ASAP_nosemanticV2_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

genki10/ASAP_nosemanticV2_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

ASAP_nosemanticV2_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. --

Downloads · 30 days

32% of all-time downloads

All-time downloads

Public

Repo size

36.4 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 8, 2025
Updated: Mar 8, 2025
SHA: 1d68612e

Try a prompt

Base models

Model card

From the Hugging Face model README

ASAP_nosemanticV2_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: 0.6971
Qwk: 0.5584
Mse: 0.6971
Rmse: 0.8349

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	4	5.8550	0.0445	5.8550	2.4197
No log	2.0	8	3.5392	0.0118	3.5392	1.8813
No log	3.0	12	2.2624	0.1266	2.2624	1.5041
No log	4.0	16	1.5638	0.0420	1.5638	1.2505
No log	5.0	20	1.4770	0.1001	1.4770	1.2153
No log	6.0	24	1.1457	0.1226	1.1457	1.0704
No log	7.0	28	1.3191	0.1970	1.3191	1.1485
No log	8.0	32	0.8228	0.1874	0.8228	0.9071
No log	9.0	36	1.0627	0.2118	1.0627	1.0309
No log	10.0	40	0.8289	0.3399	0.8289	0.9105
No log	11.0	44	0.6515	0.5136	0.6515	0.8072
No log	12.0	48	0.9406	0.4283	0.9406	0.9699
No log	13.0	52	0.6402	0.4645	0.6402	0.8001
No log	14.0	56	0.6856	0.4711	0.6856	0.8280
No log	15.0	60	0.7281	0.4407	0.7281	0.8533
No log	16.0	64	0.7719	0.4007	0.7719	0.8786
No log	17.0	68	0.6666	0.5156	0.6666	0.8164
No log	18.0	72	0.6093	0.5493	0.6093	0.7806
No log	19.0	76	0.7199	0.5189	0.7199	0.8485
No log	20.0	80	0.6970	0.5219	0.6970	0.8349
No log	21.0	84	1.1404	0.3841	1.1404	1.0679
No log	22.0	88	1.0080	0.3973	1.0080	1.0040
No log	23.0	92	0.7881	0.4916	0.7881	0.8878
No log	24.0	96	0.7528	0.5093	0.7528	0.8677
No log	25.0	100	0.7079	0.5431	0.7079	0.8413
No log	26.0	104	0.7835	0.5351	0.7835	0.8852
No log	27.0	108	0.6904	0.5523	0.6904	0.8309
No log	28.0	112	0.7012	0.5123	0.7012	0.8374
No log	29.0	116	0.8281	0.4749	0.8281	0.9100
No log	30.0	120	0.6978	0.4795	0.6978	0.8354
No log	31.0	124	0.7171	0.5277	0.7171	0.8468
No log	32.0	128	0.7011	0.4944	0.7011	0.8373
No log	33.0	132	0.8210	0.5098	0.8210	0.9061
No log	34.0	136	0.7717	0.4919	0.7717	0.8784
No log	35.0	140	0.9666	0.4750	0.9666	0.9832
No log	36.0	144	0.6536	0.5537	0.6536	0.8084
No log	37.0	148	0.6702	0.5567	0.6702	0.8187
No log	38.0	152	0.6771	0.5682	0.6771	0.8228
No log	39.0	156	0.6596	0.5649	0.6596	0.8121
No log	40.0	160	0.7924	0.5289	0.7924	0.8901
No log	41.0	164	0.7213	0.5129	0.7213	0.8493
No log	42.0	168	0.6734	0.5375	0.6734	0.8206
No log	43.0	172	0.9722	0.4911	0.9722	0.9860
No log	44.0	176	0.7177	0.5141	0.7177	0.8471
No log	45.0	180	0.6508	0.5222	0.6508	0.8067
No log	46.0	184	0.6735	0.5259	0.6735	0.8207
No log	47.0	188	0.6574	0.5510	0.6574	0.8108
No log	48.0	192	0.7117	0.5398	0.7117	0.8436
No log	49.0	196	0.6603	0.5596	0.6603	0.8126
No log	50.0	200	0.6917	0.5696	0.6917	0.8317
No log	51.0	204	0.8032	0.5649	0.8032	0.8962
No log	52.0	208	0.6672	0.5598	0.6672	0.8168
No log	53.0	212	0.6552	0.5634	0.6552	0.8095
No log	54.0	216	0.8263	0.5532	0.8263	0.9090
No log	55.0	220	0.6676	0.5690	0.6676	0.8171
No log	56.0	224	0.7128	0.5595	0.7128	0.8442
No log	57.0	228	0.6271	0.5608	0.6271	0.7919
No log	58.0	232	0.8424	0.5334	0.8424	0.9178
No log	59.0	236	0.6840	0.5660	0.6840	0.8270
No log	60.0	240	0.7159	0.5595	0.7159	0.8461
No log	61.0	244	0.6536	0.5506	0.6536	0.8084
No log	62.0	248	0.8329	0.5314	0.8329	0.9126
No log	63.0	252	0.7482	0.5440	0.7482	0.8650
No log	64.0	256	0.6769	0.5350	0.6769	0.8227
No log	65.0	260	0.6623	0.5586	0.6623	0.8138
No log	66.0	264	0.6496	0.5554	0.6496	0.8060
No log	67.0	268	0.6773	0.5500	0.6773	0.8230
No log	68.0	272	0.6845	0.5506	0.6845	0.8273
No log	69.0	276	0.7115	0.5462	0.7115	0.8435
No log	70.0	280	0.6971	0.5584	0.6971	0.8349

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 8, 2025
Updated: Mar 8, 2025