---
license: mit
tags:
- generated_from_trainer
base_model: Microsoft/Multilingual-MiniLM-L12-H384
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: my-model-MiniLM-Area
  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. -->

# my-model-MiniLM-Area

This model is a fine-tuned version of [Microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/Microsoft/Multilingual-MiniLM-L12-H384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5228
- Accuracy: 0.4323
- F1: 0.3979
- Precision: 0.3932
- Recall: 0.4323

## 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: 5e-05
- train_batch_size: 30
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.8812        | 1.0   | 25   | 1.8038          | 0.2839   | 0.1709 | 0.2712    | 0.2839 |
| 1.8043        | 2.0   | 50   | 1.7540          | 0.3742   | 0.2586 | 0.2046    | 0.3742 |
| 1.7687        | 3.0   | 75   | 1.6908          | 0.3806   | 0.2557 | 0.1927    | 0.3806 |
| 1.6959        | 4.0   | 100  | 1.6325          | 0.4      | 0.2695 | 0.2033    | 0.4    |
| 1.6178        | 5.0   | 125  | 1.6401          | 0.4129   | 0.3338 | 0.2874    | 0.4129 |
| 1.5189        | 6.0   | 150  | 1.5471          | 0.4581   | 0.3631 | 0.3030    | 0.4581 |
| 1.4393        | 7.0   | 175  | 1.5966          | 0.4258   | 0.3761 | 0.3451    | 0.4258 |
| 1.3757        | 8.0   | 200  | 1.5716          | 0.4452   | 0.3945 | 0.3556    | 0.4452 |
| 1.3032        | 9.0   | 225  | 1.5691          | 0.4387   | 0.3646 | 0.3443    | 0.4387 |
| 1.2434        | 10.0  | 250  | 1.5740          | 0.4452   | 0.4057 | 0.3798    | 0.4452 |
| 1.1837        | 11.0  | 275  | 1.5108          | 0.4645   | 0.3854 | 0.3852    | 0.4645 |
| 1.1231        | 12.0  | 300  | 1.5409          | 0.4516   | 0.3972 | 0.3561    | 0.4516 |
| 1.0815        | 13.0  | 325  | 1.5111          | 0.4774   | 0.4116 | 0.3865    | 0.4774 |
| 1.0555        | 14.0  | 350  | 1.5171          | 0.4645   | 0.4014 | 0.3674    | 0.4645 |
| 0.9964        | 15.0  | 375  | 1.4971          | 0.4581   | 0.3877 | 0.3504    | 0.4581 |
| 0.9627        | 16.0  | 400  | 1.5157          | 0.4516   | 0.4118 | 0.3882    | 0.4516 |
| 0.9247        | 17.0  | 425  | 1.4996          | 0.4387   | 0.3882 | 0.3664    | 0.4387 |
| 0.9286        | 18.0  | 450  | 1.4990          | 0.4452   | 0.4008 | 0.3856    | 0.4452 |
| 0.892         | 19.0  | 475  | 1.5288          | 0.4323   | 0.4025 | 0.4031    | 0.4323 |
| 0.8843        | 20.0  | 500  | 1.5228          | 0.4323   | 0.3979 | 0.3932    | 0.4323 |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1