TagPredictionModels
Collection
The models in this collection were introduced in the paper Domain Adaptation for Automated Tag Prediction in Competitive Programming. • 15 items • Updated
train_2025-05-16-10-50-14_final
This model is a fine-tuned version of deepseek-ai/deepseek-llm-7b-base on the top_5_training_dataset dataset.
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 200
- num_epochs: 5.0
- 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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Model tree for Sauron0019/DeepSeek-LLM-7B-Base-TagPrediction-Top5-Editorial
Base model
deepseek-ai/deepseek-llm-7b-base