polyglot-tagger/multilabel-language-identification

Polyglot Tagger: Multi-label Language Identification

Refer to polyglot-tagger/language-identification. It is trained on the same dataset as a text-classifier rather than as a token classifier.

This model is a fine-tuned version of xlm-roberta-base. It achieves the following results on the evaluation set:

• —Loss: 0.0123
• —Precision: 0.9859
• —Recall: 0.9831
• —F1: 0.9845
• —Accuracy: 0.9412

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 5e-05
• —trainbatchsize: 32
• —evalbatchsize: 32
• —seed: 42
• —gradientaccumulationsteps: 18
• —totaltrainbatch_size: 576
• —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: linear
• —num_epochs: 2
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

• —Transformers 5.5.4
• —Pytorch 2.11.0+cu128
• —Datasets 4.8.4
• —Tokenizers 0.22.2