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

# sentiment_bert

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: 0.7469
- Accuracy: 0.6802
- F1: 0.6332
- Precision: 0.6152
- Recall: 0.6942

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7975        | 1.0   | 94   | 0.9153          | 0.4158   | 0.4603 | 0.5512    | 0.5862 |
| 0.7765        | 2.0   | 188  | 0.8220          | 0.6583   | 0.6023 | 0.5894    | 0.6461 |
| 0.71          | 3.0   | 282  | 0.8345          | 0.6062   | 0.5908 | 0.5955    | 0.6858 |
| 0.6439        | 4.0   | 376  | 0.7753          | 0.6568   | 0.6241 | 0.6133    | 0.7010 |
| 0.6623        | 5.0   | 470  | 0.7469          | 0.6802   | 0.6332 | 0.6152    | 0.6942 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1