bert-base-uncased-pandas-github-issues

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: 0.0324
• F1 Micro: 0.5528
• Precision Micro: 0.6494
• Recall Micro: 0.4813
• Accuracy: 0.2932

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: 0.0001
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Micro	Precision Micro	Recall Micro	Accuracy
No log	1.0	123	0.2308	0.0320	0.0164	0.5905	0.0
No log	2.0	246	0.0527	0.1882	0.5469	0.1136	0.1233
No log	3.0	369	0.0476	0.2757	0.3968	0.2113	0.1459
No log	4.0	492	0.0409	0.4130	0.6102	0.3121	0.2533
0.1778	5.0	615	0.0365	0.4977	0.6169	0.4170	0.2671
0.1778	6.0	738	0.0346	0.5304	0.6461	0.4499	0.2820
0.1778	7.0	861	0.0328	0.5450	0.6405	0.4743	0.2815
0.1778	8.0	984	0.0324	0.5528	0.6494	0.4813	0.2932

Framework versions

• Transformers 4.53.3
• Pytorch 2.6.0+cu124
• Datasets 4.4.1
• Tokenizers 0.21.2