bge-reranker-v2-m3-finetuned
This model is a fine-tuned version of BAAI/bge-reranker-v2-m3 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2950
- Pointwise Accuracy: 0.919
- Pointwise F1: 0.7990
- Pointwise Auc: 0.9567
- Group Top1 Accuracy: 0.885
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Pointwise Accuracy | Pointwise F1 | Pointwise Auc | Group Top1 Accuracy |
|---|---|---|---|---|---|---|---|
| 1.8756 | 1.0 | 619 | 0.2950 | 0.919 | 0.7990 | 0.9567 | 0.885 |
Framework versions
- Transformers 5.13.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2