Version25NewTestASAP_FineTuningBERT_AugV25_k5_task1_organization_k5_k5_fold0

genki10/Version25NewTestASAP_FineTuningBERT_AugV25_k5_task1_organization_k5_k5_fold0

Version25NewTestASAP_FineTuningBERT_AugV25_k5_task1_organization_k5_k5_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

39% of all-time downloads

All-time downloads

Public

Repo size

27.2 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 17, 2025
Updated: Mar 17, 2025
SHA: 39a8ff69

Try a prompt

Base models

Model card

From the Hugging Face model README

Version25NewTestASAP_FineTuningBERT_AugV25_k5_task1_organization_k5_k5_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: 0.5758
Qwk: 0.5340
Mse: 0.5758
Rmse: 0.7588

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	4	7.5818	0.0	7.5818	2.7535
No log	2.0	8	5.8395	0.0203	5.8395	2.4165
No log	3.0	12	4.2190	0.0039	4.2190	2.0540
No log	4.0	16	2.6127	-0.0008	2.6126	1.6164
No log	5.0	20	1.5534	0.0316	1.5534	1.2464
No log	6.0	24	1.1307	0.0316	1.1307	1.0634
No log	7.0	28	0.8850	0.1108	0.8850	0.9407
No log	8.0	32	0.9319	0.1703	0.9319	0.9654
No log	9.0	36	1.5052	0.2087	1.5052	1.2269
No log	10.0	40	0.6529	0.4744	0.6529	0.8080
No log	11.0	44	0.6838	0.4414	0.6838	0.8269
No log	12.0	48	0.7211	0.4366	0.7211	0.8492
No log	13.0	52	0.5818	0.4733	0.5818	0.7627
No log	14.0	56	0.6095	0.5018	0.6095	0.7807
No log	15.0	60	0.6412	0.5313	0.6412	0.8007
No log	16.0	64	0.6192	0.5425	0.6192	0.7869
No log	17.0	68	0.7106	0.4716	0.7106	0.8430
No log	18.0	72	0.6711	0.5386	0.6711	0.8192
No log	19.0	76	1.1548	0.3225	1.1548	1.0746
No log	20.0	80	0.7791	0.4624	0.7791	0.8827
No log	21.0	84	0.7019	0.4538	0.7019	0.8378
No log	22.0	88	0.6238	0.5111	0.6238	0.7898
No log	23.0	92	0.6639	0.5093	0.6639	0.8148
No log	24.0	96	0.6514	0.4763	0.6514	0.8071
No log	25.0	100	0.6006	0.5475	0.6006	0.7750
No log	26.0	104	0.6372	0.5332	0.6372	0.7983
No log	27.0	108	0.5843	0.5427	0.5843	0.7644
No log	28.0	112	0.5594	0.5560	0.5594	0.7479
No log	29.0	116	0.6352	0.5230	0.6352	0.7970
No log	30.0	120	0.6016	0.5072	0.6016	0.7756
No log	31.0	124	0.5777	0.5238	0.5777	0.7601
No log	32.0	128	0.6002	0.5343	0.6002	0.7747
No log	33.0	132	0.5840	0.5262	0.5840	0.7642
No log	34.0	136	0.6373	0.4957	0.6373	0.7983
No log	35.0	140	0.6639	0.4942	0.6639	0.8148
No log	36.0	144	0.6215	0.4929	0.6215	0.7884
No log	37.0	148	0.8175	0.4564	0.8175	0.9042
No log	38.0	152	0.6942	0.4765	0.6942	0.8332
No log	39.0	156	0.6694	0.4905	0.6694	0.8182
No log	40.0	160	0.6474	0.5034	0.6474	0.8046
No log	41.0	164	0.6186	0.5402	0.6186	0.7865
No log	42.0	168	0.5924	0.5279	0.5924	0.7697
No log	43.0	172	0.5893	0.5199	0.5893	0.7677
No log	44.0	176	0.5795	0.5167	0.5795	0.7612
No log	45.0	180	0.6257	0.4933	0.6257	0.7910
No log	46.0	184	0.5848	0.5271	0.5848	0.7647
No log	47.0	188	0.5991	0.5148	0.5991	0.7740
No log	48.0	192	0.5655	0.5405	0.5655	0.7520
No log	49.0	196	0.6122	0.5343	0.6122	0.7824
No log	50.0	200	0.6002	0.5364	0.6002	0.7747
No log	51.0	204	0.6229	0.5218	0.6229	0.7893
No log	52.0	208	0.5958	0.5209	0.5958	0.7719
No log	53.0	212	0.6666	0.4966	0.6666	0.8165
No log	54.0	216	0.6053	0.5301	0.6053	0.7780
No log	55.0	220	0.6573	0.5015	0.6573	0.8108
No log	56.0	224	0.6325	0.4904	0.6325	0.7953
No log	57.0	228	0.5979	0.5278	0.5979	0.7733
No log	58.0	232	0.5758	0.5340	0.5758	0.7588

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 17, 2025
Updated: Mar 17, 2025