---
library_name: peft
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: MBERT_uncased_WeightedFocalLoss_lora
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# MBERT_uncased_WeightedFocalLoss_lora

This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0414
- Accuracy: 0.708
- F1: 0.8290
- Precision: 0.7195
- Recall: 0.9779
- Roc Auc: 0.4890

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| No log        | 0.992 | 31   | 0.0430          | 0.62     | 0.7613 | 0.6982    | 0.8370 | 0.4439  |
| No log        | 1.984 | 62   | 0.0417          | 0.699    | 0.8226 | 0.7174    | 0.9641 | 0.4839  |
| No log        | 2.976 | 93   | 0.0414          | 0.708    | 0.8290 | 0.7195    | 0.9779 | 0.4890  |


### Framework versions

- PEFT 0.13.3.dev0
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3