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: 0.5714
- Precision: 0.7417
- Recall: 1.0
- F1 and accuracy: {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847}

## 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: 0.001
- 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  | 0.5780          | 0.7417    | 1.0    | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.647         | 2.0   | 782  | 0.5748          | 0.7417    | 1.0    | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6216        | 3.0   | 1173 | 0.5713          | 0.7417    | 1.0    | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6201        | 4.0   | 1564 | 0.5726          | 0.7417    | 1.0    | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6201        | 5.0   | 1955 | 0.5765          | 0.7417    | 1.0    | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6199        | 6.0   | 2346 | 0.5756          | 0.7417    | 1.0    | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6365        | 7.0   | 2737 | 0.5827          | 0.7417    | 1.0    | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6165        | 8.0   | 3128 | 0.5715          | 0.7417    | 1.0    | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6185        | 9.0   | 3519 | 0.5715          | 0.7417    | 1.0    | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |
| 0.6185        | 10.0  | 3910 | 0.5714          | 0.7417    | 1.0    | {'accuracy': 0.7416879795396419, 'f1': 0.8516886930983847} |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
