ASAP_FineTuningBERT_AugV9_k3_task1_organization_k3_k3_fold3

genki10/ASAP_FineTuningBERT_AugV9_k3_task1_organization_k3_k3_fold3

ASAP_FineTuningBERT_AugV9_k3_task1_organization_k3_k3_fold3 is a text classification model from genki10. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Feb 12, 2025
Updated: Feb 12, 2025
SHA: ca825098

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV9_k3_task1_organization_k3_k3_fold3

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6526
Qwk: 0.5399
Mse: 0.6533
Rmse: 0.8082

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: 64
eval_batch_size: 64
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	3	10.9125	0.0012	10.9102	3.3031
No log	2.0	6	8.6058	0.0	8.6041	2.9333
No log	3.0	9	5.7406	0.0612	5.7393	2.3957
No log	4.0	12	3.6623	0.0038	3.6610	1.9134
No log	5.0	15	2.2202	0.1590	2.2196	1.4898
No log	6.0	18	1.5111	0.0365	1.5104	1.2290
No log	7.0	21	1.1890	0.0365	1.1885	1.0902
No log	8.0	24	1.5342	0.0488	1.5335	1.2383
No log	9.0	27	1.1753	0.0729	1.1751	1.0840
No log	10.0	30	1.2736	0.0799	1.2733	1.1284
No log	11.0	33	0.9346	0.1950	0.9344	0.9667
No log	12.0	36	0.7744	0.3875	0.7743	0.8800
No log	13.0	39	1.0122	0.3254	1.0126	1.0063
No log	14.0	42	1.1182	0.3145	1.1188	1.0577
No log	15.0	45	0.6539	0.4525	0.6543	0.8089
No log	16.0	48	0.8002	0.4189	0.8007	0.8948
No log	17.0	51	0.8120	0.4470	0.8125	0.9014
No log	18.0	54	0.7617	0.4741	0.7623	0.8731
No log	19.0	57	0.9265	0.4305	0.9271	0.9629
No log	20.0	60	1.0025	0.4121	1.0032	1.0016
No log	21.0	63	0.8706	0.4598	0.8713	0.9335
No log	22.0	66	0.8860	0.4557	0.8867	0.9417
No log	23.0	69	0.7413	0.5325	0.7420	0.8614
No log	24.0	72	0.8155	0.4844	0.8162	0.9034
No log	25.0	75	0.7966	0.5230	0.7972	0.8928
No log	26.0	78	0.8874	0.4683	0.8879	0.9423
No log	27.0	81	0.8772	0.4783	0.8778	0.9369
No log	28.0	84	0.6782	0.5462	0.6788	0.8239
No log	29.0	87	0.9808	0.4225	0.9812	0.9906
No log	30.0	90	0.6403	0.5147	0.6409	0.8006
No log	31.0	93	0.8117	0.4690	0.8123	0.9013
No log	32.0	96	0.6619	0.5210	0.6625	0.8140
No log	33.0	99	0.8633	0.4777	0.8639	0.9295
No log	34.0	102	0.6463	0.5426	0.6468	0.8043
No log	35.0	105	0.8670	0.4688	0.8676	0.9315
No log	36.0	108	0.7099	0.5204	0.7106	0.8429
No log	37.0	111	0.7233	0.5218	0.7240	0.8509
No log	38.0	114	0.6789	0.5286	0.6795	0.8243
No log	39.0	117	0.8737	0.4683	0.8744	0.9351
No log	40.0	120	0.7424	0.5408	0.7431	0.8620
No log	41.0	123	0.7518	0.5318	0.7523	0.8674
No log	42.0	126	0.8251	0.5030	0.8257	0.9087
No log	43.0	129	0.6744	0.5482	0.6750	0.8216
No log	44.0	132	0.8108	0.5056	0.8116	0.9009
No log	45.0	135	0.6701	0.5509	0.6707	0.8190
No log	46.0	138	0.7564	0.5195	0.7571	0.8701
No log	47.0	141	0.7165	0.5273	0.7172	0.8468
No log	48.0	144	0.6308	0.5488	0.6314	0.7946
No log	49.0	147	0.8825	0.4811	0.8832	0.9398
No log	50.0	150	0.6551	0.5446	0.6558	0.8098
No log	51.0	153	0.6634	0.5500	0.6641	0.8149
No log	52.0	156	0.8063	0.4992	0.8071	0.8984
No log	53.0	159	0.6247	0.5738	0.6253	0.7908
No log	54.0	162	0.6847	0.5261	0.6854	0.8279
No log	55.0	165	0.6551	0.5355	0.6558	0.8098
No log	56.0	168	0.6708	0.5236	0.6715	0.8194
No log	57.0	171	0.7467	0.4892	0.7475	0.8646
No log	58.0	174	0.6792	0.5245	0.6799	0.8246
No log	59.0	177	0.6677	0.5327	0.6683	0.8175
No log	60.0	180	0.6834	0.5334	0.6840	0.8271
No log	61.0	183	0.6309	0.5636	0.6314	0.7946
No log	62.0	186	0.6725	0.5250	0.6730	0.8204
No log	63.0	189	0.7149	0.5132	0.7155	0.8459
No log	64.0	192	0.6454	0.5419	0.6460	0.8038
No log	65.0	195	0.7719	0.5033	0.7726	0.8790
No log	66.0	198	0.6395	0.5526	0.6402	0.8001
No log	67.0	201	0.6300	0.5619	0.6307	0.7941
No log	68.0	204	0.7837	0.5149	0.7844	0.8857
No log	69.0	207	0.6955	0.5094	0.6962	0.8344
No log	70.0	210	0.6196	0.5684	0.6203	0.7876
No log	71.0	213	0.7057	0.4988	0.7064	0.8405
No log	72.0	216	0.7072	0.4958	0.7079	0.8414
No log	73.0	219	0.6189	0.5743	0.6195	0.7871
No log	74.0	222	0.6390	0.5494	0.6397	0.7998
No log	75.0	225	0.7038	0.5095	0.7045	0.8394
No log	76.0	228	0.6435	0.5382	0.6441	0.8026
No log	77.0	231	0.6436	0.5385	0.6442	0.8026
No log	78.0	234	0.6281	0.5531	0.6287	0.7929
No log	79.0	237	0.6356	0.5460	0.6362	0.7976
No log	80.0	240	0.7129	0.5114	0.7136	0.8448
No log	81.0	243	0.7221	0.5099	0.7227	0.8501
No log	82.0	246	0.6393	0.5631	0.6400	0.8000
No log	83.0	249	0.6309	0.5706	0.6315	0.7947
No log	84.0	252	0.6619	0.5427	0.6625	0.8140
No log	85.0	255	0.6753	0.5355	0.6759	0.8222
No log	86.0	258	0.6267	0.5534	0.6273	0.7920
No log	87.0	261	0.6220	0.5559	0.6226	0.7891
No log	88.0	264	0.6529	0.5403	0.6535	0.8084
No log	89.0	267	0.6454	0.5435	0.6460	0.8037
No log	90.0	270	0.6323	0.5537	0.6329	0.7955
No log	91.0	273	0.6363	0.5491	0.6369	0.7980
No log	92.0	276	0.6440	0.5476	0.6446	0.8029
No log	93.0	279	0.6526	0.5399	0.6533	0.8082

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Feb 12, 2025
Updated: Feb 12, 2025