ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k7_task1_organization

MayBashendy/ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k7_task1_organization

ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k7_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. --

Downloads · 30 days

57% of all-time downloads

All-time downloads

Public

Parameters

135M

541 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors541 MB · 100%

At a glance

Task: Text Classification
Library: transformers
Model type: bert
Access: Public
Created: Dec 8, 2024
Updated: Dec 8, 2024
SHA: 3c51c2ca

Try a prompt

Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits4_FineTuningAraBERT_run1_AugV5_k7_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: 0.6228
Qwk: 0.7221
Mse: 0.6228
Rmse: 0.7892

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	0.0588	2	5.3010	-0.0348	5.3010	2.3024
No log	0.1176	4	3.0165	0.0416	3.0165	1.7368
No log	0.1765	6	1.8904	0.1436	1.8904	1.3749
No log	0.2353	8	1.2702	0.3072	1.2702	1.1270
No log	0.2941	10	1.1195	0.3804	1.1195	1.0581
No log	0.3529	12	1.0247	0.2967	1.0247	1.0123
No log	0.4118	14	0.9947	0.3007	0.9947	0.9973
No log	0.4706	16	1.0265	0.2494	1.0265	1.0131
No log	0.5294	18	1.2174	0.1921	1.2174	1.1034
No log	0.5882	20	1.6084	-0.0183	1.6084	1.2682
No log	0.6471	22	1.6520	-0.0157	1.6520	1.2853
No log	0.7059	24	1.3652	0.0898	1.3652	1.1684
No log	0.7647	26	1.2343	0.2851	1.2343	1.1110
No log	0.8235	28	1.2108	0.2793	1.2108	1.1003
No log	0.8824	30	1.0964	0.3171	1.0964	1.0471
No log	0.9412	32	0.9310	0.4358	0.9310	0.9649
No log	1.0	34	0.7709	0.4498	0.7709	0.8780
No log	1.0588	36	0.7623	0.5036	0.7623	0.8731
No log	1.1176	38	0.7507	0.5585	0.7507	0.8664
No log	1.1765	40	0.7052	0.5740	0.7052	0.8397
No log	1.2353	42	1.2213	0.4456	1.2213	1.1051
No log	1.2941	44	1.5957	0.3491	1.5957	1.2632
No log	1.3529	46	1.1976	0.4964	1.1976	1.0943
No log	1.4118	48	0.6636	0.6504	0.6636	0.8146
No log	1.4706	50	0.6685	0.6598	0.6685	0.8176
No log	1.5294	52	0.6553	0.6596	0.6553	0.8095
No log	1.5882	54	0.6937	0.6577	0.6937	0.8329
No log	1.6471	56	1.0036	0.5746	1.0036	1.0018
No log	1.7059	58	1.3607	0.4456	1.3607	1.1665
No log	1.7647	60	1.2936	0.4646	1.2936	1.1373
No log	1.8235	62	0.9184	0.5996	0.9184	0.9583
No log	1.8824	64	0.6744	0.6782	0.6744	0.8212
No log	1.9412	66	0.7638	0.5735	0.7638	0.8740
No log	2.0	68	0.9145	0.5343	0.9145	0.9563
No log	2.0588	70	0.8361	0.5795	0.8361	0.9144
No log	2.1176	72	0.7625	0.5598	0.7625	0.8732
No log	2.1765	74	0.7358	0.6315	0.7358	0.8578
No log	2.2353	76	0.8664	0.5965	0.8664	0.9308
No log	2.2941	78	0.8642	0.6064	0.8642	0.9296
No log	2.3529	80	0.7329	0.6199	0.7329	0.8561
No log	2.4118	82	0.6699	0.6115	0.6699	0.8185
No log	2.4706	84	0.7437	0.5619	0.7437	0.8624
No log	2.5294	86	0.8615	0.5685	0.8615	0.9282
No log	2.5882	88	0.8077	0.6468	0.8077	0.8987
No log	2.6471	90	0.6471	0.6962	0.6471	0.8044
No log	2.7059	92	0.5852	0.7142	0.5852	0.7650
