---
library_name: peft
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- base_model:adapter:bert-base-multilingual-cased
- lora
- transformers
metrics:
- f1
model-index:
- name: mbert-loraxs-qa-vanilla
  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. -->

# mbert-loraxs-qa-vanilla

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4437
- Exact Match: 55.68
- F1: 70.3954

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 1.6012        | 1.0   | 634  | 1.6034          | 52.8        | 66.3884 |
| 1.5347        | 2.0   | 1268 | 1.5703          | 54.16       | 67.8105 |
| 1.5039        | 3.0   | 1902 | 1.4684          | 55.12       | 69.1541 |
| 1.4706        | 4.0   | 2536 | 1.4466          | 55.68       | 69.7596 |
| 1.4197        | 5.0   | 3170 | 1.4419          | 56.48       | 70.7228 |
| 1.3971        | 6.0   | 3804 | 1.4516          | 55.76       | 70.6580 |
| 1.3627        | 7.0   | 4438 | 1.4437          | 55.68       | 70.3954 |


### Framework versions

- PEFT 0.19.1
- Transformers 5.9.0
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2