Classifier_with_external_sets_05

This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2840
• Accuracy: 0.9627

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-06
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.9983	289	0.6833	0.7547
0.403	2.0	579	0.4286	0.7700
0.403	2.9983	868	0.5718	0.8196
0.1978	4.0	1158	0.3336	0.8813
0.1978	4.9983	1447	0.3455	0.8795
0.1523	6.0	1737	0.5141	0.8398
0.1371	6.9983	2026	0.2422	0.9291
0.1371	8.0	2316	0.1653	0.9486
0.1073	8.9983	2605	0.1606	0.9480
0.1073	10.0	2895	0.3522	0.8991
0.0966	10.9983	3184	0.2096	0.9309
0.0966	12.0	3474	0.1263	0.9664
0.0887	12.9983	3763	0.2030	0.9529
0.0935	14.0	4053	0.1045	0.9676
0.0935	14.9983	4342	0.1270	0.9664
0.0751	16.0	4632	0.1873	0.9596
0.0751	16.9983	4921	0.2181	0.9621
0.0644	18.0	5211	0.1207	0.9713
0.0589	18.9983	5500	0.3134	0.9315
0.0589	20.0	5790	0.2447	0.9505
0.0451	20.9983	6079	0.2650	0.9474
0.0451	22.0	6369	0.2205	0.9596
0.0414	22.9983	6658	0.1899	0.9657
0.0414	24.0	6948	0.2518	0.9590
0.0415	24.9983	7237	0.2175	0.9572
0.0358	26.0	7527	0.3080	0.9462
0.0358	26.9983	7816	0.2570	0.9474
0.0332	28.0	8106	0.2519	0.9554
0.0332	28.9983	8395	0.3117	0.9492
0.028	30.0	8685	0.3270	0.9517
0.028	30.9983	8974	0.2641	0.9602
0.0281	32.0	9264	0.2669	0.9615
0.0227	32.9983	9553	0.2558	0.9615
0.0227	34.0	9843	0.3255	0.9505
0.0218	34.9983	10132	0.3818	0.9431
0.0218	36.0	10422	0.2411	0.9657
0.0224	36.9983	10711	0.2391	0.9645
0.0201	38.0	11001	0.3097	0.9602
0.0201	38.9983	11290	0.3057	0.9590
0.0168	40.0	11580	0.2537	0.9621
0.0168	40.9983	11869	0.2661	0.9615
0.0171	42.0	12159	0.3151	0.9590
0.0171	42.9983	12448	0.2814	0.9621
0.0176	44.0	12738	0.2748	0.9633
0.0153	44.9983	13027	0.2950	0.9633
0.0153	46.0	13317	0.3171	0.9596
0.0133	46.9983	13606	0.2659	0.9633
0.0133	48.0	13896	0.3022	0.9633
0.0142	48.9983	14185	0.3028	0.9609
0.0142	49.9136	14450	0.2840	0.9627

Framework versions

• Transformers 4.40.0
• Pytorch 2.2.2+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1