No log	2.7647	94	0.6798	0.6841	0.6798	0.8245
No log	2.8235	96	0.6871	0.7025	0.6871	0.8289
No log	2.8824	98	0.6093	0.7258	0.6093	0.7806
No log	2.9412	100	0.6251	0.7368	0.6251	0.7906
No log	3.0	102	0.8050	0.6893	0.8050	0.8972
No log	3.0588	104	0.8484	0.6585	0.8484	0.9211
No log	3.1176	106	0.7450	0.6975	0.7450	0.8632
No log	3.1765	108	0.6236	0.7311	0.6236	0.7897
No log	3.2353	110	0.6072	0.7539	0.6072	0.7792
No log	3.2941	112	0.6168	0.7339	0.6168	0.7854
No log	3.3529	114	0.6533	0.7342	0.6533	0.8083
No log	3.4118	116	0.7218	0.7124	0.7218	0.8496
No log	3.4706	118	0.7430	0.7105	0.7430	0.8620
No log	3.5294	120	0.7044	0.7205	0.7044	0.8393
No log	3.5882	122	0.6873	0.7214	0.6873	0.8290
No log	3.6471	124	0.6835	0.7120	0.6835	0.8268
No log	3.7059	126	0.7138	0.7170	0.7138	0.8448
No log	3.7647	128	0.7473	0.7315	0.7473	0.8645
No log	3.8235	130	0.8464	0.6686	0.8464	0.9200
No log	3.8824	132	0.9504	0.6443	0.9504	0.9749
No log	3.9412	134	0.9552	0.6398	0.9552	0.9774
No log	4.0	136	0.8445	0.6713	0.8445	0.9190
No log	4.0588	138	0.7028	0.7355	0.7028	0.8383
No log	4.1176	140	0.6399	0.7371	0.6399	0.8000
No log	4.1765	142	0.6432	0.7456	0.6432	0.8020
No log	4.2353	144	0.6494	0.7445	0.6494	0.8059
No log	4.2941	146	0.6544	0.7344	0.6544	0.8089
No log	4.3529	148	0.6635	0.7338	0.6635	0.8146
No log	4.4118	150	0.6889	0.7341	0.6889	0.8300
No log	4.4706	152	0.7430	0.7004	0.7430	0.8619
No log	4.5294	154	0.7651	0.6910	0.7651	0.8747
No log	4.5882	156	0.7370	0.6989	0.7370	0.8585
No log	4.6471	158	0.7123	0.7014	0.7123	0.8440
No log	4.7059	160	0.6530	0.7341	0.6530	0.8081
No log	4.7647	162	0.6176	0.7468	0.6176	0.7859
No log	4.8235	164	0.6229	0.7383	0.6229	0.7893
No log	4.8824	166	0.6241	0.7138	0.6241	0.7900
No log	4.9412	168	0.6054	0.7551	0.6054	0.7781
No log	5.0	170	0.6314	0.7581	0.6314	0.7946
No log	5.0588	172	0.6426	0.7287	0.6426	0.8016
No log	5.1176	174	0.6266	0.7410	0.6266	0.7916
No log	5.1765	176	0.5998	0.7407	0.5998	0.7745
No log	5.2353	178	0.5990	0.7233	0.5990	0.7739
No log	5.2941	180	0.6197	0.7231	0.6197	0.7872
No log	5.3529	182	0.6165	0.7356	0.6165	0.7852
No log	5.4118	184	0.5939	0.7381	0.5939	0.7706
No log	5.4706	186	0.5991	0.7190	0.5991	0.7740
No log	5.5294	188	0.6177	0.7013	0.6177	0.7859
No log	5.5882	190	0.6246	0.7054	0.6246	0.7903
No log	5.6471	192	0.6240	0.7234	0.6240	0.7900
No log	5.7059	194	0.6229	0.7356	0.6229	0.7893
No log	5.7647	196	0.6402	0.7149	0.6402	0.8001
No log	5.8235	198	0.7039	0.6899	0.7039	0.8390
No log	5.8824	200	0.7160	0.6899	0.7160	0.8462
No log	5.9412	202	0.7066	0.6899	0.7066	0.8406
No log	6.0	204	0.6627	0.7152	0.6627	0.8141
No log	6.0588	206	0.6727	0.7102	0.6727	0.8202
No log	6.1176	208	0.7221	0.6911	0.7221	0.8498
No log	6.1765	210	0.8192	0.6716	0.8192	0.9051
No log	6.2353	212	0.8646	0.6579	0.8646	0.9298
No log	6.2941	214	0.8250	0.6716	0.8250	0.9083
No log	6.3529	216	0.7261	0.6941	0.7261	0.8521
No log	6.4118	218	0.6585	0.7239	0.6585	0.8115
