TagPredictionModels
Collection
The models in this collection were introduced in the paper Domain Adaptation for Automated Tag Prediction in Competitive Programming.
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16 items
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Updated
train_2025-06-07-20-30-22
This model is a fine-tuned version of google/gemma-3-12b-it on the top_20_training_dataset and the top_20_validation_dataset datasets.
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: 2e-05
- train_batch_size: 5
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 25
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 150
- num_epochs: 4.0
- label_smoothing_factor: 0.05
Training results
Framework versions
- PEFT 0.15.1
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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