ASAP_nosemantic_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

genki10/ASAP_nosemantic_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

ASAP_nosemantic_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4 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: 2be29b64

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Base models

Model card

From the Hugging Face model README

ASAP_nosemantic_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

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: 0.6428
Qwk: 0.6237
Mse: 0.6428
Rmse: 0.8017

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	10.8361	0.0192	10.8361	3.2918
No log	2.0	4	10.1920	0.0037	10.1920	3.1925
No log	3.0	6	9.3550	0.0018	9.3550	3.0586
No log	4.0	8	7.9538	0.0	7.9538	2.8203
No log	5.0	10	7.1325	0.0	7.1325	2.6707
No log	6.0	12	6.4798	0.0	6.4798	2.5455
No log	7.0	14	5.5695	0.0157	5.5695	2.3600
No log	8.0	16	4.6102	0.0079	4.6102	2.1471
No log	9.0	18	3.6342	0.0079	3.6342	1.9063
No log	10.0	20	2.9321	0.0	2.9321	1.7123
No log	11.0	22	2.3515	0.1465	2.3515	1.5335
No log	12.0	24	1.9326	0.0746	1.9326	1.3902
No log	13.0	26	1.6064	0.0213	1.6064	1.2675
No log	14.0	28	1.2741	0.0213	1.2741	1.1288
No log	15.0	30	1.0659	0.0213	1.0659	1.0324
No log	16.0	32	0.8800	0.0213	0.8800	0.9381
No log	17.0	34	0.7816	0.3759	0.7816	0.8841
No log	18.0	36	1.2260	0.2111	1.2260	1.1073
No log	19.0	38	0.8521	0.4261	0.8521	0.9231
No log	20.0	40	0.6033	0.4475	0.6033	0.7767
No log	21.0	42	0.5952	0.5536	0.5952	0.7715
No log	22.0	44	1.2814	0.2785	1.2814	1.1320
No log	23.0	46	0.7902	0.4652	0.7902	0.8889
No log	24.0	48	0.5382	0.5262	0.5382	0.7336
No log	25.0	50	0.6054	0.5538	0.6054	0.7781
No log	26.0	52	1.2011	0.3906	1.2011	1.0959
No log	27.0	54	0.8746	0.4738	0.8746	0.9352
No log	28.0	56	0.5568	0.5622	0.5568	0.7462
No log	29.0	58	0.6225	0.5356	0.6225	0.7890
No log	30.0	60	0.6305	0.5404	0.6305	0.7940
No log	31.0	62	0.5779	0.5526	0.5779	0.7602
No log	32.0	64	0.5652	0.6185	0.5652	0.7518
No log	33.0	66	0.5918	0.5767	0.5918	0.7693
No log	34.0	68	0.5705	0.6138	0.5705	0.7553
No log	35.0	70	0.6111	0.6095	0.6111	0.7817
No log	36.0	72	0.6958	0.6080	0.6958	0.8342
No log	37.0	74	0.6979	0.6232	0.6979	0.8354
No log	38.0	76	0.6739	0.6157	0.6739	0.8209
No log	39.0	78	0.6782	0.6209	0.6782	0.8235
No log	40.0	80	0.6618	0.6250	0.6618	0.8135
No log	41.0	82	0.6430	0.6183	0.6430	0.8018
No log	42.0	84	0.6785	0.6162	0.6785	0.8237
No log	43.0	86	0.6363	0.6219	0.6363	0.7977
No log	44.0	88	0.6404	0.6270	0.6404	0.8003
No log	45.0	90	0.6420	0.6082	0.6420	0.8013
No log	46.0	92	0.6539	0.6112	0.6539	0.8086
No log	47.0	94	0.6699	0.6249	0.6699	0.8185
No log	48.0	96	0.6516	0.6239	0.6516	0.8072
No log	49.0	98	0.6393	0.6221	0.6393	0.7996
No log	50.0	100	0.6530	0.6309	0.6530	0.8081
No log	51.0	102	0.6689	0.6319	0.6689	0.8179
No log	52.0	104	0.7674	0.6085	0.7674	0.8760
No log	53.0	106	0.6532	0.6066	0.6532	0.8082
No log	54.0	108	0.6407	0.6131	0.6407	0.8004
No log	55.0	110	0.6648	0.6354	0.6648	0.8153
No log	56.0	112	0.5951	0.6255	0.5951	0.7714
No log	57.0	114	0.5901	0.6293	0.5901	0.7682
No log	58.0	116	0.6800	0.6161	0.6800	0.8246
No log	59.0	118	0.7292	0.5973	0.7292	0.8540
No log	60.0	120	0.6861	0.6128	0.6861	0.8283
No log	61.0	122	0.8446	0.5747	0.8446	0.9190
No log	62.0	124	0.8278	0.5820	0.8278	0.9098
No log	63.0	126	0.6809	0.6139	0.6809	0.8251
No log	64.0	128	0.7127	0.6006	0.7127	0.8442
No log	65.0	130	0.6469	0.6300	0.6469	0.8043
No log	66.0	132	0.6673	0.6156	0.6673	0.8169
No log	67.0	134	0.6398	0.6263	0.6398	0.7999
No log	68.0	136	0.6615	0.6172	0.6615	0.8133
No log	69.0	138	0.7511	0.6052	0.7511	0.8667
No log	70.0	140	0.7198	0.6201	0.7198	0.8484
No log	71.0	142	0.6394	0.6127	0.6394	0.7996
No log	72.0	144	0.6670	0.6197	0.6670	0.8167
No log	73.0	146	0.7791	0.5807	0.7791	0.8827
No log	74.0	148	0.7347	0.5917	0.7347	0.8571
No log	75.0	150	0.6428	0.6237	0.6428	0.8017

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