rlcc-taste-sentences-concat

trungpq/rlcc-taste-sentences-concat

rlcc-taste-sentences-concat is a machine learning model from trungpq. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
Model type: bert_classifier
Access: Public
Created: May 18, 2025
Updated: May 18, 2025
SHA: 9c4280d0

Model card

From the Hugging Face model README

rlcc-taste-sentences-concat

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.0363
Accuracy: 0.5268
F1 Macro: 0.4636
Precision Macro: 0.5476
Recall Macro: 0.4513
Total Tf: [216, 194, 1036, 194]

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 OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 92
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	Total Tf
1.1727	1.0	93	1.2539	0.3683	0.2353	0.2889	0.3153	[151, 259, 971, 259]
0.9438	2.0	186	1.1557	0.4707	0.3431	0.3681	0.4003	[193, 217, 1013, 217]
0.7558	3.0	279	1.1786	0.5049	0.3872	0.3848	0.4193	[207, 203, 1027, 203]
0.5583	4.0	372	1.3389	0.4878	0.3754	0.3717	0.4043	[200, 210, 1020, 210]
0.4408	5.0	465	1.4745	0.4780	0.3686	0.3658	0.3979	[196, 214, 1016, 214]
0.3553	6.0	558	1.4768	0.5317	0.4111	0.3985	0.4277	[218, 192, 1038, 192]
0.2992	7.0	651	1.5794	0.5220	0.4022	0.3936	0.4150	[214, 196, 1034, 196]
0.2443	8.0	744	1.6612	0.5366	0.4148	0.4044	0.4316	[220, 190, 1040, 190]
0.1993	9.0	837	1.7750	0.5220	0.4472	0.6406	0.4427	[214, 196, 1034, 196]
0.1841	10.0	930	1.7578	0.5268	0.4468	0.5663	0.4344	[216, 194, 1036, 194]
0.1506	11.0	1023	1.8948	0.5268	0.4623	0.5250	0.4504	[216, 194, 1036, 194]
0.1283	12.0	1116	1.9670	0.5244	0.4612	0.5472	0.4491	[215, 195, 1035, 195]
0.1472	13.0	1209	2.0294	0.5195	0.4598	0.5793	0.4463	[213, 197, 1033, 197]
0.1298	14.0	1302	2.0262	0.5220	0.4571	0.5206	0.4456	[214, 196, 1034, 196]
0.0865	15.0	1395	2.0363	0.5268	0.4636	0.5476	0.4513	[216, 194, 1036, 194]

Framework versions

Transformers 4.51.1
Pytorch 2.8.0.dev20250409+cu128
Datasets 3.5.0
Tokenizers 0.21.1

Library: transformers
Type: bert_classifier
Created: May 18, 2025
Updated: May 18, 2025