Version27NewTestASAP_FineTuningBERT_AugV27_k5_task1_organization_k5_k5_fold3

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k5_task1_organization_k5_k5_fold3

Version27NewTestASAP_FineTuningBERT_AugV27_k5_task1_organization_k5_k5_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 18, 2025
Updated: Mar 18, 2025
SHA: 334ef7ed

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Base models

Model card

From the Hugging Face model README

Version27NewTestASAP_FineTuningBERT_AugV27_k5_task1_organization_k5_k5_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: 0.5320
Qwk: 0.6145
Mse: 0.5322
Rmse: 0.7295

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	3	8.1033	0.0	8.1017	2.8463
No log	2.0	6	6.1441	-0.0201	6.1426	2.4784
No log	3.0	9	4.4799	0.0	4.4787	2.1163
No log	4.0	12	3.1098	0.0	3.1088	1.7632
No log	5.0	15	2.1736	0.0729	2.1728	1.4740
No log	6.0	18	1.5543	0.0102	1.5537	1.2465
No log	7.0	21	1.1188	0.0102	1.1182	1.0575
No log	8.0	24	0.8827	0.2844	0.8822	0.9393
No log	9.0	27	0.7680	0.4099	0.7677	0.8762
No log	10.0	30	1.2889	0.2427	1.2886	1.1352
No log	11.0	33	0.6709	0.5354	0.6707	0.8190
No log	12.0	36	0.9098	0.4255	0.9097	0.9538
No log	13.0	39	0.6906	0.5335	0.6905	0.8310
No log	14.0	42	0.9181	0.4326	0.9182	0.9582
No log	15.0	45	0.5182	0.5708	0.5182	0.7198
No log	16.0	48	0.7317	0.4891	0.7318	0.8554
No log	17.0	51	0.5257	0.5648	0.5257	0.7250
No log	18.0	54	0.5976	0.5477	0.5978	0.7732
No log	19.0	57	0.5740	0.5514	0.5740	0.7576
No log	20.0	60	0.5817	0.5925	0.5819	0.7628
No log	21.0	63	0.5893	0.5928	0.5893	0.7677
No log	22.0	66	0.6168	0.5871	0.6171	0.7855
No log	23.0	69	0.5963	0.5959	0.5963	0.7722
No log	24.0	72	0.5889	0.6076	0.5892	0.7676
No log	25.0	75	0.5734	0.6219	0.5735	0.7573
No log	26.0	78	0.6874	0.5422	0.6879	0.8294
No log	27.0	81	0.5966	0.6216	0.5968	0.7725
No log	28.0	84	0.6771	0.5435	0.6775	0.8231
No log	29.0	87	0.5891	0.6294	0.5894	0.7677
No log	30.0	90	0.6294	0.5670	0.6298	0.7936
No log	31.0	93	0.6019	0.6141	0.6021	0.7759
No log	32.0	96	0.5685	0.6153	0.5689	0.7542
No log	33.0	99	0.5746	0.6274	0.5750	0.7583
No log	34.0	102	0.6464	0.6209	0.6465	0.8040
No log	35.0	105	0.6160	0.5933	0.6163	0.7851
No log	36.0	108	0.5881	0.6014	0.5884	0.7671
No log	37.0	111	0.5714	0.6287	0.5717	0.7561
No log	38.0	114	0.6194	0.6199	0.6196	0.7872
No log	39.0	117	0.6052	0.6379	0.6054	0.7781
No log	40.0	120	0.5743	0.6050	0.5747	0.7581
No log	41.0	123	0.5317	0.6319	0.5320	0.7294
No log	42.0	126	0.5403	0.6309	0.5405	0.7352
No log	43.0	129	0.5430	0.6145	0.5432	0.7371
No log	44.0	132	0.5593	0.6167	0.5596	0.7481
No log	45.0	135	0.5866	0.6415	0.5867	0.7660
No log	46.0	138	0.5553	0.6029	0.5556	0.7454
No log	47.0	141	0.5282	0.6362	0.5284	0.7269
No log	48.0	144	0.5854	0.5811	0.5857	0.7653
