Version23NewTestASAP_FineTuningBERT_AugV23_k1_task1_organization_k1_k1_fold3

genki10/Version23NewTestASAP_FineTuningBERT_AugV23_k1_task1_organization_k1_k1_fold3

Version23NewTestASAP_FineTuningBERT_AugV23_k1_task1_organization_k1_k1_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: Mar 16, 2025
Updated: Mar 16, 2025
SHA: 47bb7b67

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Base models

Model card

From the Hugging Face model README

Version23NewTestASAP_FineTuningBERT_AugV23_k1_task1_organization_k1_k1_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: 0.6585
Qwk: 0.6302
Mse: 0.6584
Rmse: 0.8114

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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	1	11.0663	0.0051	11.0645	3.3263
No log	2.0	2	9.9788	0.0	9.9771	3.1587
No log	3.0	3	8.8818	0.0	8.8803	2.9800
No log	4.0	4	7.9911	0.0	7.9896	2.8266
No log	5.0	5	7.2041	0.0	7.2027	2.6838
No log	6.0	6	6.4935	0.0	6.4922	2.5480
No log	7.0	7	5.8482	0.0301	5.8469	2.4180
No log	8.0	8	5.3042	0.0021	5.3029	2.3028
No log	9.0	9	4.8279	0.0	4.8266	2.1970
No log	10.0	10	4.3619	0.0	4.3608	2.0882
No log	11.0	11	3.9005	0.0	3.8993	1.9747
No log	12.0	12	3.4841	0.0	3.4830	1.8663
No log	13.0	13	3.0904	0.0	3.0894	1.7577
No log	14.0	14	2.7258	0.0	2.7249	1.6507
No log	15.0	15	2.4012	0.1921	2.4003	1.5493
No log	16.0	16	2.1024	0.1095	2.1016	1.4497
No log	17.0	17	1.8398	0.0799	1.8390	1.3561
No log	18.0	18	1.6114	0.0329	1.6107	1.2692
No log	19.0	19	1.4746	0.0202	1.4740	1.2141
No log	20.0	20	1.3003	0.0266	1.2997	1.1400
No log	21.0	21	1.1608	0.0266	1.1603	1.0772
No log	22.0	22	1.0377	0.0202	1.0372	1.0184
No log	23.0	23	0.9400	0.0266	0.9396	0.9693
No log	24.0	24	0.8567	0.2945	0.8562	0.9253
No log	25.0	25	0.7899	0.3816	0.7896	0.8886
No log	26.0	26	0.7720	0.4040	0.7716	0.8784
No log	27.0	27	0.6594	0.4848	0.6591	0.8118
No log	28.0	28	0.6599	0.4865	0.6595	0.8121
No log	29.0	29	0.6774	0.5074	0.6770	0.8228
No log	30.0	30	0.7428	0.4688	0.7424	0.8616
No log	31.0	31	0.6047	0.5363	0.6045	0.7775
No log	32.0	32	0.6896	0.5116	0.6894	0.8303
No log	33.0	33	0.7965	0.4563	0.7964	0.8924
No log	34.0	34	0.6479	0.5379	0.6479	0.8049
No log	35.0	35	0.7787	0.4749	0.7788	0.8825
No log	36.0	36	0.8631	0.4408	0.8632	0.9291
No log	37.0	37	0.6683	0.5162	0.6684	0.8176
No log	38.0	38	0.6897	0.5062	0.6899	0.8306
No log	39.0	39	0.6399	0.5613	0.6400	0.8000
No log	40.0	40	0.7618	0.5008	0.7621	0.8730
No log	41.0	41	0.6816	0.5378	0.6818	0.8257
No log	42.0	42	0.5858	0.6221	0.5857	0.7653
No log	43.0	43	0.5878	0.6189	0.5877	0.7666
No log	44.0	44	0.6068	0.6171	0.6069	0.7790
No log	45.0	45	0.7127	0.5251	0.7129	0.8443
No log	46.0	46	0.6607	0.5706	0.6609	0.8129
No log	47.0	47	0.5804	0.6270	0.5803	0.7618
No log	48.0	48	0.5853	0.6260	0.5851	0.7649
No log	49.0	49	0.6438	0.5875	0.6439	0.8024
No log	50.0	50	0.8295	0.4963	0.8297	0.9109
No log	51.0	51	0.8380	0.5081	0.8382	0.9155
No log	52.0	52	0.6874	0.5991	0.6875	0.8292
No log	53.0	53	0.6501	0.6168	0.6500	0.8062
No log	54.0	54	0.6533	0.6146	0.6532	0.8082
No log	55.0	55	0.7001	0.5955	0.7002	0.8368
No log	56.0	56	0.8966	0.4827	0.8968	0.9470
No log	57.0	57	0.9105	0.4701	0.9107	0.9543
No log	58.0	58	0.7356	0.5463	0.7358	0.8578
No log	59.0	59	0.6122	0.6222	0.6122	0.7824
No log	60.0	60	0.6238	0.6507	0.6237	0.7897
No log	61.0	61	0.5915	0.6394	0.5915	0.7691
No log	62.0	62	0.6467	0.5825	0.6468	0.8042
No log	63.0	63	0.6812	0.5597	0.6813	0.8254
No log	64.0	64	0.6164	0.5939	0.6165	0.7852
No log	65.0	65	0.5931	0.6297	0.5930	0.7701
No log	66.0	66	0.6324	0.6379	0.6323	0.7952
No log	67.0	67	0.6142	0.6231	0.6142	0.7837
No log	68.0	68	0.6280	0.5925	0.6281	0.7925
No log	69.0	69	0.6543	0.5659	0.6544	0.8090
No log	70.0	70	0.6274	0.5964	0.6276	0.7922
No log	71.0	71	0.6540	0.6303	0.6539	0.8086
No log	72.0	72	0.6759	0.6246	0.6758	0.8221
No log	73.0	73	0.6340	0.6176	0.6340	0.7962
No log	74.0	74	0.6422	0.5802	0.6424	0.8015
No log	75.0	75	0.6702	0.5597	0.6704	0.8188
No log	76.0	76	0.6371	0.5845	0.6373	0.7983
No log	77.0	77	0.6061	0.6376	0.6061	0.7785
No log	78.0	78	0.6683	0.6258	0.6682	0.8174
No log	79.0	79	0.6937	0.6152	0.6935	0.8328
No log	80.0	80	0.6368	0.6349	0.6366	0.7979
No log	81.0	81	0.5760	0.6330	0.5760	0.7589
No log	82.0	82	0.6458	0.5705	0.6459	0.8037
No log	83.0	83	0.7281	0.5243	0.7282	0.8534
No log	84.0	84	0.7228	0.5285	0.7229	0.8503
No log	85.0	85	0.6439	0.5701	0.6440	0.8025
No log	86.0	86	0.5688	0.6232	0.5689	0.7542
No log	87.0	87	0.6266	0.6345	0.6264	0.7915
No log	88.0	88	0.7163	0.6159	0.7160	0.8462
No log	89.0	89	0.7237	0.6188	0.7234	0.8505
No log	90.0	90	0.6585	0.6302	0.6584	0.8114

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 16, 2025
Updated: Mar 16, 2025