lbl-file15-fold1

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.6882
• Accuracy: 0.4296
• F1: 0.4410
• Precision: 0.5669
• Recall: 0.4296
• Accuracy Label Label 0: 0.5021
• Accuracy Label Label 1: 0.4776
• Accuracy Label Label 2: 0.0888
• Accuracy Label Label 3: 0.7349
• Accuracy Label Label 4: 0.1862
• Accuracy Label Label 5: 0.6569
• Accuracy Label Label 6: 0.2955
• Accuracy Label Label 7: 0.2470
• Accuracy Label Label 8: 0.4405
• Accuracy Label Label 9: 0.6897

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: 1e-07
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Accuracy Label Label 0	Accuracy Label Label 1	Accuracy Label Label 2	Accuracy Label Label 3	Accuracy Label Label 4	Accuracy Label Label 5	Accuracy Label Label 6	Accuracy Label Label 7	Accuracy Label Label 8	Accuracy Label Label 9
No log	0.16	25	1.7458	0.4072	0.4178	0.5660	0.4072	0.4378	0.4163	0.0695	0.7269	0.1255	0.6444	0.2955	0.2390	0.4246	0.7088
No log	0.32	50	1.7245	0.4156	0.4269	0.5706	0.4156	0.4678	0.4245	0.0734	0.7269	0.1538	0.6569	0.2955	0.2430	0.4325	0.7011
No log	0.48	75	1.7084	0.4228	0.4344	0.5738	0.4228	0.4893	0.4408	0.0811	0.7349	0.1741	0.6569	0.2955	0.2430	0.4365	0.6973
No log	0.64	100	1.6977	0.4256	0.4373	0.5698	0.4256	0.4893	0.4653	0.0811	0.7349	0.1822	0.6569	0.2955	0.2430	0.4405	0.6897
No log	0.8	125	1.6911	0.4276	0.4391	0.5663	0.4276	0.4979	0.4694	0.0849	0.7349	0.1862	0.6569	0.2955	0.2430	0.4405	0.6897
No log	0.96	150	1.6883	0.4296	0.4411	0.5673	0.4296	0.5064	0.4776	0.0888	0.7349	0.1862	0.6569	0.2955	0.2430	0.4405	0.6897

Framework versions

• Transformers 4.30.2
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.13.3