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trungpq/
rlcc-new-appearance-upsample_replacement-absa-min

Name: rlcc-new-appearance-upsample_replacement-absa-min
Availability: InStock

transformers

safetensors

bert_with_absa

generated_from_trainer

endpoints_compatible

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rlcc-new-appearance-upsample_replacement-absa-min

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

Loss: 1.8481
Accuracy: 0.6245
F1 Macro: 0.6223
Precision Macro: 0.6689
Recall Macro: 0.6097
F1 Micro: 0.6245
Precision Micro: 0.6245
Recall Micro: 0.6245
Total Tf: [173, 104, 450, 104]

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: 44
num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	F1 Micro	Precision Micro	Recall Micro	Total Tf
1.1469	1.0	45	1.1054	0.3177	0.2676	0.4053	0.3210	0.3177	0.3177	0.3177	[88, 189, 365, 189]
0.96	2.0	90	1.0237	0.4621	0.3943	0.4744	0.5120	0.4621	0.4621	0.4621	[128, 149, 405, 149]
0.7704	3.0	135	0.9339	0.5740	0.5682	0.5905	0.5990	0.5740	0.5740	0.5740	[159, 118, 436, 118]
0.5715	4.0	180	0.8803	0.5921	0.6003	0.5993	0.6019	0.5921	0.5921	0.5921	[164, 113, 441, 113]
0.3997	5.0	225	0.9415	0.6245	0.6297	0.6415	0.6229	0.6245	0.6245	0.6245	[173, 104, 450, 104]
0.2982	6.0	270	1.0422	0.6065	0.6154	0.6368	0.6109	0.6065	0.6065	0.6065	[168, 109, 445, 109]
0.2393	7.0	315	1.2277	0.5921	0.5824	0.6200	0.5877	0.5921	0.5921	0.5921	[164, 113, 441, 113]
0.1233	8.0	360	1.1651	0.6282	0.6341	0.6381	0.6309	0.6282	0.6282	0.6282	[174, 103, 451, 103]
0.097	9.0	405	1.2939	0.6318	0.6344	0.6595	0.6238	0.6318	0.6318	0.6318	[175, 102, 452, 102]
0.0809	10.0	450	1.3094	0.6498	0.6540	0.6746	0.6449	0.6498	0.6498	0.6498	[180, 97, 457, 97]
0.0856	11.0	495	1.4169	0.6318	0.6380	0.6563	0.6289	0.6318	0.6318	0.6318	[175, 102, 452, 102]
0.0693	12.0	540	1.5354	0.6282	0.6269	0.6641	0.6167	0.6282	0.6282	0.6282	[174, 103, 451, 103]
0.0807	13.0	585	1.5027	0.6462	0.6443	0.6780	0.6358	0.6462	0.6462	0.6462	[179, 98, 456, 98]
0.0444	14.0	630	1.5199	0.6643	0.6649	0.6886	0.6579	0.6643	0.6643	0.6643	[184, 93, 461, 93]
0.0363	15.0	675	1.6163	0.6209	0.6219	0.6573	0.6097	0.6209	0.6209	0.6209	[172, 105, 449, 105]
0.044	16.0	720	1.7608	0.6101	0.6073	0.6403	0.5997	0.6101	0.6101	0.6101	[169, 108, 446, 108]
0.0204	17.0	765	1.6763	0.6498	0.6492	0.6871	0.6378	0.6498	0.6498	0.6498	[180, 97, 457, 97]
0.01	18.0	810	1.7111	0.6354	0.6381	0.6695	0.6258	0.6354	0.6354	0.6354	[176, 101, 453, 101]
0.0232	19.0	855	1.8481	0.6245	0.6223	0.6689	0.6097	0.6245	0.6245	0.6245	[173, 104, 450, 104]

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

trungpq/rlcc-new-appearance-upsample_replacement-absa-min

rlcc-new-appearance-upsample_replacement-absa-min

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

trungpq/
rlcc-new-appearance-upsample_replacement-absa-min