gut_1024-finetuned-lora-bert-base-t2t-multi

This model is a fine-tuned version of AIRI-Institute/gena-lm-bert-base-t2t-multi on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4764
• F1: 0.8478
• Mcc Score: 0.5903
• Accuracy: 0.8049

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: 8
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Mcc Score	Accuracy
0.7012	0.02	100	0.6683	0.7478	0.0	0.5971
0.7003	0.04	200	0.6391	0.7825	0.3306	0.6710
0.6583	0.05	300	0.6211	0.7853	0.3430	0.6778
0.6381	0.07	400	0.6512	0.7812	0.3247	0.6681
0.6438	0.09	500	0.6524	0.3380	0.1874	0.5004
0.6028	0.11	600	0.5646	0.8004	0.5013	0.7606
0.5154	0.12	700	0.5437	0.8392	0.5576	0.7884
0.5226	0.14	800	0.4823	0.8503	0.5901	0.8024
0.5104	0.16	900	0.4856	0.8452	0.5851	0.8028
0.5538	0.18	1000	0.4764	0.8478	0.5903	0.8049

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.0+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2