learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
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: 500
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
4.2448	0.0267	500	0.3176	0.8796	0.9528	0.8556	0.9016
2.4191	0.0533	1000	0.2767	0.9138	0.9271	0.9403	0.9336
1.9636	0.08	1500	0.2391	0.9142	0.9607	0.9039	0.9314
1.7834	0.1067	2000	0.2148	0.9254	0.9643	0.9183	0.9407
1.675	0.1333	2500	0.2138	0.9292	0.9560	0.9332	0.9444
1.7663	0.16	3000	0.2685	0.9177	0.9747	0.8956	0.9335
1.7593	0.1867	3500	0.2410	0.9254	0.9678	0.9147	0.9405
1.7727	0.2133	4000	0.1939	0.9354	0.9592	0.9397	0.9494
1.6498	0.24	4500	0.2019	0.9358	0.9564	0.9433	0.9498
1.5754	0.2667	5000	0.2334	0.9377	0.9577	0.9451	0.9514
1.6312	0.2933	5500	0.2120	0.935	0.9671	0.9308	0.9486
1.5515	0.32	6000	0.2342	0.9354	0.9637	0.9350	0.9491
1.6374	0.3467	6500	0.2143	0.9385	0.9555	0.9487	0.9521
1.6782	0.3733	7000	0.1865	0.9373	0.9599	0.9421	0.9509
1.614	0.4	7500	0.2039	0.9404	0.9562	0.9511	0.9536
1.5568	0.4267	8000	0.1862	0.9423	0.9641	0.9457	0.9548
1.5774	0.4533	8500	0.1818	0.94	0.9634	0.9427	0.9530
1.5722	0.48	9000	0.2388	0.9396	0.9628	0.9427	0.9527
1.5544	0.5067	9500	0.2009	0.9408	0.9635	0.9439	0.9536
1.5426	0.5333	10000	0.2398	0.9385	0.9662	0.9374	0.9515
1.5144	0.56	10500	0.2223	0.9381	0.9662	0.9368	0.9512
1.508	0.5867	11000	0.2135	0.9446	0.9517	0.9630	0.9573
1.5881	0.6133	11500	0.1886	0.9404	0.9546	0.9529	0.9537
1.4951	0.64	12000	0.2053	0.9442	0.9671	0.9457	0.9563
1.583	0.6667	12500	0.2088	0.9412	0.9663	0.9415	0.9538
1.5312	0.6933	13000	0.2041	0.9373	0.9719	0.9296	0.9503
1.5474	0.72	13500	0.1907	0.9412	0.9663	0.9415	0.9538
1.4928	0.7467	14000	0.1998	0.9438	0.9631	0.9493	0.9561
1.5224	0.7733	14500	0.1940	0.9423	0.9591	0.9511	0.9551
1.5267	0.8	15000	0.2095	0.9442	0.9665	0.9463	0.9563
1.6073	0.8267	15500	0.1905	0.945	0.9620	0.9523	0.9571
1.4924	0.8533	16000	0.2118	0.9462	0.9666	0.9493	0.9579
1.543	0.88	16500	0.2074	0.9442	0.9603	0.9529	0.9566
1.6774	0.9067	17000	0.2044	0.9446	0.9631	0.9505	0.9568
1.5077	0.9333	17500	0.2007	0.945	0.9626	0.9517	0.9571
1.4738	0.96	18000	0.2018	0.9442	0.9603	0.9529	0.9566
1.4543	0.9867	18500	0.2045	0.9442	0.9592	0.9541	0.9566

Framework versions

Transformers 4.46.0
Pytorch 2.4.1+cu121
Datasets 3.0.2
Tokenizers 0.20.1

tom-010/judge_answer___33_deberta_base_enwiki-answerability-2411

judge_answer___33_deberta_base_enwiki-answerability-2411

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tom-010/
judge_answer___33_deberta_base_enwiki-answerability-2411