This is a BERT-based text classification model fine-tuned on a 5-class dataset. The best checkpoint was selected based on validation F1 score across multiple hyperparameter sweeps.
Training Details
Base Model: bert-base-uncased
Best Run ID: run-def456
Best Run Name: sweep-lr5e5-bs16
Learning Rate: 5e-05
Batch Size: 16
Weight Decay: 0.01
Best Epoch: 10
Evaluation Results
Validation F1: 0.851
Validation Accuracy: 0.865
Final Validation Loss: 0.487
Validation Metrics
Run Comparison (sorted by val_f1 descending)
Run ID
Run Name
Learning Rate
Batch Size
Weight Decay
Val F1
Val Accuracy
Val Loss
Best Epoch
run-def456
sweep-lr5e5-bs16
5e-05
16
0.01
0.851
0.865
0.487
10
run-pqr678
sweep-lr5e5-bs16-wd005
5e-05
16
0.005
0.841
0.855
0.512
10
run-jkl012
sweep-lr5e5-bs32-wd0
5e-05
32
0.0
0.829
0.841
0.583
8
run-abc123
sweep-lr3e5-bs32
3e-05
32
0.01
0.811
0.826
0.585
10
run-ghi789
sweep-lr2e5-bs64
2e-05
64
0.02
0.782
0.796
0.649
10
run-mno345
sweep-lr1e4-bs32
0.0001
32
0.01
0.735
0.751
0.821
7
Confusion Matrix
Intended Use
This model is intended for text classification tasks with 5 output classes. It should not be used for generating text or for tasks outside its training distribution.
Limitations
The model's performance is benchmark-specific and may not generalize to out-of-distribution inputs or domains not seen during training.
