ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold2

genki10/ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold2

ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold2 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: Feb 12, 2025
Updated: Feb 12, 2025
SHA: 0b2eaa4a

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold2

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: 0.8196
Qwk: 0.5266
Mse: 0.8188
Rmse: 0.9049

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	10.1319	-0.0008	10.1319	3.1831
No log	2.0	4	8.0767	0.0	8.0768	2.8420
No log	3.0	6	6.2533	0.0002	6.2536	2.5007
No log	4.0	8	5.0100	0.0123	5.0105	2.2384
No log	5.0	10	4.0272	0.0039	4.0276	2.0069
No log	6.0	12	3.2442	0.0	3.2446	1.8013
No log	7.0	14	2.5927	0.0	2.5932	1.6103
No log	8.0	16	2.1972	0.1308	2.1977	1.4825
No log	9.0	18	1.8141	0.0107	1.8145	1.3470
No log	10.0	20	1.5459	0.0107	1.5463	1.2435
No log	11.0	22	1.4382	0.0241	1.4386	1.1994
No log	12.0	24	1.1293	0.0174	1.1297	1.0629
No log	13.0	26	1.0117	0.0307	1.0120	1.0060
No log	14.0	28	0.9740	0.0515	0.9743	0.9871
No log	15.0	30	0.8582	0.2136	0.8585	0.9265
No log	16.0	32	0.8534	0.2515	0.8537	0.9239
No log	17.0	34	0.8895	0.4485	0.8896	0.9432
No log	18.0	36	0.8453	0.4118	0.8454	0.9194
No log	19.0	38	0.7851	0.4465	0.7851	0.8861
No log	20.0	40	0.7037	0.4585	0.7037	0.8388
No log	21.0	42	0.5963	0.4720	0.5962	0.7721
No log	22.0	44	0.5772	0.4507	0.5772	0.7597
No log	23.0	46	0.5410	0.4415	0.5411	0.7356
No log	24.0	48	0.5715	0.3668	0.5716	0.7561
No log	25.0	50	0.6022	0.3702	0.6023	0.7761
No log	26.0	52	0.5424	0.4486	0.5425	0.7365
No log	27.0	54	0.5197	0.5266	0.5194	0.7207
No log	28.0	56	0.5736	0.5439	0.5737	0.7574
No log	29.0	58	0.5610	0.5782	0.5607	0.7488
No log	30.0	60	0.5646	0.5767	0.5640	0.7510
No log	31.0	62	0.5665	0.5768	0.5660	0.7524
No log	32.0	64	0.6033	0.5605	0.6031	0.7766
No log	33.0	66	0.5731	0.5859	0.5729	0.7569
No log	34.0	68	0.5802	0.5761	0.5799	0.7615
No log	35.0	70	0.6192	0.5742	0.6187	0.7866
No log	36.0	72	0.6210	0.5638	0.6203	0.7876
No log	37.0	74	0.6256	0.5510	0.6249	0.7905
No log	38.0	76	0.6289	0.5604	0.6282	0.7926
No log	39.0	78	0.6316	0.5676	0.6309	0.7943
No log	40.0	80	0.7020	0.5620	0.7011	0.8373
No log	41.0	82	0.7725	0.5343	0.7717	0.8785
No log	42.0	84	0.6801	0.5777	0.6793	0.8242
No log	43.0	86	0.5998	0.5909	0.5994	0.7742
No log	44.0	88	0.5916	0.5965	0.5912	0.7689
No log	45.0	90	0.7575	0.5277	0.7568	0.8699
No log	46.0	92	0.8241	0.5011	0.8234	0.9074
No log	47.0	94	0.6391	0.5742	0.6386	0.7991
No log	48.0	96	0.5587	0.5643	0.5585	0.7473
No log	49.0	98	0.5695	0.5637	0.5694	0.7546
No log	50.0	100	0.6571	0.5787	0.6565	0.8103
No log	51.0	102	0.8781	0.5130	0.8772	0.9366
No log	52.0	104	0.8140	0.5373	0.8132	0.9018
No log	53.0	106	0.6467	0.5961	0.6461	0.8038
No log	54.0	108	0.6267	0.5910	0.6264	0.7914
No log	55.0	110	0.6623	0.5729	0.6618	0.8135
No log	56.0	112	0.8752	0.5326	0.8743	0.9350
No log	57.0	114	0.9951	0.4600	0.9942	0.9971
No log	58.0	116	1.0110	0.4533	1.0102	1.0051
No log	59.0	118	0.8237	0.5394	0.8228	0.9071
No log	60.0	120	0.6691	0.5595	0.6685	0.8176
No log	61.0	122	0.6400	0.5703	0.6396	0.7997
No log	62.0	124	0.6375	0.5762	0.6369	0.7981
No log	63.0	126	0.7236	0.5627	0.7229	0.8502
No log	64.0	128	0.8196	0.5266	0.8188	0.9049

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Feb 12, 2025
Updated: Feb 12, 2025