Version2ASAP_FineTuningBERT_AugV5_k3_task1_organization_k3_k3_fold0

genki10/Version2ASAP_FineTuningBERT_AugV5_k3_task1_organization_k3_k3_fold0

Version2ASAP_FineTuningBERT_AugV5_k3_task1_organization_k3_k3_fold0 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 27, 2025
Updated: Feb 27, 2025
SHA: 345341b3

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Base models

Model card

From the Hugging Face model README

Version2ASAP_FineTuningBERT_AugV5_k3_task1_organization_k3_k3_fold0

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: 0.5652
Qwk: 0.5585
Mse: 0.5652
Rmse: 0.7518

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	8.1821	0.0	8.1821	2.8604
No log	2.0	6	6.8822	0.0	6.8822	2.6234
No log	3.0	9	5.5840	0.0112	5.5840	2.3630
No log	4.0	12	4.3612	0.0039	4.3612	2.0883
No log	5.0	15	3.1755	0.0	3.1755	1.7820
No log	6.0	18	2.2107	0.0678	2.2107	1.4869
No log	7.0	21	1.6129	0.0316	1.6129	1.2700
No log	8.0	24	1.3119	0.0316	1.3119	1.1454
No log	9.0	27	1.5628	0.0812	1.5628	1.2501
No log	10.0	30	1.2276	0.0812	1.2276	1.1080
No log	11.0	33	1.0658	0.0548	1.0658	1.0324
No log	12.0	36	2.2930	0.1763	2.2930	1.5143
No log	13.0	39	1.5152	0.1682	1.5152	1.2309
No log	14.0	42	0.9759	0.1207	0.9759	0.9879
No log	15.0	45	0.8449	0.3054	0.8449	0.9192
No log	16.0	48	0.7200	0.4259	0.7200	0.8485
No log	17.0	51	0.7270	0.4582	0.7270	0.8526
No log	18.0	54	0.6228	0.3932	0.6228	0.7892
No log	19.0	57	0.8325	0.4071	0.8325	0.9124
No log	20.0	60	0.7438	0.4501	0.7438	0.8625
No log	21.0	63	0.5721	0.4118	0.5721	0.7564
No log	22.0	66	0.8180	0.4398	0.8180	0.9044
No log	23.0	69	0.8597	0.4157	0.8597	0.9272
No log	24.0	72	0.5734	0.4368	0.5734	0.7572
No log	25.0	75	0.6508	0.4723	0.6508	0.8067
No log	26.0	78	0.6568	0.4847	0.6568	0.8105
No log	27.0	81	0.5725	0.4911	0.5725	0.7566
No log	28.0	84	0.8372	0.4290	0.8372	0.9150
No log	29.0	87	0.5946	0.5331	0.5946	0.7711
No log	30.0	90	0.6106	0.5058	0.6106	0.7814
No log	31.0	93	0.5913	0.5456	0.5913	0.7690
No log	32.0	96	0.6691	0.5110	0.6691	0.8180
No log	33.0	99	0.6067	0.5572	0.6067	0.7789
No log	34.0	102	0.7989	0.4534	0.7989	0.8938
No log	35.0	105	0.5977	0.5368	0.5977	0.7731
No log	36.0	108	0.6160	0.5443	0.6160	0.7849
No log	37.0	111	0.7900	0.4651	0.7900	0.8888
No log	38.0	114	0.5712	0.5625	0.5712	0.7558
No log	39.0	117	0.6166	0.5132	0.6166	0.7853
No log	40.0	120	0.5965	0.5647	0.5965	0.7723
No log	41.0	123	0.6917	0.5139	0.6917	0.8317
No log	42.0	126	0.5828	0.5655	0.5828	0.7634
No log	43.0	129	0.6540	0.5404	0.6540	0.8087
No log	44.0	132	0.6288	0.5642	0.6288	0.7930
