xlm-r-base-amazon-massive-slot

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xlm-r-base-amazon-massive-slot

This model is a fine-tuned version of xlm-roberta-base on the MASSIVE1.1 dataset. It achieves the following results on the evaluation set:

• Loss: 0.5006
• Precision: 0.8144
• Recall: 0.8683
• F1: 0.8405
• Accuracy: 0.9333

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20

Training results

Framework versions

• Transformers 4.22.2
• Pytorch 1.12.1+cu113
• Datasets 2.5.1
• Tokenizers 0.12.1

Citation

@article{kubis2023back,
  title={Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors},
  author={Kubis, Marek and Sk{\'o}rzewski, Pawe{\l} and Sowa{\'n}ski, Marcin and Zi{\k{e}}tkiewicz, Tomasz},
  journal={arXiv preprint arXiv:2310.16609},
  year={2023}
  eprint={2310.16609},
  archivePrefix={arXiv},
}