ArabicNewSplits3_FineTuningAraBERT_run2_AugV5_k3_task1_organization

MayBashendy/ArabicNewSplits3_FineTuningAraBERT_run2_AugV5_k3_task1_organization

ArabicNewSplits3_FineTuningAraBERT_run2_AugV5_k3_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

17% 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 4, 2024
Updated: Dec 4, 2024
SHA: b4b9b76c

Try a prompt

Base models

aubmindlab/bert-base-arabertv02

Model card

From the Hugging Face model README

ArabicNewSplits3_FineTuningAraBERT_run2_AugV5_k3_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: 0.5895
Qwk: 0.7150
Mse: 0.5895
Rmse: 0.7678

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	0.1111	2	4.6159	-0.0151	4.6159	2.1485
No log	0.2222	4	3.1817	0.0823	3.1817	1.7837
No log	0.3333	6	2.0625	0.1585	2.0625	1.4361
No log	0.4444	8	1.5896	0.0970	1.5896	1.2608
No log	0.5556	10	1.4254	-0.0198	1.4254	1.1939
No log	0.6667	12	1.3065	-0.0879	1.3065	1.1430
No log	0.7778	14	1.2325	0.2355	1.2325	1.1102
No log	0.8889	16	1.1667	0.2771	1.1667	1.0801
No log	1.0	18	1.1624	0.1984	1.1624	1.0781
No log	1.1111	20	1.1807	0.1402	1.1807	1.0866
No log	1.2222	22	1.1105	0.2786	1.1105	1.0538
No log	1.3333	24	1.1206	0.3000	1.1206	1.0586
No log	1.4444	26	1.1355	0.3000	1.1355	1.0656
No log	1.5556	28	1.0673	0.3000	1.0673	1.0331
No log	1.6667	30	0.9710	0.3363	0.9710	0.9854
No log	1.7778	32	0.8963	0.3077	0.8963	0.9467
No log	1.8889	34	0.8319	0.5529	0.8319	0.9121
No log	2.0	36	0.8111	0.5997	0.8111	0.9006
No log	2.1111	38	0.7766	0.5755	0.7766	0.8813
No log	2.2222	40	0.7712	0.5374	0.7712	0.8782
No log	2.3333	42	0.9014	0.5218	0.9014	0.9494
No log	2.4444	44	0.8942	0.5753	0.8942	0.9456
No log	2.5556	46	0.7889	0.5831	0.7889	0.8882
No log	2.6667	48	0.7390	0.6510	0.7390	0.8597
No log	2.7778	50	0.7742	0.6616	0.7742	0.8799
No log	2.8889	52	0.7071	0.6558	0.7071	0.8409
No log	3.0	54	0.6903	0.6143	0.6903	0.8309
No log	3.1111	56	0.7637	0.6519	0.7637	0.8739
No log	3.2222	58	0.7214	0.6291	0.7214	0.8493
No log	3.3333	60	0.6335	0.6786	0.6335	0.7959
No log	3.4444	62	0.8637	0.5887	0.8637	0.9294
No log	3.5556	64	0.9222	0.5336	0.9222	0.9603
No log	3.6667	66	0.6861	0.6901	0.6861	0.8283
No log	3.7778	68	0.6346	0.6662	0.6346	0.7966
No log	3.8889	70	0.6625	0.6969	0.6625	0.8140
No log	4.0	72	0.6645	0.7093	0.6645	0.8151
No log	4.1111	74	0.6671	0.6908	0.6671	0.8168
No log	4.2222	76	0.6898	0.6908	0.6898	0.8305
No log	4.3333	78	0.7002	0.6566	0.7002	0.8368
No log	4.4444	80	0.7621	0.6801	0.7621	0.8730
No log	4.5556	82	0.7382	0.6598	0.7382	0.8592
No log	4.6667	84	0.6864	0.7410	0.6864	0.8285
No log	4.7778	86	0.7187	0.6922	0.7187	0.8478
No log	4.8889	88	0.7506	0.6824	0.7506	0.8664
No log	5.0	90	0.7020	0.7292	0.7020	0.8378
No log	5.1111	92	0.6940	0.7665	0.6940	0.8331
No log	5.2222	94	0.7207	0.6822	0.7207	0.8489
No log	5.3333	96	0.6875	0.7665	0.6875	0.8292
No log	5.4444	98	0.6767	0.7547	0.6767	0.8226
No log	5.5556	100	0.6818	0.7547	0.6818	0.8257
No log	5.6667	102	0.7630	0.6092	0.7630	0.8735
No log	5.7778	104	0.7491	0.6180	0.7491	0.8655
No log	5.8889	106	0.6703	0.6976	0.6703	0.8187
No log	6.0	108	0.6312	0.6803	0.6312	0.7945
No log	6.1111	110	0.6457	0.7312	0.6457	0.8036
No log	6.2222	112	0.6333	0.6488	0.6333	0.7958
No log	6.3333	114	0.6384	0.6451	0.6384	0.7990
No log	6.4444	116	0.6531	0.5980	0.6531	0.8081
No log	6.5556	118	0.6065	0.6568	0.6065	0.7788
No log	6.6667	120	0.5956	0.7179	0.5956	0.7718
No log	6.7778	122	0.5867	0.7157	0.5867	0.7659
No log	6.8889	124	0.5791	0.7157	0.5791	0.7610
No log	7.0	126	0.5811	0.7024	0.5811	0.7623
No log	7.1111	128	0.5775	0.7150	0.5775	0.7599
No log	7.2222	130	0.5976	0.7135	0.5976	0.7731
No log	7.3333	132	0.5941	0.7135	0.5941	0.7708
No log	7.4444	134	0.5852	0.7024	0.5852	0.7650
No log	7.5556	136	0.5858	0.7024	0.5858	0.7654
No log	7.6667	138	0.5980	0.7078	0.5980	0.7733
No log	7.7778	140	0.6178	0.7252	0.6178	0.7860
No log	7.8889	142	0.6120	0.7252	0.6120	0.7823
No log	8.0	144	0.6322	0.7130	0.6322	0.7951
No log	8.1111	146	0.6190	0.7252	0.6190	0.7868
No log	8.2222	148	0.6157	0.7252	0.6157	0.7846
No log	8.3333	150	0.5942	0.6969	0.5942	0.7708
No log	8.4444	152	0.5777	0.7276	0.5777	0.7600
No log	8.5556	154	0.5846	0.7353	0.5846	0.7646
No log	8.6667	156	0.6011	0.6585	0.6011	0.7753
No log	8.7778	158	0.6006	0.6616	0.6006	0.7750
No log	8.8889	160	0.5908	0.7172	0.5908	0.7687
No log	9.0	162	0.5879	0.7353	0.5879	0.7667
No log	9.1111	164	0.5872	0.7466	0.5872	0.7663
No log	9.2222	166	0.5884	0.7339	0.5884	0.7671
No log	9.3333	168	0.5897	0.7276	0.5897	0.7679
No log	9.4444	170	0.5909	0.7150	0.5909	0.7687
No log	9.5556	172	0.5907	0.7150	0.5907	0.7686
No log	9.6667	174	0.5911	0.7150	0.5911	0.7688
No log	9.7778	176	0.5902	0.7150	0.5902	0.7683
No log	9.8889	178	0.5893	0.7150	0.5893	0.7677
No log	10.0	180	0.5895	0.7150	0.5895	0.7678

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