No log	45.0	135	0.6184	0.5402	0.6184	0.7864
No log	46.0	138	0.7188	0.5098	0.7188	0.8478
No log	47.0	141	0.6413	0.5553	0.6413	0.8008
No log	48.0	144	0.6739	0.5472	0.6739	0.8209
No log	49.0	147	0.6764	0.5170	0.6764	0.8224
No log	50.0	150	0.6091	0.5088	0.6091	0.7805
No log	51.0	153	0.6139	0.5300	0.6139	0.7835
No log	52.0	156	0.6032	0.5503	0.6032	0.7767
No log	53.0	159	0.6103	0.5478	0.6103	0.7812
No log	54.0	162	0.5710	0.5540	0.5710	0.7557
No log	55.0	165	0.5945	0.5333	0.5945	0.7710
No log	56.0	168	0.5864	0.5722	0.5864	0.7658
No log	57.0	171	0.5632	0.5564	0.5632	0.7505
No log	58.0	174	0.5527	0.5611	0.5527	0.7435
No log	59.0	177	0.5758	0.5428	0.5758	0.7588
No log	60.0	180	0.5890	0.5645	0.5890	0.7675
No log	61.0	183	0.5908	0.5609	0.5908	0.7687
No log	62.0	186	0.5838	0.5681	0.5838	0.7641
No log	63.0	189	0.5595	0.5581	0.5595	0.7480
No log	64.0	192	0.5608	0.5618	0.5608	0.7489
No log	65.0	195	0.5567	0.5677	0.5567	0.7461
No log	66.0	198	0.5858	0.5681	0.5858	0.7654
No log	67.0	201	0.5630	0.5756	0.5630	0.7503
No log	68.0	204	0.6027	0.5626	0.6027	0.7763
No log	69.0	207	0.5748	0.5561	0.5748	0.7581
No log	70.0	210	0.5874	0.5596	0.5874	0.7664
No log	71.0	213	0.5872	0.5647	0.5872	0.7663
No log	72.0	216	0.5827	0.5540	0.5827	0.7634
No log	73.0	219	0.5760	0.5569	0.5760	0.7590
No log	74.0	222	0.5748	0.5760	0.5748	0.7582
No log	75.0	225	0.5735	0.5592	0.5735	0.7573
No log	76.0	228	0.5582	0.5504	0.5582	0.7471
No log	77.0	231	0.5416	0.5501	0.5416	0.7359
No log	78.0	234	0.5713	0.5520	0.5713	0.7558
No log	79.0	237	0.5637	0.5444	0.5637	0.7508
No log	80.0	240	0.5807	0.5737	0.5807	0.7621
No log	81.0	243	0.5931	0.5660	0.5931	0.7701
No log	82.0	246	0.5764	0.5636	0.5764	0.7592
No log	83.0	249	0.5566	0.5574	0.5566	0.7460
No log	84.0	252	0.5506	0.5573	0.5506	0.7420
No log	85.0	255	0.5816	0.5545	0.5816	0.7626
No log	86.0	258	0.5730	0.5694	0.5730	0.7569
No log	87.0	261	0.5846	0.5675	0.5846	0.7646
No log	88.0	264	0.5828	0.5712	0.5828	0.7634
No log	89.0	267	0.5788	0.5693	0.5788	0.7608
No log	90.0	270	0.5770	0.5676	0.5770	0.7596
No log	91.0	273	0.5775	0.5764	0.5775	0.7599
No log	92.0	276	0.5753	0.5726	0.5753	0.7585
No log	93.0	279	0.5743	0.5632	0.5743	0.7578
No log	94.0	282	0.5713	0.5519	0.5713	0.7558
No log	95.0	285	0.5688	0.5478	0.5688	0.7542
No log	96.0	288	0.5655	0.5552	0.5655	0.7520
No log	97.0	291	0.5652	0.5471	0.5652	0.7518
No log	98.0	294	0.5655	0.5614	0.5655	0.7520
No log	99.0	297	0.5650	0.5585	0.5650	0.7517
No log	100.0	300	0.5652	0.5585	0.5652	0.7518

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Feb 27, 2025
Updated: Feb 27, 2025