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: 0.5474
- Precision: 0.7635
- Recall: 0.9338
- F1 and accuracy: {'accuracy': 0.7456359102244389, 'f1': 0.8401253918495297}

## 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   | 401  | 0.6017          | 0.7157    | 1.0    | {'accuracy': 0.71571072319202, 'f1': 0.8343023255813953}   |
| 0.5798        | 2.0   | 802  | 0.5967          | 0.7157    | 1.0    | {'accuracy': 0.71571072319202, 'f1': 0.8343023255813953}   |
| 0.5546        | 3.0   | 1203 | 0.5722          | 0.7157    | 1.0    | {'accuracy': 0.71571072319202, 'f1': 0.8343023255813953}   |
| 0.5403        | 4.0   | 1604 | 0.5624          | 0.7259    | 0.9965 | {'accuracy': 0.7281795511221946, 'f1': 0.8399412628487517} |
| 0.5206        | 5.0   | 2005 | 0.5597          | 0.7368    | 0.9756 | {'accuracy': 0.7331670822942643, 'f1': 0.8395802098950524} |
| 0.5206        | 6.0   | 2406 | 0.5588          | 0.7520    | 0.9617 | {'accuracy': 0.7456359102244389, 'f1': 0.8440366972477064} |
| 0.5153        | 7.0   | 2807 | 0.5679          | 0.7554    | 0.9686 | {'accuracy': 0.7531172069825436, 'f1': 0.8488549618320611} |
| 0.4959        | 8.0   | 3208 | 0.5693          | 0.7576    | 0.9582 | {'accuracy': 0.7506234413965087, 'f1': 0.8461538461538461} |
| 0.4801        | 9.0   | 3609 | 0.5466          | 0.7635    | 0.9338 | {'accuracy': 0.7456359102244389, 'f1': 0.8401253918495297} |
| 0.4949        | 10.0  | 4010 | 0.5474          | 0.7635    | 0.9338 | {'accuracy': 0.7456359102244389, 'f1': 0.8401253918495297} |


### Framework versions

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