---
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_90_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_90_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: 0.3325
- Accuracy: 0.9174
- Precision: 0.9126
- Recall: 0.9140
- F1: 0.9128

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3664        | 1.0   | 48   | 0.3609          | 0.8991   | 0.8935    | 0.8988 | 0.8938 |
| 0.2282        | 2.0   | 96   | 0.3376          | 0.8991   | 0.8920    | 0.8978 | 0.8927 |
| 0.1638        | 3.0   | 144  | 0.3184          | 0.9128   | 0.9070    | 0.9079 | 0.9070 |
| 0.1595        | 4.0   | 192  | 0.3291          | 0.9174   | 0.9147    | 0.9131 | 0.9135 |
| 0.1388        | 5.0   | 240  | 0.3495          | 0.8945   | 0.8844    | 0.8918 | 0.8865 |
| 0.1075        | 6.0   | 288  | 0.3357          | 0.9174   | 0.9151    | 0.9141 | 0.9139 |
| 0.1073        | 7.0   | 336  | 0.3311          | 0.9174   | 0.9126    | 0.9140 | 0.9128 |
| 0.1507        | 8.0   | 384  | 0.3325          | 0.9174   | 0.9126    | 0.9140 | 0.9128 |


### Framework versions

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