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
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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: 0.6074
- Precision: 0.7192
- Recall: 0.912
- F1 and accuracy: {'accuracy': 0.7146529562982005, 'f1': 0.8042328042328042}

## 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   | 388  | 0.6278          | 0.6723    | 0.96   | {'accuracy': 0.6735218508997429, 'f1': 0.7907742998352554} |
| 0.5998        | 2.0   | 776  | 0.6380          | 0.6713    | 0.956  | {'accuracy': 0.6709511568123393, 'f1': 0.7887788778877888} |
| 0.5865        | 3.0   | 1164 | 0.6196          | 0.6988    | 0.9    | {'accuracy': 0.6863753213367609, 'f1': 0.7867132867132868} |
| 0.5681        | 4.0   | 1552 | 0.6284          | 0.7018    | 0.932  | {'accuracy': 0.7017994858611826, 'f1': 0.8006872852233677} |
| 0.5681        | 5.0   | 1940 | 0.6072          | 0.7143    | 0.88   | {'accuracy': 0.6966580976863753, 'f1': 0.7885304659498208} |
| 0.5641        | 6.0   | 2328 | 0.6122          | 0.7031    | 0.9    | {'accuracy': 0.6915167095115681, 'f1': 0.7894736842105263} |
| 0.5356        | 7.0   | 2716 | 0.6074          | 0.7125    | 0.912  | {'accuracy': 0.7069408740359897, 'f1': 0.8}                |
| 0.5407        | 8.0   | 3104 | 0.6016          | 0.7320    | 0.896  | {'accuracy': 0.7223650385604113, 'f1': 0.8057553956834531} |
| 0.5407        | 9.0   | 3492 | 0.6079          | 0.7192    | 0.912  | {'accuracy': 0.7146529562982005, 'f1': 0.8042328042328042} |
| 0.535         | 10.0  | 3880 | 0.6074          | 0.7192    | 0.912  | {'accuracy': 0.7146529562982005, 'f1': 0.8042328042328042} |


### Framework versions

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