0.7860	0.8866
No log	9.5135	352	0.7863	0.7158	0.7863	0.8867
No log	9.5676	354	0.7938	0.7158	0.7938	0.8910
No log	9.6216	356	0.8068	0.6818	0.8068	0.8982
No log	9.6757	358	0.8173	0.6696	0.8173	0.9041
No log	9.7297	360	0.8288	0.6696	0.8288	0.9104
No log	9.7838	362	0.8420	0.6345	0.8420	0.9176
No log	9.8378	364	0.8509	0.6345	0.8509	0.9224
No log	9.8919	366	0.8527	0.6345	0.8527	0.9234
No log	9.9459	368	0.8514	0.6345	0.8514	0.9227
No log	10.0	370	0.8513	0.6345	0.8513	0.9227

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1

Task: Text Classification
Library: transformers
Type: bert
Created: Dec 4, 2024
Updated: Dec 4, 2024