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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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: 0.5930
- Precision: 0.7325
- Recall: 0.7542
- F1 and accuracy: {'accuracy': 0.6702412868632708, 'f1': 0.74321503131524}

## 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  | 0.6533          | 0.6327    | 1.0    | {'accuracy': 0.6327077747989276, 'f1': 0.7750410509031198} |
| 0.67          | 2.0   | 744  | 0.6432          | 0.6327    | 1.0    | {'accuracy': 0.6327077747989276, 'f1': 0.7750410509031198} |
| 0.6548        | 3.0   | 1116 | 0.6197          | 0.6341    | 0.9915 | {'accuracy': 0.6327077747989276, 'f1': 0.7735537190082644} |
| 0.6548        | 4.0   | 1488 | 0.6020          | 0.6678    | 0.8178 | {'accuracy': 0.6273458445040214, 'f1': 0.7352380952380952} |
| 0.6211        | 5.0   | 1860 | 0.5969          | 0.696     | 0.7373 | {'accuracy': 0.6300268096514745, 'f1': 0.7160493827160493} |
| 0.5929        | 6.0   | 2232 | 0.5954          | 0.6980    | 0.7542 | {'accuracy': 0.6380697050938338, 'f1': 0.7250509164969451} |
| 0.5887        | 7.0   | 2604 | 0.5940          | 0.7412    | 0.7161 | {'accuracy': 0.6621983914209115, 'f1': 0.728448275862069}  |
| 0.5887        | 8.0   | 2976 | 0.5937          | 0.7426    | 0.7458 | {'accuracy': 0.675603217158177, 'f1': 0.7441860465116279}  |
| 0.5809        | 9.0   | 3348 | 0.5933          | 0.7247    | 0.7585 | {'accuracy': 0.6648793565683646, 'f1': 0.7412008281573499} |
| 0.5726        | 10.0  | 3720 | 0.5930          | 0.7325    | 0.7542 | {'accuracy': 0.6702412868632708, 'f1': 0.74321503131524}   |


### Framework versions

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