gttbsc_distilbert-freezed-best
Ground truth text based multi-label DAC
Model description
Backbone: DistilBert uncased
Pooling: Self attention
Multi-label classification head: 2 dense layers with two dropouts 0.3 and Tanh activation inbetween
Training and evaluation data
Trained on ground truth slue-phase-2 hvb.
Evaluated on ground truth (GT) and Whisper small transcripts (E2E).
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00043
- weight decay: 1.5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20 (due to small patience stopped at around 3 epoch)
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Dataset used to train Masioki/gttbsc_distilbert-freezed-best
Evaluation results
- F1 macro E2E on asapp/slue-phase-2self-reported63.460
- F1 macro GT on asapp/slue-phase-2self-reported69.820