canine-mouse-enhancers

This model is a fine-tuned version of google/canine-c on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.9641
• Accuracy: 0.7727

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-06
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	242	0.6476	0.6281
No log	2.0	484	0.6080	0.6860
0.6372	3.0	726	0.5989	0.7231
0.6372	4.0	968	0.6285	0.6694
0.5955	5.0	1210	0.5904	0.6860
0.5955	6.0	1452	0.5782	0.7107
0.5812	7.0	1694	0.5845	0.6983
0.5812	8.0	1936	0.6186	0.6983
0.5901	9.0	2178	0.5814	0.7231
0.5901	10.0	2420	0.6152	0.7355
0.5535	11.0	2662	0.5556	0.7438
0.5535	12.0	2904	0.5476	0.7479
0.5566	13.0	3146	0.6583	0.7107
0.5566	14.0	3388	0.5571	0.7521
0.5419	15.0	3630	0.6231	0.7231
0.5419	16.0	3872	0.6068	0.7603
0.546	17.0	4114	0.6581	0.7273
0.546	18.0	4356	0.6350	0.7438
0.5359	19.0	4598	0.7081	0.7438
0.5359	20.0	4840	0.6711	0.7521
0.5262	21.0	5082	0.8095	0.7190
0.5262	22.0	5324	0.7282	0.7521
0.5666	23.0	5566	0.7604	0.7479
0.5666	24.0	5808	0.8097	0.7521
0.5456	25.0	6050	0.8513	0.7521
0.5456	26.0	6292	0.7954	0.7603
0.5612	27.0	6534	0.8435	0.7521
0.5612	28.0	6776	0.9000	0.7355
0.5358	29.0	7018	0.9241	0.7603
0.5358	30.0	7260	0.9005	0.7479
0.5434	31.0	7502	0.8875	0.7645
0.5434	32.0	7744	0.8878	0.7686
0.5434	33.0	7986	0.9162	0.7645
0.5066	34.0	8228	0.8665	0.7686
0.5066	35.0	8470	0.8756	0.7686
0.5276	36.0	8712	0.9723	0.7603
0.5276	37.0	8954	1.0044	0.7521
0.4916	38.0	9196	0.9647	0.7521
0.4916	39.0	9438	0.9819	0.7603
0.4865	40.0	9680	0.9644	0.7686
0.4865	41.0	9922	0.9084	0.7851
0.4505	42.0	10164	1.0152	0.7521
0.4505	43.0	10406	0.9332	0.7769
0.4798	44.0	10648	0.9803	0.7603
0.4798	45.0	10890	1.0211	0.7521
0.4234	46.0	11132	0.9143	0.7810
0.4234	47.0	11374	0.9969	0.7645
0.4269	48.0	11616	0.9515	0.7851
0.4269	49.0	11858	0.9998	0.7686
0.4135	50.0	12100	0.9641	0.7727

Framework versions

• Transformers 4.26.1
• Pytorch 2.0.0+cu117
• Datasets 2.19.0
• Tokenizers 0.13.3