---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-multilingual-cased-IDMGSP-danish
  results: []
datasets:
- ernlavr/IDMGSP-danish
language:
- da
library_name: transformers
---

<!-- 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. -->

# bert-base-multilingual-cased-IDMGSP-danish

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the on the [ernlavr/IDMGSP-danish](https://huggingface.co/datasets/ernlavr/IDMGSP-danish) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0123
- Accuracy: {'accuracy': 0.8289043068464459}
- F1: {'f1': 0.842473183078221}

## 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: 0.0001
- 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: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                         |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|
| 0.4692        | 1.0   | 480  | 0.3779          | {'accuracy': 0.8519439717240477} | {'f1': 0.84845236500067}   |
| 0.3267        | 2.0   | 960  | 0.5350          | {'accuracy': 0.7896321508050792} | {'f1': 0.8138538167496815} |
| 0.5149        | 3.0   | 1440 | 0.7051          | {'accuracy': 0.7510145306977353} | {'f1': 0.7911267296288161} |
| 0.2823        | 4.0   | 1920 | 0.6520          | {'accuracy': 0.7317711742374656} | {'f1': 0.7837010450754776} |
| 0.2107        | 5.0   | 2400 | 0.3335          | {'accuracy': 0.8785181306453724} | {'f1': 0.8759689922480619} |
| 0.1868        | 6.0   | 2880 | 0.8269          | {'accuracy': 0.8175153815944496} | {'f1': 0.8349123638086214} |
| 0.0969        | 7.0   | 3360 | 0.4585          | {'accuracy': 0.877470873150936}  | {'f1': 0.872200983069361}  |
| 0.1116        | 8.0   | 3840 | 1.0309          | {'accuracy': 0.7993192826286163} | {'f1': 0.8236106316879531} |
| 0.0386        | 9.0   | 4320 | 0.9517          | {'accuracy': 0.8294279355936641} | {'f1': 0.8426898466739103} |
| 0.0204        | 10.0  | 4800 | 1.0123          | {'accuracy': 0.8289043068464459} | {'f1': 0.842473183078221}  |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1