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: 0.4881
- Precision: 0.7966
- Recall: 0.9216
- F1 and accuracy: {'accuracy': 0.7605985037406484, 'f1': 0.8545454545454545}

## 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  | 0.5429          | 0.7631    | 1.0    | {'accuracy': 0.7630922693266833, 'f1': 0.8656294200848657} |
| 0.5808        | 2.0   | 802  | 0.5361          | 0.7631    | 1.0    | {'accuracy': 0.7630922693266833, 'f1': 0.8656294200848657} |
| 0.5805        | 3.0   | 1203 | 0.5235          | 0.7631    | 1.0    | {'accuracy': 0.7630922693266833, 'f1': 0.8656294200848657} |
| 0.5554        | 4.0   | 1604 | 0.5096          | 0.7669    | 1.0    | {'accuracy': 0.7680798004987531, 'f1': 0.8680851063829788} |
| 0.5214        | 5.0   | 2005 | 0.5046          | 0.7734    | 0.9706 | {'accuracy': 0.7605985037406484, 'f1': 0.8608695652173913} |
| 0.5214        | 6.0   | 2406 | 0.4971          | 0.7950    | 0.9379 | {'accuracy': 0.7680798004987531, 'f1': 0.8605697151424289} |
| 0.5152        | 7.0   | 2807 | 0.4919          | 0.7983    | 0.9183 | {'accuracy': 0.7605985037406484, 'f1': 0.8541033434650457} |
| 0.4956        | 8.0   | 3208 | 0.4881          | 0.8017    | 0.9118 | {'accuracy': 0.7605985037406484, 'f1': 0.8532110091743118} |
| 0.4891        | 9.0   | 3609 | 0.4881          | 0.7972    | 0.9248 | {'accuracy': 0.7630922693266833, 'f1': 0.8562783661119516} |
| 0.5038        | 10.0  | 4010 | 0.4881          | 0.7966    | 0.9216 | {'accuracy': 0.7605985037406484, 'f1': 0.8545454545454545} |


### Framework versions

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