results_classification

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: 0.2517
• Accuracy: 0.9214
• F1: 0.9214

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 50
• training_steps: 500

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.152	0.0133	50	0.3216	0.9037	0.9033
0.1533	0.0267	100	0.3024	0.9096	0.9095
0.1443	0.04	150	0.3356	0.9017	0.9010
0.1101	0.0533	200	0.3121	0.9134	0.9133
0.1147	0.0667	250	0.3813	0.9005	0.9002
0.1611	0.08	300	0.2992	0.9134	0.9129
0.1553	0.0933	350	0.2858	0.9166	0.9166
0.1268	0.1067	400	0.2769	0.9186	0.9185
0.2011	0.12	450	0.2525	0.9214	0.9215
0.1845	0.1333	500	0.2517	0.9214	0.9214

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1