mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.1

This model is a fine-tuned version of asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.0 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5335
• F1 Macro: 0.8675
• F1 Micro: 0.8692
• Accuracy Balanced: 0.8674
• Accuracy: 0.8692
• Precision Macro: 0.8677
• Recall Macro: 0.8674
• Precision Micro: 0.8692
• Recall Micro: 0.8692

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 128
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.06
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Micro	Accuracy Balanced	Accuracy	Precision Macro	Recall Macro	Precision Micro	Recall Micro
0.1975	0.17	200	0.3474	0.8688	0.8708	0.8678	0.8708	0.8701	0.8678	0.8708	0.8708
0.1974	0.34	400	0.3580	0.8600	0.8624	0.8585	0.8624	0.8621	0.8585	0.8624	0.8624
0.2054	0.51	600	0.3616	0.8520	0.8565	0.8476	0.8565	0.8638	0.8476	0.8565	0.8565
0.2094	0.68	800	0.3772	0.8658	0.8687	0.8630	0.8687	0.8710	0.8630	0.8687	0.8687
0.2118	0.85	1000	0.3701	0.8729	0.8740	0.8747	0.8740	0.8719	0.8747	0.8740	0.8740
0.1948	1.02	1200	0.3778	0.8698	0.8714	0.8702	0.8714	0.8696	0.8702	0.8714	0.8714
0.1447	1.19	1400	0.3964	0.8666	0.8692	0.8642	0.8692	0.8706	0.8642	0.8692	0.8692
0.1723	1.35	1600	0.3855	0.8718	0.8735	0.8716	0.8735	0.8720	0.8716	0.8735	0.8735
0.1476	1.52	1800	0.4164	0.8637	0.8661	0.8620	0.8661	0.8661	0.8620	0.8661	0.8661
0.1515	1.69	2000	0.3958	0.8724	0.8740	0.8725	0.8740	0.8724	0.8725	0.8740	0.8740
0.1378	1.86	2200	0.4390	0.8694	0.8708	0.8699	0.8708	0.8689	0.8699	0.8708	0.8708
0.1332	2.03	2400	0.4535	0.8732	0.8745	0.8740	0.8745	0.8726	0.8740	0.8745	0.8745
0.0913	2.2	2600	0.5235	0.8638	0.8661	0.8625	0.8661	0.8656	0.8625	0.8661	0.8661
0.1076	2.37	2800	0.5339	0.8638	0.8661	0.8623	0.8661	0.8659	0.8623	0.8661	0.8661
0.09	2.54	3000	0.5388	0.8670	0.8687	0.8667	0.8687	0.8672	0.8667	0.8687	0.8687
0.0928	2.71	3200	0.5266	0.8649	0.8666	0.8648	0.8666	0.8650	0.8648	0.8666	0.8666
0.0805	2.88	3400	0.5433	0.8658	0.8677	0.8654	0.8677	0.8663	0.8654	0.8677	0.8677

Eval results

Datasets	asadfgglie/nli-zh-tw-all/test	asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test	eval_dataset	test_dataset
eval_loss	0.576	0.165	0.584	0.523
eval_f1_macro	0.869	0.945	0.868	0.878
eval_f1_micro	0.87	0.945	0.87	0.879
eval_accuracy_balanced	0.868	0.945	0.867	0.878
eval_accuracy	0.87	0.945	0.87	0.879
eval_precision_macro	0.87	0.945	0.868	0.88
eval_recall_macro	0.868	0.945	0.867	0.878
eval_precision_micro	0.87	0.945	0.87	0.879
eval_recall_micro	0.87	0.945	0.87	0.879
eval_runtime	229.83	4.05	51.2	203.627
eval_samples_per_second	36.984	233.57	36.894	37.112
eval_steps_per_second	0.292	1.975	0.293	0.295
Size of dataset	8500	946	1889	7557

Framework versions

• Transformers 4.33.3
• Pytorch 2.5.1+cu121
• Datasets 2.14.7
• Tokenizers 0.13.3