You can deploy this model and start earning money today!

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

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

Views

No views yet

rlcc-new-taste-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:

More information needed

More information needed

More information needed

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: 46
num_epochs: 25

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	F1 Micro	Precision Micro	Recall Micro	Total Tf
1.1078	1.0	47	1.0887	0.4192	0.3204	0.3280	0.4145	0.4192	0.4192	0.4192	[153, 212, 518, 212]
0.9254	2.0	94	0.9627	0.5205	0.5194	0.5239	0.5177	0.5205	0.5205	0.5205	[190, 175, 555, 175]
0.7557	3.0	141	0.9997	0.5233	0.5211	0.5319	0.5203	0.5233	0.5233	0.5233	[191, 174, 556, 174]
0.5228	4.0	188	1.0522	0.5616	0.5601	0.5622	0.5583	0.5616	0.5616	0.5616	[205, 160, 570, 160]
0.3544	5.0	235	1.2115	0.5589	0.5538	0.5624	0.5566	0.5589	0.5589	0.5589	[204, 161, 569, 161]
0.3223	6.0	282	1.3242	0.5534	0.5569	0.5665	0.5517	0.5534	0.5534	0.5534	[202, 163, 567, 163]
0.2549	7.0	329	1.3983	0.5534	0.5569	0.5662	0.5513	0.5534	0.5534	0.5534	[202, 163, 567, 163]
0.176	8.0	376	1.4543	0.5616	0.5590	0.5613	0.5591	0.5616	0.5616	0.5616	[205, 160, 570, 160]
0.1445	9.0	423	1.5338	0.5699	0.5670	0.5715	0.5673	0.5699	0.5699	0.5699	[208, 157, 573, 157]
0.1119	10.0	470	1.6388	0.5562	0.5608	0.5728	0.5545	0.5562	0.5562	0.5562	[203, 162, 568, 162]
0.1004	11.0	517	1.6842	0.5507	0.5573	0.5750	0.5501	0.5507	0.5507	0.5507	[201, 164, 566, 164]
0.0786	12.0	564	1.8593	0.5479	0.5538	0.5688	0.5466	0.5479	0.5479	0.5479	[200, 165, 565, 165]
0.0663	13.0	611	1.8557	0.5616	0.5649	0.5754	0.5593	0.5616	0.5616	0.5616	[205, 160, 570, 160]
0.0519	14.0	658	1.9674	0.5425	0.5476	0.5626	0.5411	0.5425	0.5425	0.5425	[198, 167, 563, 167]