---
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_whole_dataseet_
  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_whole_dataseet_

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: 0.0925
- Accuracy: 0.9817
- Precision: 0.9797
- Recall: 0.9828
- F1: 0.9809

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3209        | 1.0   | 55   | 0.2928          | 0.9037   | 0.9020    | 0.8929 | 0.8950 |
| 0.1962        | 2.0   | 110  | 0.1979          | 0.9450   | 0.9447    | 0.9353 | 0.9387 |
| 0.2778        | 3.0   | 165  | 0.1383          | 0.9587   | 0.9530    | 0.9627 | 0.9560 |
| 0.2216        | 4.0   | 220  | 0.1156          | 0.9679   | 0.9667    | 0.9640 | 0.9652 |
| 0.2203        | 5.0   | 275  | 0.1061          | 0.9817   | 0.9797    | 0.9828 | 0.9809 |
| 0.1948        | 6.0   | 330  | 0.0967          | 0.9817   | 0.9797    | 0.9828 | 0.9809 |
| 0.2017        | 7.0   | 385  | 0.0902          | 0.9817   | 0.9797    | 0.9828 | 0.9809 |
| 0.2384        | 8.0   | 440  | 0.0925          | 0.9817   | 0.9797    | 0.9828 | 0.9809 |


### Framework versions

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