ec_classfication

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

• Loss: 0.6543
• F1: 0.7609

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-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	F1
No log	1.0	31	0.6418	0.4262
No log	2.0	62	0.4992	0.7342
No log	3.0	93	0.4732	0.7879
No log	4.0	124	0.4817	0.7089
No log	5.0	155	0.4872	0.7742
No log	6.0	186	0.5026	0.7872
No log	7.0	217	0.5202	0.7778
No log	8.0	248	0.5280	0.7711
No log	9.0	279	0.5629	0.75
No log	10.0	310	0.6319	0.7872
No log	11.0	341	0.6363	0.7872
No log	12.0	372	0.6850	0.7708
No log	13.0	403	0.6702	0.7872
No log	14.0	434	0.6495	0.7692
No log	15.0	465	0.6543	0.7609

Framework versions

• Transformers 4.27.3
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.2