| { |
| "model_type": "ConcatModelWithRationale", |
| "hatebert_model": "GroNLP/HateBERT", |
| "rationale_model": "bert-base-uncased", |
|
|
| "training_config": { |
| "cnn_filters": 128, |
| "cnn_kernels": [2, 3, 4], |
| "cnn_dropout": 0.3, |
| "adapter_dim": 128, |
| "max_length": 128, |
| "num_labels": 2, |
|
|
| "batch_size": 8, |
| "learning_rate": 2e-5, |
| "weight_decay": 0.05, |
| "best_val_f1": 0.00 |
| }, |
|
|
| "architecture": { |
| "temporal_cnn": "TemporalCNN (max+mean pooling)", |
| "msa_cnn": "MultiScaleAttentionCNN (token attention pooling)", |
| "selector": "Gumbel-Sigmoid Token Selector", |
| "projection": "ProjectionMLP (concat CLS + CNN + rationale pooled)" |
| }, |
|
|
| "notes": { |
| "trained_with": "ConcatModelWithRationale", |
| "framework": "PyTorch + Transformers", |
| "encoder_main": "HateBERT", |
| "encoder_rationale": "BERT-base-uncased" |
| } |
| } |
