---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-IDMGSP-danish
  results: []
datasets:
- ernlavr/IDMGSP-danish
language:
- da
---

<!-- 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. -->

# xlm-roberta-base-IDMGSP-danish

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [ernlavr/IDMGSP-danish](https://huggingface.co/datasets/ernlavr/IDMGSP-danish) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0276
- Accuracy: {'accuracy': 0.8530452362901187}
- F1: {'f1': 0.8630636995172495}

## Intended uses & limitations

Binary classification, label `0` - text is not AI generated; label `1` - text is AI generated

## Training and evaluation data

[ernlavr/IDMGSP-danish](https://huggingface.co/datasets/ernlavr/IDMGSP-danish) dataset.

## 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                         |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|
| 0.3631        | 1.0   | 496  | 0.9902          | {'accuracy': 0.5959059893858984} | {'f1': 0.7104310032596887} |
| 0.3208        | 2.0   | 992  | 1.0736          | {'accuracy': 0.7261814505938843} | {'f1': 0.780245411215901}  |
| 0.2191        | 3.0   | 1488 | 0.3496          | {'accuracy': 0.8664392216325499} | {'f1': 0.8717077315208156} |
| 0.1548        | 4.0   | 1984 | 0.5604          | {'accuracy': 0.8155168056608542} | {'f1': 0.8378858538751943} |
| 0.1127        | 5.0   | 2480 | 0.4164          | {'accuracy': 0.8641647712913824} | {'f1': 0.871056735036584}  |
| 0.1372        | 6.0   | 2976 | 0.5515          | {'accuracy': 0.8822340156684357} | {'f1': 0.8833833833833834} |
| 0.0279        | 7.0   | 3472 | 0.7203          | {'accuracy': 0.8458428102097548} | {'f1': 0.8573766658873042} |
| 0.0456        | 8.0   | 3968 | 0.8584          | {'accuracy': 0.8498862774829417} | {'f1': 0.8604651162790697} |
| 0.0095        | 9.0   | 4464 | 0.9214          | {'accuracy': 0.8512762193580996} | {'f1': 0.861415283174379}  |
| 0.0076        | 10.0  | 4960 | 1.0276          | {'accuracy': 0.8530452362901187} | {'f1': 0.8630636995172495} |


### Framework versions

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