ASAP_nosemantic_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0

genki10/ASAP_nosemantic_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0

ASAP_nosemantic_FineTuningBERT_AugV12_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: Mar 7, 2025
Updated: Mar 7, 2025
SHA: 04b5f376

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Base models

Model card

From the Hugging Face model README

ASAP_nosemantic_FineTuningBERT_AugV12_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: 0.6573
Qwk: 0.5933
Mse: 0.6573
Rmse: 0.8107

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	2	8.9993	0.0	8.9993	2.9999
No log	2.0	4	7.6554	0.0	7.6554	2.7668
No log	3.0	6	6.8777	0.0	6.8777	2.6225
No log	4.0	8	6.0198	0.0157	6.0198	2.4535
No log	5.0	10	5.1949	0.0115	5.1949	2.2792
No log	6.0	12	4.3862	0.0039	4.3862	2.0943
No log	7.0	14	3.6108	0.0	3.6108	1.9002
No log	8.0	16	2.8578	0.0	2.8578	1.6905
No log	9.0	18	2.2015	0.0751	2.2015	1.4837
No log	10.0	20	1.6827	0.0316	1.6827	1.2972
No log	11.0	22	1.3370	0.0316	1.3370	1.1563
No log	12.0	24	1.0776	0.0316	1.0776	1.0381
No log	13.0	26	0.8693	0.0417	0.8693	0.9324
No log	14.0	28	0.7364	0.4349	0.7364	0.8581
No log	15.0	30	0.8033	0.3109	0.8033	0.8963
No log	16.0	32	0.5935	0.4343	0.5935	0.7704
No log	17.0	34	0.7251	0.4660	0.7251	0.8515
No log	18.0	36	1.0504	0.3443	1.0504	1.0249
No log	19.0	38	0.6529	0.5121	0.6529	0.8080
No log	20.0	40	0.5315	0.4777	0.5315	0.7290
No log	21.0	42	0.5219	0.5331	0.5219	0.7224
No log	22.0	44	0.9826	0.4016	0.9826	0.9913
No log	23.0	46	0.5445	0.5463	0.5445	0.7379
No log	24.0	48	0.5770	0.4668	0.5770	0.7596
No log	25.0	50	0.6184	0.5128	0.6184	0.7864
No log	26.0	52	0.6529	0.5040	0.6529	0.8080
No log	27.0	54	0.5420	0.5068	0.5420	0.7362
No log	28.0	56	0.5613	0.5179	0.5613	0.7492
No log	29.0	58	0.5759	0.5075	0.5759	0.7589
No log	30.0	60	0.5639	0.5239	0.5639	0.7509
No log	31.0	62	0.5537	0.5518	0.5537	0.7441
No log	32.0	64	0.5864	0.5409	0.5864	0.7658
No log	33.0	66	0.5760	0.5715	0.5760	0.7590
No log	34.0	68	0.5947	0.5553	0.5947	0.7712
No log	35.0	70	0.6145	0.5420	0.6145	0.7839
No log	36.0	72	0.7486	0.4816	0.7486	0.8652
No log	37.0	74	0.6839	0.5136	0.6839	0.8270
No log	38.0	76	0.6142	0.5491	0.6142	0.7837
No log	39.0	78	0.7574	0.4914	0.7574	0.8703
No log	40.0	80	0.7605	0.4891	0.7605	0.8721
No log	41.0	82	0.6504	0.5200	0.6504	0.8065
No log	42.0	84	0.6986	0.5308	0.6986	0.8358
No log	43.0	86	0.6283	0.5492	0.6283	0.7926
No log	44.0	88	0.6484	0.5262	0.6484	0.8052
No log	45.0	90	0.6039	0.5884	0.6039	0.7771
No log	46.0	92	0.6444	0.5715	0.6444	0.8028
No log	47.0	94	0.6075	0.5793	0.6075	0.7794
No log	48.0	96	0.6018	0.5769	0.6018	0.7758
No log	49.0	98	0.6219	0.5688	0.6219	0.7886
No log	50.0	100	0.6531	0.5613	0.6531	0.8081
No log	51.0	102	0.6709	0.5771	0.6709	0.8191
No log	52.0	104	0.6842	0.5617	0.6842	0.8272
No log	53.0	106	0.6704	0.5592	0.6704	0.8188
No log	54.0	108	0.6406	0.5893	0.6406	0.8004
No log	55.0	110	0.6502	0.5704	0.6502	0.8063
No log	56.0	112	0.6498	0.5740	0.6498	0.8061
No log	57.0	114	0.6515	0.5742	0.6515	0.8072
No log	58.0	116	0.6811	0.5673	0.6811	0.8253
No log	59.0	118	0.6604	0.5798	0.6604	0.8127
No log	60.0	120	0.6708	0.5249	0.6708	0.8190
No log	61.0	122	0.6594	0.5650	0.6594	0.8120
No log	62.0	124	0.7570	0.5284	0.7570	0.8701
No log	63.0	126	0.6633	0.5663	0.6633	0.8144
No log	64.0	128	0.6555	0.5819	0.6555	0.8096
No log	65.0	130	0.7084	0.5505	0.7084	0.8417
No log	66.0	132	0.8400	0.5060	0.8400	0.9165
No log	67.0	134	0.7049	0.5660	0.7049	0.8396
No log	68.0	136	0.6506	0.5731	0.6506	0.8066
No log	69.0	138	0.6415	0.6076	0.6415	0.8009
No log	70.0	140	0.6900	0.5660	0.6900	0.8307
No log	71.0	142	0.6664	0.5968	0.6664	0.8163
No log	72.0	144	0.6729	0.5875	0.6729	0.8203
No log	73.0	146	0.6970	0.5532	0.6970	0.8349
No log	74.0	148	0.7036	0.5564	0.7036	0.8388
No log	75.0	150	0.6989	0.5560	0.6989	0.8360
No log	76.0	152	0.6818	0.5668	0.6818	0.8257
No log	77.0	154	0.6639	0.6056	0.6639	0.8148
No log	78.0	156	0.6898	0.5700	0.6898	0.8306
No log	79.0	158	0.6902	0.5809	0.6902	0.8308
No log	80.0	160	0.6708	0.5967	0.6708	0.8190
No log	81.0	162	0.6849	0.5893	0.6849	0.8276
No log	82.0	164	0.7280	0.5624	0.7280	0.8533
No log	83.0	166	0.7339	0.5645	0.7339	0.8567
No log	84.0	168	0.7091	0.5782	0.7091	0.8421
No log	85.0	170	0.6984	0.5858	0.6984	0.8357
No log	86.0	172	0.7124	0.5730	0.7124	0.8440
No log	87.0	174	0.7261	0.5507	0.7261	0.8521
No log	88.0	176	0.6911	0.5646	0.6911	0.8313
No log	89.0	178	0.6573	0.5933	0.6573	0.8107

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: Mar 7, 2025
Updated: Mar 7, 2025