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asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.1-seed20241201

sourceHugging Faceupdated 1y agoView on Hugging Face

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mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.1-seed20241201

This model use same hyper-parameter with asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.1, except RANDOM_SEED.

Original version use RANDOM_SEED=42, this version use RANDOM_SEED=20241201.

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: 0.6134
—F1 Macro: 0.8616
—F1 Micro: 0.8634
—Accuracy Balanced: 0.8616
—Accuracy: 0.8634
—Precision Macro: 0.8616
—Recall Macro: 0.8616
—Precision Micro: 0.8634
—Recall Micro: 0.8634

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-05
—trainbatchsize: 16
—evalbatchsize: 128
—seed: 20241201
—gradientaccumulationsteps: 2
—totaltrainbatch_size: 32
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_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
0.2034	0.17	200	0.4241	0.8481	0.8518	0.8451	0.8518	0.8541	0.8451	0.8518	0.8518
0.219	0.34	400	0.4178	0.8608	0.8624	0.8615	0.8624	0.8602	0.8615	0.8624	0.8624
0.2142	0.51	600	0.3810	0.8572	0.8602	0.8548	0.8602	0.8613	0.8548	0.8602	0.8602
0.199	0.68	800	0.4314	0.8537	0.8571	0.8508	0.8571	0.8590	0.8508	0.8571	0.8571
0.2005	0.85	1000	0.4282	0.8572	0.8602	0.8547	0.8602	0.8615	0.8547	0.8602	0.8602
0.1846	1.02	1200	0.4631	0.8691	0.8703	0.8707	0.8703	0.8681	0.8707	0.8703	0.8703
0.154	1.19	1400	0.4922	0.8599	0.8613	0.8610	0.8613	0.8590	0.8610	0.8613	0.8613
0.1432	1.35	1600	0.5020	0.8540	0.8560	0.8540	0.8560	0.8541	0.8540	0.8560	0.8560
0.1335	1.52	1800	0.5313	0.8479	0.8507	0.8461	0.8507	0.8505	0.8461	0.8507	0.8507
0.1373	1.69	2000	0.5018	0.8546	0.8571	0.8533	0.8571	0.8563	0.8533	0.8571	0.8571
0.128	1.86	2200	0.4896	0.8644	0.8655	0.8665	0.8655	0.8633	0.8665	0.8655	0.8655
0.1257	2.03	2400	0.4922	0.8648	0.8666	0.8648	0.8666	0.8648	0.8648	0.8666	0.8666
0.0959	2.2	2600	0.5814	0.8589	0.8613	0.8576	0.8613	0.8606	0.8576	0.8613	0.8613
0.0918	2.37	2800	0.5987	0.8617	0.8634	0.8618	0.8634	0.8615	0.8618	0.8634	0.8634
0.0992	2.54	3000	0.6117	0.8631	0.8650	0.8629	0.8650	0.8634	0.8629	0.8650	0.8650
0.0897	2.71	3200	0.6191	0.8583	0.8602	0.8583	0.8602	0.8584	0.8583	0.8602	0.8602
0.1065	2.88	3400	0.6221	0.8625	0.8645	0.8619	0.8645	0.8631	0.8619	0.8645	0.8645

Eval result

Datasets	asadfgglie/nli-zh-tw-all/test	asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test	eval_dataset	test_dataset
eval_loss	0.575	0.356	0.613	0.538
evalf1macro	0.87	0.896	0.862	0.875
evalf1micro	0.871	0.896	0.863	0.876
evalaccuracybalanced	0.869	0.896	0.862	0.875
eval_accuracy	0.871	0.896	0.863	0.876
evalprecisionmacro	0.871	0.898	0.862	0.877
evalrecallmacro	0.869	0.896	0.862	0.875
evalprecisionmicro	0.871	0.896	0.863	0.876
evalrecallmicro	0.871	0.896	0.863	0.876
eval_runtime	229.732	4.248	51.374	204.44
evalsamplesper_second	37.0	222.68	36.769	36.964
evalstepsper_second	0.292	1.883	0.292	0.293
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