---
license: mit
base_model: nlptown/bert-base-multilingual-uncased-sentiment
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_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. -->

# test_bert

This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8332
- Accuracy: 0.6817

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1121        | 0.13  | 500  | 0.9841          | 0.6302   |
| 1.0067        | 0.25  | 1000 | 0.9490          | 0.6499   |
| 0.9325        | 0.38  | 1500 | 0.9200          | 0.6577   |
| 0.9301        | 0.51  | 2000 | 0.9684          | 0.6418   |
| 0.927         | 0.63  | 2500 | 0.9837          | 0.6234   |
| 0.9067        | 0.76  | 3000 | 0.8973          | 0.6572   |
| 0.8986        | 0.88  | 3500 | 0.8663          | 0.6747   |
| 0.8964        | 1.01  | 4000 | 0.8408          | 0.6767   |
| 0.8115        | 1.14  | 4500 | 0.8478          | 0.6696   |
| 0.8081        | 1.26  | 5000 | 0.8600          | 0.6681   |
| 0.7896        | 1.39  | 5500 | 0.8569          | 0.6747   |
| 0.8075        | 1.52  | 6000 | 0.8353          | 0.6767   |
| 0.802         | 1.64  | 6500 | 0.8261          | 0.6767   |
| 0.768         | 1.77  | 7000 | 0.8289          | 0.6782   |
| 0.7505        | 1.9   | 7500 | 0.8332          | 0.6817   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2