---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: NLP_91_1
  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. -->

# NLP_91_1

This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co./google-bert/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4408
- Accuracy: 0.9220
- Precision: 0.9156
- Recall: 0.9170
- F1: 0.9158

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.309         | 1.0   | 48   | 0.4280          | 0.8532   | 0.8506    | 0.8461 | 0.8454 |
| 0.2595        | 2.0   | 96   | 0.4335          | 0.8807   | 0.8767    | 0.8766 | 0.8738 |
| 0.2196        | 3.0   | 144  | 0.3883          | 0.8945   | 0.8956    | 0.8869 | 0.8876 |
| 0.1812        | 4.0   | 192  | 0.4664          | 0.8761   | 0.8856    | 0.8614 | 0.8638 |
| 0.1256        | 5.0   | 240  | 0.4764          | 0.8670   | 0.8750    | 0.8627 | 0.8625 |
| 0.142         | 6.0   | 288  | 0.5258          | 0.8670   | 0.8818    | 0.8580 | 0.8607 |
| 0.1006        | 7.0   | 336  | 0.4323          | 0.9037   | 0.8961    | 0.8989 | 0.8970 |
| 0.0897        | 8.0   | 384  | 0.4659          | 0.8991   | 0.8959    | 0.8891 | 0.8914 |
| 0.0595        | 9.0   | 432  | 0.4569          | 0.9174   | 0.9149    | 0.9099 | 0.9115 |
| 0.0399        | 10.0  | 480  | 0.4592          | 0.9037   | 0.8981    | 0.8970 | 0.8966 |
| 0.056         | 11.0  | 528  | 0.4461          | 0.9174   | 0.9102    | 0.9091 | 0.9094 |
| 0.0451        | 12.0  | 576  | 0.4772          | 0.8991   | 0.8926    | 0.8891 | 0.8906 |
| 0.0309        | 13.0  | 624  | 0.4396          | 0.9220   | 0.9160    | 0.9169 | 0.9155 |
| 0.0338        | 14.0  | 672  | 0.4423          | 0.9220   | 0.9156    | 0.9170 | 0.9158 |
| 0.0458        | 15.0  | 720  | 0.4408          | 0.9220   | 0.9156    | 0.9170 | 0.9158 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1