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61347023S/
mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.2

Name: mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.2
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mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.2

This model is a fine-tuned version of 61347023S/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.1 on the None dataset. It achieves the following results on the evaluation set:

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Datasets	asadfgglie/nli-zh-tw-all/test	asadfgglie/BanBan_2024-10-17-facial_expressions-nli	eval_dataset
Accuracy	0.875	0.953	0.871
Inference text/sec (RTX4090ti, batch=128)	126.0	829.0	125.0

The following hyperparameters were used during training:

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Micro	Accuracy Balanced	Accuracy	Precision Macro	Recall Macro	Precision Micro	Recall Micro
0.0962	0.17	200	0.5626	0.8709	0.8719	0.8714	0.8719	0.8704	0.8714	0.8719	0.8719
0.1218	0.34	400	0.5258	0.8635	0.8650	0.8629	0.8650	0.8643	0.8629	0.8650	0.8650
0.1246	0.51	600	0.4964	0.8652	0.8671	0.8635	0.8671	0.8679	0.8635	0.8671	0.8671
0.1225	0.68	800	0.5676	0.8618	0.8629	0.8623	0.8629	0.8614	0.8623	0.8629	0.8629
0.1429	0.85	1000	0.4402	0.8651	0.8666	0.8643	0.8666	0.8660	0.8643	0.8666	0.8666
0.1129	1.02	1200	0.5230	0.8688	0.8703	0.8679	0.8703	0.8699	0.8679	0.8703	0.8703
0.0921	1.19	1400	0.6435	0.8503	0.8534	0.8473	0.8534	0.8574	0.8473	0.8534	0.8534
0.0972	1.35	1600	0.5313	0.8635	0.8650	0.8628	0.8650	0.8644	0.8628	0.8650	0.8650
0.0883	1.52	1800	0.6088	0.8682	0.8692	0.8688	0.8692	0.8678	0.8688	0.8692	0.8692
0.0985	1.69	2000	0.5890	0.8696	0.8708	0.8693	0.8708	0.8698	0.8693	0.8708	0.8708
0.0838	1.86	2200	0.6647	0.8634	0.8650	0.8626	0.8650	0.8645	0.8626	0.8650	0.8650
0.0703	2.03	2400	0.6527	0.8712	0.8729	0.8699	0.8729	0.8732	0.8699	0.8729	0.8729
0.0639	2.2	2600	0.6665	0.8695	0.8714	0.8680	0.8714	0.8720	0.8680	0.8714	0.8714
0.059	2.37	2800	0.7361	0.8668	0.8687	0.8650	0.8687	0.8696	0.8650	0.8687	0.8687
0.062	2.54	3000	0.6719	0.8742	0.8756	0.8735	0.8756	0.8751	0.8735	0.8756	0.8756
0.0419	2.71	3200	0.7057	0.8734	0.8751	0.8722	0.8751	0.8753	0.8722	0.8751	0.8751
0.0539	2.88	3400	0.7020	0.8728	0.8745	0.8713	0.8745	0.8751	0.8713	0.8745	0.8745