Version19ASAP_FineTuningBERT_AugV19_k1_task1_organization_k1_k1_fold0

genki10/Version19ASAP_FineTuningBERT_AugV19_k1_task1_organization_k1_k1_fold0

Version19ASAP_FineTuningBERT_AugV19_k1_task1_organization_k1_k1_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. --

Downloads · 30 days

38% of all-time downloads

All-time downloads

Public

Repo size

46.4 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin876 MB · 100%

At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Mar 9, 2025
Updated: Mar 9, 2025
SHA: 92a7a170

Try a prompt

Base models

Model card

From the Hugging Face model README

Version19ASAP_FineTuningBERT_AugV19_k1_task1_organization_k1_k1_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: 0.6073
Qwk: 0.5917
Mse: 0.6073
Rmse: 0.7793

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	1	13.5429	0.0	13.5429	3.6801
No log	2.0	2	11.3456	-0.0001	11.3456	3.3683
No log	3.0	3	8.8799	0.0054	8.8799	2.9799
No log	4.0	4	8.0570	0.0036	8.0570	2.8385
No log	5.0	5	7.5989	0.0018	7.5989	2.7566
No log	6.0	6	6.2731	0.0488	6.2731	2.5046
No log	7.0	7	5.3784	0.0303	5.3784	2.3191
No log	8.0	8	4.8554	0.0153	4.8554	2.2035
No log	9.0	9	4.2738	0.0115	4.2738	2.0673
No log	10.0	10	3.6513	0.0115	3.6513	1.9108
No log	11.0	11	3.0573	0.0115	3.0573	1.7485
No log	12.0	12	3.0166	0.0179	3.0166	1.7368
No log	13.0	13	2.5004	0.0840	2.5004	1.5812
No log	14.0	14	2.0890	0.1050	2.0890	1.4453
No log	15.0	15	1.9397	0.0773	1.9397	1.3927
No log	16.0	16	1.7381	0.0646	1.7381	1.3184
No log	17.0	17	1.4890	0.0520	1.4890	1.2203
No log	18.0	18	1.3305	0.0520	1.3305	1.1535
No log	19.0	19	1.2762	0.0316	1.2762	1.1297
No log	20.0	20	1.1151	0.0316	1.1151	1.0560
No log	21.0	21	0.9556	0.0316	0.9556	0.9775
No log	22.0	22	0.9285	0.0481	0.9285	0.9636
No log	23.0	23	0.8863	0.1227	0.8863	0.9415
No log	24.0	24	0.8106	0.2667	0.8106	0.9003
No log	25.0	25	0.7382	0.2813	0.7382	0.8592
No log	26.0	26	0.7041	0.2353	0.7041	0.8391
No log	27.0	27	0.6760	0.2477	0.6760	0.8222
No log	28.0	28	0.6564	0.2656	0.6564	0.8102
No log	29.0	29	0.6421	0.2623	0.6421	0.8013
No log	30.0	30	0.6293	0.2778	0.6293	0.7933
No log	31.0	31	0.6299	0.2775	0.6299	0.7937
No log	32.0	32	0.6211	0.2748	0.6211	0.7881
No log	33.0	33	0.6035	0.2954	0.6035	0.7768
No log	34.0	34	0.5693	0.3812	0.5693	0.7545
No log	35.0	35	0.5578	0.4511	0.5578	0.7469
No log	36.0	36	0.5476	0.4657	0.5476	0.7400
No log	37.0	37	0.5641	0.4804	0.5641	0.7511
No log	38.0	38	0.5815	0.5286	0.5815	0.7625
No log	39.0	39	0.5685	0.5394	0.5685	0.7540
No log	40.0	40	0.6413	0.5185	0.6413	0.8008
No log	41.0	41	0.6774	0.5158	0.6774	0.8231
No log	42.0	42	0.6164	0.5246	0.6164	0.7851
No log	43.0	43	0.6411	0.5883	0.6411	0.8007
No log	44.0	44	0.7027	0.5734	0.7027	0.8383
No log	45.0	45	0.6695	0.6056	0.6695	0.8182
No log	46.0	46	0.6072	0.5844	0.6072	0.7793
No log	47.0	47	0.6025	0.5642	0.6025	0.7762
No log	48.0	48	0.6034	0.6014	0.6034	0.7768
No log	49.0	49	0.6451	0.6165	0.6451	0.8032
No log	50.0	50	0.6596	0.6075	0.6596	0.8122
No log	51.0	51	0.6193	0.6031	0.6193	0.7870
No log	52.0	52	0.5938	0.5724	0.5938	0.7706
No log	53.0	53	0.5836	0.5784	0.5836	0.7640
No log	54.0	54	0.5875	0.6243	0.5875	0.7665
No log	55.0	55	0.6843	0.6039	0.6843	0.8272
No log	56.0	56	0.6989	0.5924	0.6989	0.8360
No log	57.0	57	0.6227	0.6216	0.6227	0.7891
No log	58.0	58	0.5677	0.6287	0.5677	0.7535
No log	59.0	59	0.5737	0.6007	0.5737	0.7574
No log	60.0	60	0.5879	0.6081	0.5879	0.7668
No log	61.0	61	0.6205	0.6140	0.6205	0.7877
No log	62.0	62	0.6609	0.6138	0.6609	0.8130
No log	63.0	63	0.6507	0.6131	0.6507	0.8067
No log	64.0	64	0.5896	0.6271	0.5896	0.7679
No log	65.0	65	0.5418	0.6143	0.5418	0.7361
No log	66.0	66	0.5363	0.5993	0.5363	0.7323
No log	67.0	67	0.5290	0.6075	0.5290	0.7273
No log	68.0	68	0.5410	0.6200	0.5410	0.7355
No log	69.0	69	0.6149	0.6211	0.6149	0.7842
No log	70.0	70	0.7667	0.5695	0.7667	0.8756
No log	71.0	71	0.8454	0.5596	0.8454	0.9194
No log	72.0	72	0.8232	0.5615	0.8232	0.9073
No log	73.0	73	0.7316	0.5858	0.7316	0.8554
No log	74.0	74	0.6352	0.6144	0.6352	0.7970
No log	75.0	75	0.5994	0.5904	0.5994	0.7742
No log	76.0	76	0.6147	0.5880	0.6147	0.7840
No log	77.0	77	0.6194	0.5915	0.6194	0.7870
No log	78.0	78	0.6073	0.5917	0.6073	0.7793

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 9, 2025
Updated: Mar 9, 2025