tensorboard safetensors generated_from_trainer base_model:distilbert/distilbert-base-multilingual-cased license:apache-2.0 region:us
---
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: []
---

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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: 0.5321
- Precision: 0.7883
- Recall: 0.8589
- F1 and accuracy: {'accuracy': 0.7487113402061856, 'f1': 0.8220802919708029}

## 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                                            |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| 0.6034        | 1.0   | 1552  | 0.5999          | 0.6781    | 0.9981 | {'accuracy': 0.678479381443299, 'f1': 0.8075588121866564}  |
| 0.5756        | 2.0   | 3104  | 0.5892          | 0.7067    | 0.9418 | {'accuracy': 0.696520618556701, 'f1': 0.8075194115243155}  |
| 0.5607        | 3.0   | 4656  | 0.5630          | 0.7449    | 0.8770 | {'accuracy': 0.7139175257731959, 'f1': 0.8056042031523644} |
| 0.5458        | 4.0   | 6208  | 0.5549          | 0.7544    | 0.8990 | {'accuracy': 0.7338917525773195, 'f1': 0.8203566768160069} |
| 0.5342        | 5.0   | 7760  | 0.5816          | 0.7381    | 0.9457 | {'accuracy': 0.7364690721649485, 'f1': 0.8290848307563727} |
| 0.5266        | 6.0   | 9312  | 0.5399          | 0.7705    | 0.8799 | {'accuracy': 0.7416237113402062, 'f1': 0.8215398308856252} |
| 0.519         | 7.0   | 10864 | 0.5315          | 0.7932    | 0.8408 | {'accuracy': 0.7442010309278351, 'f1': 0.8162887552059231} |
| 0.4878        | 8.0   | 12416 | 0.5318          | 0.7880    | 0.8541 | {'accuracy': 0.7461340206185567, 'f1': 0.8197621225983532} |
| 0.485         | 9.0   | 13968 | 0.5332          | 0.7851    | 0.8637 | {'accuracy': 0.7480670103092784, 'f1': 0.8225147526100772} |
| 0.5044        | 10.0  | 15520 | 0.5321          | 0.7883    | 0.8589 | {'accuracy': 0.7487113402061856, 'f1': 0.8220802919708029} |


### Framework versions

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