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hcene/
results

Name: results
Availability: InStock

peft

tensorboard

safetensors

generated_from_trainer

MoritzLaurer/mDeBERTa-v3-base-mnli-xnli

adapter

mit

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results

This model is a fine-tuned version of MoritzLaurer/mDeBERTa-v3-base-mnli-xnli on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6765
Accuracy: 0.7634
Precision: 0.7675
Recall: 0.7644
F1: 0.7627
Ratio: 0.3297

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: 0.0005
train_batch_size: 20
eval_batch_size: 20
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
lr_scheduler_warmup_steps: 4
num_epochs: 20
label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Ratio
1.7741	0.17	10	1.0961	0.7061	0.7103	0.7073	0.7078	0.3262
1.2149	0.34	20	0.8783	0.7025	0.7066	0.7038	0.7044	0.3262
0.959	0.52	30	0.8413	0.6774	0.6981	0.6784	0.6854	0.2939
0.9582	0.69	40	0.7705	0.7312	0.7417	0.7321	0.7314	0.3190
0.8706	0.86	50	0.6728	0.7419	0.7545	0.7437	0.7385	0.3190
0.8804	1.03	60	0.6933	0.7133	0.7402	0.7157	0.6919	0.3190
0.8999	1.21	70	0.7167	0.7133	0.7208	0.7144	0.7158	0.3190
0.8914	1.38	80	0.6910	0.7384	0.7549	0.7390	0.7325	0.3226
0.8578	1.55	90	0.6862	0.7348	0.7533	0.7369	0.7243	0.3262
0.8755	1.72	100	0.6889	0.7240	0.7449	0.7263	0.7095	0.3262
0.8551	1.9	110	0.7090	0.7133	0.7408	0.7158	0.6899	0.3262
0.8736	2.07	120	0.7019	0.7097	0.7236	0.7120	0.6975	0.3262
0.8647	2.24	130	0.7078	0.7240	0.7354	0.7261	0.7174	0.3262
0.8755	2.41	140	0.7023	0.7527	0.7716	0.7533	0.7448	0.3262
0.858	2.59	150	0.6745	0.7384	0.7450	0.7393	0.7372	0.3262
0.8912	2.76	160	0.6842	0.7491	0.7635	0.7511	0.7424	0.3297
0.8294	2.93	170	0.6623	0.7599	0.7624	0.7609	0.7602	0.3297
0.8481	3.1	180	0.6652	0.7599	0.7715	0.7617	0.7551	0.3333
0.8488	3.28	190	0.6782	0.7312	0.7609	0.7335	0.7131	0.3297
0.8418	3.45	200	0.6884	0.7706	0.7738	0.7719	0.7720	0.3262
0.8774	3.62	210	0.7066	0.7419	0.7523	0.7438	0.7381	0.3262
0.8496	3.79	220	0.6687	0.7133	0.7214	0.7154	0.7060	0.3333
0.825	3.97	230	0.6618	0.7634	0.7833	0.7639	0.7547	0.3297
0.8933	4.14	240	0.6946	0.7419	0.7692	0.7424	0.7278	0.3262
0.8579	4.31	250	0.6795	0.7491	0.7786	0.7495	0.7353	0.3262
0.8023	4.48	260	0.6595	0.7563	0.7727	0.7569	0.7501	0.3262
0.8736	4.66	270	0.6703	0.7491	0.7558	0.7508	0.7482	0.3262
0.8291	4.83	280	0.7102	0.6989	0.7630	0.7019	0.6499	0.3262
0.8923	5.0	290	0.7004	0.7097	0.7571	0.7124	0.6756	0.3262
0.8571	5.17	300	0.6739	0.7634	0.7717	0.7642	0.7621	0.3262
0.8521	5.34	310	0.6666	0.7563	0.7710	0.7569	0.7511	0.3262
0.8369	5.52	320	0.6815	0.7455	0.7487	0.7467	0.7472	0.3262
0.7897	5.69	330	0.6731	0.7097	0.7343	0.7122	0.6871	0.3262
0.8801	5.86	340	0.6773	0.7419	0.7631	0.7441	0.7304	0.3297
0.891	6.03	350	0.7107	0.7491	0.7556	0.7509	0.7473	0.3297
0.8444	6.21	360	0.6805	0.7634	0.7879	0.7639	0.7543	0.3262
0.8375	6.38	370	0.6562	0.7599	0.7725	0.7605	0.7560	0.3262
0.8141	6.55	380	0.6578	0.7276	0.7409	0.7296	0.7217	0.3262
0.8792	6.72	390	0.6790	0.7204	0.7355	0.7226	0.7121	0.3262
0.8868	6.9	400	0.7063	0.7384	0.7411	0.7397	0.7404	0.3262
0.8767	7.07	410	0.7074	0.7240	0.7440	0.7262	0.7126	0.3262
0.8545	7.24	420	0.6725	0.7276	0.7520	0.7300	0.7108	0.3297
0.8589	7.41	430	0.6712	0.7276	0.7473	0.7299	0.7139	0.3297
0.8522	7.59	440	0.6853	0.7634	0.7655	0.7649	0.7644	0.3297
0.777	7.76	450	0.6623	0.7634	0.7714	0.7642	0.7604	0.3297
0.8903	7.93	460	0.6629	0.7599	0.7629	0.7609	0.7598	0.3297
0.8168	8.1	470	0.6714	0.7599	0.7650	0.7608	0.7584	0.3297
0.7979	8.28	480	0.6469	0.7491	0.7505	0.7505	0.7504	0.3297
0.8674	8.45	490	0.6553	0.7455	0.7603	0.7475	0.7382	0.3297
0.8475	8.62	500	0.6788	0.7563	0.7576	0.7576	0.7576	0.3297
0.8723	8.79	510	0.6862	0.7599	0.7613	0.7612	0.7611	0.3297
0.8684	8.97	520	0.6938	0.7563	0.7604	0.7579	0.7560	0.3297
0.8278	9.14	530	0.6765	0.7634	0.7675	0.7644	0.7627	0.3297

Framework versions

PEFT 0.9.0
Transformers 4.39.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2

hcene/results

results

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hcene/
results