| --- |
| library_name: transformers |
| license: mit |
| base_model: xlm-roberta-base |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: mhqa-cross-encoder-reranker |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # mhqa-cross-encoder-reranker |
|
|
| This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0111 |
|
|
| ## 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: 16 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - 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 |
| - num_epochs: 2.0 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:-----:|:---------------:| |
| | 0.0223 | 0.0474 | 250 | 0.0165 | |
| | 0.0237 | 0.0949 | 500 | 0.0162 | |
| | 0.0233 | 0.1423 | 750 | 0.0147 | |
| | 0.0236 | 0.1897 | 1000 | 0.0166 | |
| | 0.0176 | 0.2371 | 1250 | 0.0157 | |
| | 0.0153 | 0.2846 | 1500 | 0.0140 | |
| | 0.0230 | 0.3320 | 1750 | 0.0175 | |
| | 0.0174 | 0.3794 | 2000 | 0.0158 | |
| | 0.0153 | 0.4269 | 2250 | 0.0153 | |
| | 0.0187 | 0.4743 | 2500 | 0.0140 | |
| | 0.0157 | 0.5217 | 2750 | 0.0138 | |
| | 0.0203 | 0.5692 | 3000 | 0.0144 | |
| | 0.0154 | 0.6166 | 3250 | 0.0134 | |
| | 0.0158 | 0.6640 | 3500 | 0.0133 | |
| | 0.0192 | 0.7114 | 3750 | 0.0127 | |
| | 0.0212 | 0.7589 | 4000 | 0.0160 | |
| | 0.0143 | 0.8063 | 4250 | 0.0131 | |
| | 0.0113 | 0.8537 | 4500 | 0.0125 | |
| | 0.0140 | 0.9012 | 4750 | 0.0127 | |
| | 0.0129 | 0.9486 | 5000 | 0.0126 | |
| | 0.0163 | 0.9960 | 5250 | 0.0122 | |
| | 0.0148 | 1.0434 | 5500 | 0.0123 | |
| | 0.0136 | 1.0909 | 5750 | 0.0120 | |
| | 0.0140 | 1.1383 | 6000 | 0.0122 | |
| | 0.0153 | 1.1857 | 6250 | 0.0128 | |
| | 0.0135 | 1.2332 | 6500 | 0.0122 | |
| | 0.0139 | 1.2806 | 6750 | 0.0133 | |
| | 0.0147 | 1.3280 | 7000 | 0.0115 | |
| | 0.0133 | 1.3755 | 7250 | 0.0121 | |
| | 0.0110 | 1.4229 | 7500 | 0.0116 | |
| | 0.0130 | 1.4703 | 7750 | 0.0118 | |
| | 0.0138 | 1.5177 | 8000 | 0.0122 | |
| | 0.0119 | 1.5652 | 8250 | 0.0115 | |
| | 0.0089 | 1.6126 | 8500 | 0.0113 | |
| | 0.0110 | 1.6600 | 8750 | 0.0113 | |
| | 0.0138 | 1.7075 | 9000 | 0.0119 | |
| | 0.0140 | 1.7549 | 9250 | 0.0116 | |
| | 0.0116 | 1.8023 | 9500 | 0.0111 | |
| | 0.0132 | 1.8497 | 9750 | 0.0114 | |
| | 0.0119 | 1.8972 | 10000 | 0.0113 | |
| | 0.0131 | 1.9446 | 10250 | 0.0113 | |
| | 0.0124 | 1.9920 | 10500 | 0.0113 | |
| | 0.0124 | 2.0 | 10542 | 0.0113 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.10.2 |
| - Pytorch 2.12.0+cu130 |
| - Datasets 5.0.0 |
| - Tokenizers 0.22.2 |
| |
