xlm-roberta-base-IDMGSP-danish – AI Model by ernlavr | AlphaNeural AI

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ernlavr/
xlm-roberta-base-IDMGSP-danish

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text-classification

generated_from_trainer

ernlavr/IDMGSP-danish

FacebookAI/xlm-roberta-base

autotrain_compatible

endpoints_compatible

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xlm-roberta-base-IDMGSP-danish

This model is a fine-tuned version of xlm-roberta-base on the 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 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