classify-ISIN-STEP6_binary

This model is a fine-tuned version of albert/albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0002
• Accuracy: 1.0
• F1: 1.0
• Precision: 1.0
• Recall: 1.0
• Accuracy Label Gd622:null: 1.0
• Accuracy Label Gd622:yes: 1.0

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-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 200
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Accuracy Label Gd622:null	Accuracy Label Gd622:yes
0.0056	2.4691	100	0.0042	1.0	1.0	1.0	1.0	1.0	1.0
0.001	4.9383	200	0.0009	1.0	1.0	1.0	1.0	1.0	1.0
0.0005	7.4074	300	0.0004	1.0	1.0	1.0	1.0	1.0	1.0
0.0003	9.8765	400	0.0003	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	12.3457	500	0.0002	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	14.8148	600	0.0002	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	17.2840	700	0.0002	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	19.7531	800	0.0002	1.0	1.0	1.0	1.0	1.0	1.0

Framework versions

• Transformers 4.43.3
• Pytorch 2.4.0
• Datasets 2.20.0
• Tokenizers 0.19.1