ASAP_FineTuningBERT_AugV10_k10_task1_organization_k10_k10_fold0

genki10/ASAP_FineTuningBERT_AugV10_k10_task1_organization_k10_k10_fold0

ASAP_FineTuningBERT_AugV10_k10_task1_organization_k10_k10_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. --

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Feb 13, 2025
Updated: Feb 13, 2025
SHA: 72d5dc1c

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Base models

Model card

From the Hugging Face model README

ASAP_FineTuningBERT_AugV10_k10_task1_organization_k10_k10_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: 0.7467
Qwk: 0.4691
Mse: 0.7467
Rmse: 0.8641

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	8	5.7433	0.0119	5.7433	2.3965
No log	2.0	16	2.4702	0.0872	2.4702	1.5717
No log	3.0	24	1.7093	0.0316	1.7093	1.3074
No log	4.0	32	1.4326	0.0372	1.4326	1.1969
No log	5.0	40	1.1072	0.1786	1.1072	1.0522
No log	6.0	48	0.6884	0.3132	0.6884	0.8297
No log	7.0	56	0.6065	0.3997	0.6065	0.7788
No log	8.0	64	0.7396	0.4367	0.7396	0.8600
No log	9.0	72	0.7144	0.4635	0.7144	0.8452
No log	10.0	80	0.7471	0.3873	0.7471	0.8643
No log	11.0	88	0.6928	0.4795	0.6928	0.8323
No log	12.0	96	0.6992	0.4495	0.6992	0.8362
No log	13.0	104	0.9904	0.3397	0.9904	0.9952
No log	14.0	112	0.7279	0.4738	0.7279	0.8532
No log	15.0	120	0.7754	0.4581	0.7754	0.8806
No log	16.0	128	0.7077	0.4558	0.7077	0.8413
No log	17.0	136	0.7776	0.4396	0.7776	0.8818
No log	18.0	144	0.8004	0.4365	0.8004	0.8946
No log	19.0	152	0.8639	0.4566	0.8639	0.9294
No log	20.0	160	0.8304	0.4588	0.8304	0.9113
No log	21.0	168	0.8143	0.4409	0.8143	0.9024
No log	22.0	176	0.7298	0.4486	0.7298	0.8543
No log	23.0	184	0.8025	0.4576	0.8025	0.8958
No log	24.0	192	0.7633	0.4872	0.7633	0.8737
No log	25.0	200	0.8175	0.4592	0.8175	0.9042
No log	26.0	208	0.7442	0.4612	0.7442	0.8627
No log	27.0	216	0.7625	0.4826	0.7625	0.8732
No log	28.0	224	0.8283	0.4653	0.8283	0.9101
No log	29.0	232	0.7579	0.4624	0.7579	0.8706
No log	30.0	240	0.6886	0.4796	0.6886	0.8298
No log	31.0	248	0.7358	0.4544	0.7358	0.8578
No log	32.0	256	0.7689	0.4631	0.7689	0.8769
No log	33.0	264	0.7026	0.4872	0.7026	0.8382
No log	34.0	272	0.7539	0.4685	0.7539	0.8683
No log	35.0	280	0.7366	0.4980	0.7366	0.8583
No log	36.0	288	0.8032	0.4717	0.8032	0.8962
No log	37.0	296	0.7499	0.4507	0.7499	0.8660
No log	38.0	304	0.7926	0.4938	0.7926	0.8903
No log	39.0	312	0.7451	0.4904	0.7451	0.8632
No log	40.0	320	0.7989	0.4751	0.7989	0.8938
No log	41.0	328	0.9449	0.4312	0.9449	0.9721
No log	42.0	336	0.7799	0.4678	0.7799	0.8831
No log	43.0	344	0.7435	0.4634	0.7435	0.8622
No log	44.0	352	0.7565	0.4563	0.7565	0.8697
No log	45.0	360	0.7666	0.4599	0.7666	0.8755
No log	46.0	368	0.7649	0.4866	0.7649	0.8746
No log	47.0	376	0.7609	0.4829	0.7609	0.8723
No log	48.0	384	0.7402	0.4709	0.7402	0.8604
No log	49.0	392	0.8251	0.4570	0.8251	0.9083
No log	50.0	400	0.7632	0.4480	0.7632	0.8736
No log	51.0	408	0.7331	0.4639	0.7331	0.8562
No log	52.0	416	0.7366	0.4660	0.7366	0.8582
No log	53.0	424	0.7523	0.4581	0.7523	0.8673
No log	54.0	432	0.7814	0.4564	0.7814	0.8839
No log	55.0	440	0.7467	0.4691	0.7467	0.8641

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 13, 2025
Updated: Feb 13, 2025