---
license: mit
base_model: Microsoft/Multilingual-MiniLM-L12-H384
tags:
- generated_from_trainer
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: 1.3835
- Accuracy: 0.4748
- F1: 0.4280
- Precision: 0.4147
- Recall: 0.4748

## 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: 8
- eval_batch_size: 8
- 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 | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.8171        | 1.0   | 81   | 1.7710          | 0.2302   | 0.0862 | 0.0530    | 0.2302 |
| 1.7264        | 2.0   | 162  | 1.6322          | 0.4101   | 0.2695 | 0.2007    | 0.4101 |
| 1.5603        | 3.0   | 243  | 1.5425          | 0.3885   | 0.2544 | 0.1943    | 0.3885 |
| 1.4237        | 4.0   | 324  | 1.5997          | 0.4317   | 0.3424 | 0.2883    | 0.4317 |
| 1.2819        | 5.0   | 405  | 1.5824          | 0.4101   | 0.3260 | 0.2763    | 0.4101 |
| 1.1493        | 6.0   | 486  | 1.4762          | 0.4460   | 0.3670 | 0.4151    | 0.4460 |
| 1.0688        | 7.0   | 567  | 1.4088          | 0.4748   | 0.4279 | 0.4418    | 0.4748 |
| 0.9746        | 8.0   | 648  | 1.4380          | 0.4604   | 0.3957 | 0.3835    | 0.4604 |
| 0.9039        | 9.0   | 729  | 1.4078          | 0.4748   | 0.4278 | 0.4147    | 0.4748 |
| 0.8577        | 10.0  | 810  | 1.3835          | 0.4748   | 0.4280 | 0.4147    | 0.4748 |


### Framework versions

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