Metrics Upon Eval with max_length = 512

loss: 2.4489
accuracy: 0.7250
precision: 0.7150
recall: 0.7250
precision_macro: 0.6583
recall_macro: 0.6262
macro_fpr: 0.0278
weighted_fpr: 0.0264
weighted_specificity: 0.9597
macro_specificity: 0.9790
weighted_sensitivity: 0.7250
macro_sensitivity: 0.6262
f1_micro: 0.7250
f1_macro: 0.6317
f1_weighted: 0.7155
runtime: 27.7396
samples_per_second: 46.5400
steps_per_second: 5.8400

tiny-mistral

This model is a fine-tuned version of openaccess-ai-collective/tiny-mistral on an unknown dataset. It achieves the following results on the evaluation set (at last epoch):

Loss: 2.5607
Accuracy: 0.7126
Precision: 0.7033
Recall: 0.7126
Precision Macro: 0.6443
Recall Macro: 0.5942
Macro Fpr: 0.0292
Weighted Fpr: 0.0282
Weighted Specificity: 0.9577
Macro Specificity: 0.9779
Weighted Sensitivity: 0.7111
Macro Sensitivity: 0.5942
F1 Micro: 0.7111
F1 Macro: 0.6107
F1 Weighted: 0.7086

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	Precision Macro	Recall Macro	Macro Fpr	Weighted Fpr	Weighted Specificity	Macro Specificity	Weighted Sensitivity	Macro Sensitivity	F1 Micro	F1 Macro	F1 Weighted
1.4479	1.0	643	1.1182	0.6499	0.6258	0.6499	0.4712	0.4744	0.0390	0.0371	0.9470	0.9731	0.6499	0.4744	0.6499	0.4547	0.6214
0.8133	2.0	1286	1.0854	0.6987	0.7197	0.6987	0.5877	0.5528	0.0305	0.0299	0.9608	0.9773	0.6987	0.5528	0.6987	0.5474	0.6970
0.5592	3.0	1929	1.6114	0.6987	0.7107	0.6987	0.6368	0.5881	0.0304	0.0299	0.9609	0.9773	0.6987	0.5881	0.6987	0.6013	0.6998
0.2375	4.0	2572	1.7779	0.6956	0.7001	0.6956	0.5840	0.5667	0.0310	0.0303	0.9566	0.9768	0.6956	0.5667	0.6956	0.5699	0.6923
0.1586	5.0	3215	2.1752	0.6948	0.7011	0.6948	0.5797	0.5799	0.0316	0.0304	0.9601	0.9770	0.6948	0.5799	0.6948	0.5695	0.6917
0.0956	6.0	3858	2.3261	0.7080	0.7213	0.7080	0.6169	0.6191	0.0291	0.0286	0.9646	0.9782	0.7080	0.6191	0.7080	0.6115	0.7105
0.044	7.0	4501	2.3308	0.7157	0.7143	0.7157	0.6184	0.5939	0.0285	0.0276	0.9611	0.9785	0.7157	0.5939	0.7157	0.6014	0.7131
0.0212	8.0	5144	2.5607	0.7126	0.7033	0.7126	0.6494	0.6175	0.0294	0.0280	0.9581	0.9780	0.7126	0.6175	0.7126	0.6237	0.7047
0.0183	9.0	5787	2.6405	0.7119	0.7092	0.7119	0.6133	0.5850	0.0291	0.0281	0.9599	0.9781	0.7119	0.5850	0.7119	0.5935	0.7088
0.0145	10.0	6430	2.7268	0.7088	0.7058	0.7088	0.6235	0.5945	0.0297	0.0285	0.9574	0.9777	0.7088	0.5945	0.7088	0.6039	0.7051
0.0065	11.0	7073	2.7568	0.7149	0.7133	0.7149	0.6342	0.5966	0.0286	0.0277	0.9609	0.9784	0.7149	0.5966	0.7149	0.6068	0.7123
0.0012	12.0	7716	2.9243	0.7088	0.7106	0.7088	0.6261	0.5886	0.0296	0.0285	0.9581	0.9778	0.7088	0.5886	0.7088	0.6011	0.7071
0.0019	13.0	8359	2.9101	0.7119	0.7107	0.7119	0.6399	0.5910	0.0291	0.0281	0.9576	0.9780	0.7119	0.5910	0.7119	0.6073	0.7085
0.0011	14.0	9002	2.9270	0.7103	0.7101	0.7103	0.6430	0.5925	0.0293	0.0283	0.9576	0.9779	0.7103	0.5925	0.7103	0.6090	0.7077
0.0008	15.0	9645	2.9390	0.7111	0.7110	0.7111	0.6443	0.5942	0.0292	0.0282	0.9577	0.9779	0.7111	0.5942	0.7111	0.6107	0.7086

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

xshubhamx
/

tiny-mistral

Metrics Upon Eval with max_length = 512

tiny-mistral

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for xshubhamx/tiny-mistral

Evaluation results