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

# ConcSeqBERT-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: 0.4742
- Accuracy: 0.7850
- F1: 0.7847

## 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  | 0.5586          | 0.7290   | 0.7289 |
| 0.6851        | 2.0   | 912  | 0.4842          | 0.7718   | 0.7703 |
| 0.5396        | 3.0   | 1368 | 0.4742          | 0.7850   | 0.7847 |
| 0.448         | 4.0   | 1824 | 0.5257          | 0.7932   | 0.7920 |
| 0.3934        | 5.0   | 2280 | 0.6493          | 0.7801   | 0.7779 |
| 0.3476        | 6.0   | 2736 | 0.9150          | 0.7644   | 0.7644 |
| 0.2971        | 7.0   | 3192 | 1.1450          | 0.7727   | 0.7723 |
| 0.2307        | 8.0   | 3648 | 1.0960          | 0.7743   | 0.7743 |
| 0.2112        | 9.0   | 4104 | 1.2912          | 0.7842   | 0.7842 |
| 0.1757        | 10.0  | 4560 | 1.3925          | 0.7759   | 0.7759 |


### Framework versions

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