No log	49.0	147	0.5430	0.6334	0.5432	0.7370
No log	50.0	150	0.6085	0.5701	0.6089	0.7803
No log	51.0	153	0.5337	0.6441	0.5339	0.7307
No log	52.0	156	0.5962	0.5969	0.5966	0.7724
No log	53.0	159	0.6046	0.6186	0.6048	0.7777
No log	54.0	162	0.5725	0.6084	0.5728	0.7568
No log	55.0	165	0.5690	0.6394	0.5692	0.7544
No log	56.0	168	0.6593	0.5497	0.6597	0.8122
No log	57.0	171	0.5644	0.6235	0.5645	0.7514
No log	58.0	174	0.5514	0.6213	0.5516	0.7427
No log	59.0	177	0.6257	0.5536	0.6260	0.7912
No log	60.0	180	0.5564	0.6194	0.5565	0.7460
No log	61.0	183	0.5571	0.5808	0.5573	0.7465
No log	62.0	186	0.5266	0.6182	0.5268	0.7258
No log	63.0	189	0.5824	0.6232	0.5824	0.7631
No log	64.0	192	0.5475	0.5960	0.5478	0.7401
No log	65.0	195	0.5719	0.6301	0.5719	0.7563
No log	66.0	198	0.5442	0.6412	0.5443	0.7378
No log	67.0	201	0.5986	0.5714	0.5989	0.7739
No log	68.0	204	0.5400	0.6269	0.5401	0.7349
No log	69.0	207	0.5619	0.5925	0.5622	0.7498
No log	70.0	210	0.5380	0.6130	0.5382	0.7336
No log	71.0	213	0.5276	0.6438	0.5277	0.7264
No log	72.0	216	0.5798	0.5900	0.5801	0.7616
No log	73.0	219	0.5400	0.6473	0.5401	0.7349
No log	74.0	222	0.5328	0.6223	0.5330	0.7301
No log	75.0	225	0.5297	0.5989	0.5299	0.7279
No log	76.0	228	0.5233	0.6307	0.5235	0.7235
No log	77.0	231	0.5464	0.6023	0.5466	0.7393
No log	78.0	234	0.5526	0.6527	0.5527	0.7435
No log	79.0	237	0.5687	0.6026	0.5690	0.7543
No log	80.0	240	0.5402	0.6137	0.5405	0.7352
No log	81.0	243	0.5431	0.6494	0.5432	0.7370
No log	82.0	246	0.5480	0.6026	0.5482	0.7404
No log	83.0	249	0.5298	0.6487	0.5299	0.7280
No log	84.0	252	0.5272	0.6482	0.5274	0.7262
No log	85.0	255	0.5372	0.5983	0.5374	0.7331
No log	86.0	258	0.5365	0.6438	0.5367	0.7326
No log	87.0	261	0.5460	0.5993	0.5463	0.7391
No log	88.0	264	0.5233	0.6375	0.5234	0.7235
No log	89.0	267	0.5207	0.6252	0.5209	0.7218
No log	90.0	270	0.5318	0.6101	0.5321	0.7294
No log	91.0	273	0.5244	0.6357	0.5245	0.7242
No log	92.0	276	0.5372	0.5895	0.5374	0.7331
No log	93.0	279	0.5167	0.6417	0.5169	0.7190
No log	94.0	282	0.5207	0.6366	0.5209	0.7218
No log	95.0	285	0.5264	0.6218	0.5266	0.7257
No log	96.0	288	0.5229	0.6370	0.5231	0.7233
No log	97.0	291	0.5254	0.6157	0.5256	0.7250
No log	98.0	294	0.5279	0.6318	0.5280	0.7267
No log	99.0	297	0.5340	0.6048	0.5342	0.7309
No log	100.0	300	0.5292	0.6034	0.5294	0.7276
No log	101.0	303	0.5221	0.6420	0.5223	0.7227
No log	102.0	306	0.5340	0.6028	0.5342	0.7309
No log	103.0	309	0.5224	0.6458	0.5226	0.7229
No log	104.0	312	0.5209	0.6220	0.5211	0.7219
No log	105.0	315	0.5478	0.5967	0.5480	0.7403
No log	106.0	318	0.5238	0.6444	0.5240	0.7239
No log	107.0	321	0.5269	0.6224	0.5271	0.7260
No log	108.0	324	0.5320	0.6145	0.5322	0.7295

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 18, 2025
Updated: Mar 18, 2025