---
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