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: 0.5734
- Precision: 0.7362
- Recall: 0.8026
- F1 and accuracy: {'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544}

## 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   | 372  | 0.6573          | 0.6247    | 1.0    | {'accuracy': 0.6246648793565683, 'f1': 0.768976897689769}  |
| 0.6699        | 2.0   | 744  | 0.6452          | 0.6247    | 1.0    | {'accuracy': 0.6246648793565683, 'f1': 0.768976897689769}  |
| 0.6412        | 3.0   | 1116 | 0.6130          | 0.6602    | 0.8755 | {'accuracy': 0.6407506702412868, 'f1': 0.7527675276752768} |
| 0.6412        | 4.0   | 1488 | 0.5949          | 0.7413    | 0.8240 | {'accuracy': 0.710455764075067, 'f1': 0.7804878048780487}  |
| 0.6158        | 5.0   | 1860 | 0.5860          | 0.7323    | 0.8455 | {'accuracy': 0.710455764075067, 'f1': 0.7848605577689244}  |
| 0.5891        | 6.0   | 2232 | 0.5802          | 0.7381    | 0.7983 | {'accuracy': 0.6970509383378016, 'f1': 0.7670103092783506} |
| 0.5855        | 7.0   | 2604 | 0.5770          | 0.7354    | 0.8112 | {'accuracy': 0.6997319034852547, 'f1': 0.7714285714285714} |
| 0.5855        | 8.0   | 2976 | 0.5757          | 0.7328    | 0.8240 | {'accuracy': 0.7024128686327078, 'f1': 0.7757575757575758} |
| 0.5839        | 9.0   | 3348 | 0.5741          | 0.7362    | 0.8026 | {'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544} |
| 0.5759        | 10.0  | 3720 | 0.5734          | 0.7362    | 0.8026 | {'accuracy': 0.6970509383378016, 'f1': 0.7679671457905544} |


### Framework versions

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