Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_fold4

genki10/Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_fold4

Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_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

40% of all-time downloads

All-time downloads

Public

Repo size

42.5 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: 83d1c423

Try a prompt

Base models

Model card

From the Hugging Face model README

Version19ASAP_FineTuningBERT_AugV19_k15_task1_organization_k15_k15_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: 0.7782
Qwk: 0.6243
Mse: 0.7782
Rmse: 0.8822

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	3	11.4932	0.0079	11.4932	3.3902
No log	2.0	6	9.0431	0.0	9.0431	3.0072
No log	3.0	9	5.1558	0.0515	5.1558	2.2706
No log	4.0	12	3.5481	0.0040	3.5481	1.8836
No log	5.0	15	2.5485	0.0637	2.5485	1.5964
No log	6.0	18	1.6523	0.0544	1.6523	1.2854
No log	7.0	21	1.0902	0.0213	1.0902	1.0441
No log	8.0	24	0.8389	0.3302	0.8389	0.9159
No log	9.0	27	0.7856	0.1929	0.7856	0.8863
No log	10.0	30	0.7614	0.1302	0.7614	0.8726
No log	11.0	33	1.0462	0.0454	1.0462	1.0228
No log	12.0	36	1.0356	0.2837	1.0356	1.0176
No log	13.0	39	1.2979	0.2780	1.2979	1.1393
No log	14.0	42	0.7771	0.4410	0.7771	0.8815
No log	15.0	45	0.8350	0.5523	0.8350	0.9138
No log	16.0	48	0.9286	0.5424	0.9286	0.9636
No log	17.0	51	0.6948	0.6129	0.6948	0.8335
No log	18.0	54	0.7963	0.6150	0.7963	0.8924
No log	19.0	57	0.9374	0.5975	0.9374	0.9682
No log	20.0	60	0.7044	0.6415	0.7044	0.8393
No log	21.0	63	0.9016	0.5856	0.9016	0.9495
No log	22.0	66	0.8134	0.5990	0.8134	0.9019
No log	23.0	69	0.7051	0.6281	0.7051	0.8397
No log	24.0	72	0.9434	0.5891	0.9434	0.9713
No log	25.0	75	0.7369	0.6260	0.7369	0.8584
No log	26.0	78	0.7869	0.6334	0.7869	0.8871
No log	27.0	81	0.8456	0.6175	0.8456	0.9195
No log	28.0	84	0.8454	0.6082	0.8454	0.9195
No log	29.0	87	0.9459	0.5907	0.9459	0.9726
No log	30.0	90	0.7817	0.6363	0.7817	0.8841
No log	31.0	93	0.8818	0.6180	0.8818	0.9390
No log	32.0	96	0.7111	0.6459	0.7111	0.8433
No log	33.0	99	0.8854	0.6060	0.8854	0.9410
No log	34.0	102	0.7100	0.6292	0.7100	0.8426
No log	35.0	105	0.8142	0.6271	0.8142	0.9023
No log	36.0	108	0.7496	0.6428	0.7496	0.8658
No log	37.0	111	0.9258	0.6069	0.9258	0.9622
No log	38.0	114	0.7829	0.6288	0.7829	0.8848
No log	39.0	117	0.7873	0.6274	0.7873	0.8873
No log	40.0	120	1.0052	0.5793	1.0052	1.0026
No log	41.0	123	0.6833	0.6493	0.6833	0.8266
No log	42.0	126	0.7639	0.6320	0.7639	0.8740
No log	43.0	129	0.7964	0.6194	0.7964	0.8924
No log	44.0	132	0.7253	0.6390	0.7253	0.8516
No log	45.0	135	0.6640	0.6474	0.6640	0.8149
No log	46.0	138	0.6528	0.6493	0.6528	0.8080
No log	47.0	141	0.8072	0.6178	0.8072	0.8984
No log	48.0	144	0.6998	0.6525	0.6998	0.8365
No log	49.0	147	1.0119	0.5670	1.0119	1.0059
No log	50.0	150	0.9317	0.5941	0.9317	0.9652
No log	51.0	153	0.7313	0.6573	0.7313	0.8551
No log	52.0	156	1.0315	0.5755	1.0315	1.0156
No log	53.0	159	0.9268	0.6041	0.9268	0.9627
No log	54.0	162	0.7207	0.6442	0.7207	0.8489
No log	55.0	165	0.9560	0.5784	0.9560	0.9777
No log	56.0	168	0.8121	0.6034	0.8121	0.9012
No log	57.0	171	0.7185	0.6505	0.7185	0.8477
No log	58.0	174	0.7858	0.6280	0.7858	0.8864
No log	59.0	177	0.6338	0.6567	0.6338	0.7961
No log	60.0	180	0.6537	0.6563	0.6537	0.8085
No log	61.0	183	0.7157	0.6452	0.7157	0.8460
No log	62.0	186	0.6717	0.6614	0.6717	0.8196
No log	63.0	189	0.7197	0.6435	0.7197	0.8483
No log	64.0	192	0.7273	0.6389	0.7273	0.8528
No log	65.0	195	0.7952	0.6232	0.7952	0.8918
No log	66.0	198	0.6692	0.6572	0.6692	0.8180
No log	67.0	201	0.7358	0.6413	0.7358	0.8578
No log	68.0	204	0.8883	0.5930	0.8883	0.9425
No log	69.0	207	0.7469	0.6403	0.7469	0.8642
No log	70.0	210	0.6680	0.6559	0.6680	0.8173
No log	71.0	213	0.7247	0.6398	0.7247	0.8513
No log	72.0	216	0.8347	0.6112	0.8347	0.9136
No log	73.0	219	0.7207	0.6429	0.7207	0.8490
No log	74.0	222	0.7124	0.6469	0.7124	0.8441
No log	75.0	225	0.8125	0.6181	0.8125	0.9014
No log	76.0	228	0.7210	0.6390	0.7210	0.8491
No log	77.0	231	0.7189	0.6438	0.7189	0.8479
No log	78.0	234	0.7447	0.6329	0.7447	0.8629
No log	79.0	237	0.7758	0.6251	0.7758	0.8808
No log	80.0	240	0.7085	0.6384	0.7085	0.8417
No log	81.0	243	0.7272	0.6293	0.7272	0.8528
No log	82.0	246	0.7782	0.6243	0.7782	0.8822

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