---
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_2nd
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# NLP_whole_dataseet_2nd

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: 0.0646
- Accuracy: 0.9771
- Precision: 0.9747
- Recall: 0.9741
- F1: 0.9738

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2952        | 1.0   | 55   | 0.1311          | 0.9725   | 0.9693    | 0.9690 | 0.9691 |
| 0.1988        | 2.0   | 110  | 0.0827          | 0.9679   | 0.9663    | 0.9632 | 0.9638 |
| 0.1823        | 3.0   | 165  | 0.0595          | 0.9771   | 0.9746    | 0.9712 | 0.9724 |
| 0.1237        | 4.0   | 220  | 0.0646          | 0.9771   | 0.9747    | 0.9741 | 0.9738 |


### Framework versions

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