Mistral-7B-v0.1_mbe_positive

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the mbe dataset. It achieves the following results on the evaluation set:

• Loss: 1.0233
• Accuracy: 0.6809

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: 3e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.3217	0.07	10	0.7263	0.4901
0.56	0.13	20	0.6898	0.5526
0.5281	0.2	30	0.6465	0.5888
0.994	0.27	40	0.7351	0.5987
0.4785	0.33	50	0.6004	0.6118
0.4732	0.4	60	0.5783	0.6349
0.4466	0.47	70	0.5714	0.6414
0.8737	0.53	80	0.5673	0.6184
0.4471	0.6	90	0.5631	0.6283
0.46	0.67	100	0.5504	0.6349
0.3294	0.73	110	0.6010	0.625
0.6526	0.8	120	0.5731	0.6283
0.3712	0.87	130	0.5379	0.6447
0.3341	0.93	140	0.5409	0.6283
0.552	1.0	150	0.5311	0.6382
0.4681	1.07	160	0.5371	0.6414
0.3119	1.14	170	0.6172	0.6283
0.3082	1.2	180	0.5361	0.6513
0.5217	1.27	190	0.5468	0.625
0.3888	1.34	200	0.5891	0.6316
0.2841	1.4	210	0.5429	0.6283
0.2728	1.47	220	0.5247	0.6382
0.5563	1.54	230	0.5004	0.6513
0.2862	1.6	240	0.4741	0.6546
0.2289	1.67	250	0.5441	0.6513
0.2481	1.74	260	0.5171	0.6513
0.329	1.8	270	0.5371	0.6546
0.1741	1.87	280	0.5412	0.6678
0.2888	1.94	290	0.5131	0.6711
0.4157	2.0	300	0.4555	0.6447
0.1982	2.07	310	0.5670	0.6612
0.106	2.14	320	0.7943	0.6678
0.1718	2.2	330	0.7496	0.6645
0.214	2.27	340	0.6264	0.6842
0.1571	2.34	350	0.6139	0.6316
0.1432	2.4	360	0.6199	0.6842
0.1038	2.47	370	0.6368	0.6974
0.1728	2.54	380	0.7889	0.6678
0.14	2.6	390	0.7952	0.6546
0.1522	2.67	400	0.7745	0.6579
0.1345	2.74	410	0.7231	0.6513
0.1587	2.8	420	0.7154	0.6480
0.1391	2.87	430	0.6923	0.6513
0.129	2.94	440	0.6484	0.6711
0.2092	3.01	450	0.5822	0.6743
0.015	3.07	460	1.1217	0.6579
0.051	3.14	470	1.5790	0.6480
0.0999	3.21	480	1.5168	0.6678
0.1776	3.27	490	1.2342	0.6875
0.0612	3.34	500	1.0371	0.6974
0.0858	3.41	510	1.0277	0.6776
0.0316	3.47	520	1.0387	0.6809
0.1899	3.54	530	0.8185	0.6908
0.1517	3.61	540	0.7054	0.6842
0.0324	3.67	550	0.8505	0.6842
0.0646	3.74	560	1.0057	0.6612
0.1038	3.81	570	1.0027	0.6645
0.0844	3.87	580	0.9926	0.6513
0.0986	3.94	590	0.9246	0.6579
0.0627	4.01	600	0.8539	0.6546
0.0513	4.07	610	0.9247	0.6513
0.0484	4.14	620	1.1128	0.6546
0.0244	4.21	630	1.2702	0.6480
0.0672	4.27	640	1.7169	0.6414
0.0824	4.34	650	1.6627	0.6414
0.0068	4.41	660	1.3425	0.6349
0.044	4.47	670	1.2208	0.6612
0.0378	4.54	680	1.2891	0.6447
0.0411	4.61	690	1.3528	0.6612
0.0215	4.67	700	1.2606	0.6678
0.0438	4.74	710	1.2515	0.6546
0.0936	4.81	720	1.0858	0.6645
0.0305	4.87	730	0.9839	0.6579
0.0282	4.94	740	1.0233	0.6809

Framework versions

• PEFT 0.7.1
• Transformers 4.37.2
• Pytorch 2.1.2+cu121
• Datasets 2.17.1
• Tokenizers 0.15.1