---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: SingPurcBERT-UCIRetail
  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. -->

# SingPurcBERT-UCIRetail

This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4977
- Accuracy: 0.7858
- F1: 0.7857

## 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: 2e-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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 456  | 0.5088          | 0.7652   | 0.7624 |
| 0.5884        | 2.0   | 912  | 0.5192          | 0.7702   | 0.7693 |
| 0.478         | 3.0   | 1368 | 0.4977          | 0.7858   | 0.7857 |
| 0.4144        | 4.0   | 1824 | 0.6869          | 0.7825   | 0.7824 |
| 0.3745        | 5.0   | 2280 | 0.6851          | 0.7932   | 0.7928 |
| 0.3529        | 6.0   | 2736 | 0.8428          | 0.7735   | 0.7735 |
| 0.2819        | 7.0   | 3192 | 1.2367          | 0.7776   | 0.7775 |
| 0.2451        | 8.0   | 3648 | 1.3244          | 0.7669   | 0.7668 |
| 0.1924        | 9.0   | 4104 | 1.5086          | 0.7735   | 0.7735 |
| 0.1428        | 10.0  | 4560 | 1.4940          | 0.7776   | 0.7776 |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.14.1