No log	6.4706	220	0.6420	0.7129	0.6420	0.8013
No log	6.5294	222	0.6013	0.7381	0.6013	0.7755
No log	6.5882	224	0.5831	0.7526	0.5831	0.7636
No log	6.6471	226	0.5846	0.7482	0.5846	0.7646
No log	6.7059	228	0.6036	0.7418	0.6036	0.7769
No log	6.7647	230	0.6700	0.7103	0.6700	0.8185
No log	6.8235	232	0.7132	0.7042	0.7132	0.8445
No log	6.8824	234	0.7092	0.7025	0.7092	0.8422
No log	6.9412	236	0.6582	0.7213	0.6582	0.8113
No log	7.0	238	0.5969	0.7669	0.5969	0.7726
No log	7.0588	240	0.5812	0.7564	0.5812	0.7624
No log	7.1176	242	0.5836	0.7564	0.5836	0.7639
No log	7.1765	244	0.6031	0.7737	0.6031	0.7766
No log	7.2353	246	0.6487	0.7165	0.6487	0.8054
No log	7.2941	248	0.6953	0.7151	0.6953	0.8339
No log	7.3529	250	0.7087	0.7151	0.7087	0.8418
No log	7.4118	252	0.6951	0.7193	0.6951	0.8337
No log	7.4706	254	0.6782	0.7152	0.6782	0.8235
No log	7.5294	256	0.7021	0.7152	0.7021	0.8379
No log	7.5882	258	0.7182	0.7257	0.7182	0.8475
No log	7.6471	260	0.7540	0.7021	0.7540	0.8683
No log	7.7059	262	0.8038	0.7037	0.8038	0.8966
No log	7.7647	264	0.8341	0.6880	0.8341	0.9133
No log	7.8235	266	0.8314	0.6823	0.8314	0.9118
No log	7.8824	268	0.8106	0.6757	0.8106	0.9003
No log	7.9412	270	0.7565	0.6868	0.7565	0.8698
No log	8.0	272	0.6952	0.7090	0.6952	0.8338
No log	8.0588	274	0.6626	0.7254	0.6626	0.8140
No log	8.1176	276	0.6372	0.7260	0.6372	0.7983
No log	8.1765	278	0.6279	0.7200	0.6279	0.7924
No log	8.2353	280	0.6271	0.7260	0.6271	0.7919
No log	8.2941	282	0.6316	0.7260	0.6316	0.7947
No log	8.3529	284	0.6458	0.7260	0.6458	0.8036
No log	8.4118	286	0.6764	0.7212	0.6764	0.8224
No log	8.4706	288	0.6996	0.7265	0.6996	0.8364
No log	8.5294	290	0.7011	0.7223	0.7011	0.8373
No log	8.5882	292	0.7017	0.7223	0.7017	0.8377
No log	8.6471	294	0.6864	0.7265	0.6864	0.8285
No log	8.7059	296	0.6637	0.7233	0.6637	0.8147
No log	8.7647	298	0.6522	0.7275	0.6522	0.8076
No log	8.8235	300	0.6354	0.7275	0.6354	0.7971
No log	8.8824	302	0.6258	0.7281	0.6258	0.7911
No log	8.9412	304	0.6209	0.7281	0.6209	0.7879
No log	9.0	306	0.6202	0.7281	0.6202	0.7876
No log	9.0588	308	0.6113	0.7119	0.6113	0.7818
No log	9.1176	310	0.6062	0.7119	0.6062	0.7786
No log	9.1765	312	0.6053	0.7119	0.6053	0.7780
No log	9.2353	314	0.6089	0.7119	0.6089	0.7803
No log	9.2941	316	0.6134	0.7242	0.6134	0.7832
No log	9.3529	318	0.6181	0.7221	0.6181	0.7862
No log	9.4118	320	0.6232	0.7221	0.6232	0.7894
No log	9.4706	322	0.6239	0.7221	0.6239	0.7899
No log	9.5294	324	0.6237	0.7221	0.6237	0.7897
No log	9.5882	326	0.6244	0.7221	0.6244	0.7902
No log	9.6471	328	0.6231	0.7221	0.6231	0.7894
No log	9.7059	330	0.6231	0.7221	0.6231	0.7894
No log	9.7647	332	0.6231	0.7221	0.6231	0.7894
No log	9.8235	334	0.6228	0.7221	0.6228	0.7892
No log	9.8824	336	0.6222	0.7221	0.6222	0.7888
No log	9.9412	338	0.6225	0.7221	0.6225	0.7890
No log	10.0	340	0.6228	0.7221	0.6228	0.7892

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 8, 2024
Updated: Dec 8, 2024