---
license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: distilbert-base-multilingual-cased-lora-text-classification
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-multilingual-cased-lora-text-classification

This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5041
- Precision: 0.7846
- Recall: 0.9075
- F1 and accuracy: {'accuracy': 0.7544757033248082, 'f1': 0.8415841584158416}

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 and accuracy                                            |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| No log        | 1.0   | 391  | 0.5886          | 0.7187    | 1.0    | {'accuracy': 0.7186700767263428, 'f1': 0.8363095238095238} |
| 0.6142        | 2.0   | 782  | 0.5735          | 0.7187    | 1.0    | {'accuracy': 0.7186700767263428, 'f1': 0.8363095238095238} |
| 0.5823        | 3.0   | 1173 | 0.5369          | 0.7321    | 0.9822 | {'accuracy': 0.7289002557544757, 'f1': 0.838905775075988}  |
| 0.5451        | 4.0   | 1564 | 0.5190          | 0.7486    | 0.9537 | {'accuracy': 0.7365728900255755, 'f1': 0.8388106416275432} |
| 0.5451        | 5.0   | 1955 | 0.5266          | 0.7542    | 0.9609 | {'accuracy': 0.7468030690537084, 'f1': 0.8450704225352114} |
| 0.5161        | 6.0   | 2346 | 0.5047          | 0.7731    | 0.9217 | {'accuracy': 0.7493606138107417, 'f1': 0.8409090909090909} |
| 0.5093        | 7.0   | 2737 | 0.5046          | 0.7761    | 0.9253 | {'accuracy': 0.7544757033248082, 'f1': 0.8441558441558441} |
| 0.4962        | 8.0   | 3128 | 0.5047          | 0.7774    | 0.9075 | {'accuracy': 0.7468030690537084, 'f1': 0.8374384236453202} |
| 0.4996        | 9.0   | 3519 | 0.5024          | 0.7937    | 0.8897 | {'accuracy': 0.7544757033248082, 'f1': 0.8389261744966443} |
| 0.4996        | 10.0  | 3910 | 0.5041          | 0.7846    | 0.9075 | {'accuracy': 0.7544757033248082, 'f1': 0.8415841584158416} |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2