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

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.1445	1.0	720	0.5446	0.6681	0.6770	0.6725	0.8842
0.5908	2.0	1440	0.3869	0.7331	0.7706	0.7514	0.9083
0.3228	3.0	2160	0.3285	0.7658	0.8288	0.7961	0.9219
0.2561	4.0	2880	0.3063	0.7819	0.8402	0.8100	0.9257
0.1808	5.0	3600	0.3000	0.8011	0.8429	0.8214	0.9305
0.1487	6.0	4320	0.2982	0.8201	0.8492	0.8344	0.9361
0.1156	7.0	5040	0.3252	0.8009	0.8569	0.8280	0.9313
0.094	8.0	5760	0.3481	0.8127	0.8502	0.8310	0.9333
0.0843	9.0	6480	0.3764	0.7990	0.8613	0.8290	0.9304
0.0641	10.0	7200	0.3822	0.7930	0.8609	0.8256	0.9280
0.0547	11.0	7920	0.3889	0.8223	0.8649	0.8431	0.9354
0.04	12.0	8640	0.4416	0.8019	0.8633	0.8314	0.9288
0.0368	13.0	9360	0.4339	0.8117	0.8606	0.8354	0.9328
0.0297	14.0	10080	0.4698	0.8062	0.8623	0.8333	0.9314
0.0227	15.0	10800	0.4763	0.8058	0.8656	0.8346	0.9327
0.0185	16.0	11520	0.4793	0.8124	0.8613	0.8361	0.9326
0.0182	17.0	12240	0.4835	0.8191	0.8629	0.8404	0.9341
0.0147	18.0	12960	0.4981	0.8140	0.8693	0.8407	0.9336
0.0111	19.0	13680	0.5002	0.8099	0.8719	0.8398	0.9340
0.0128	20.0	14400	0.5006	0.8144	0.8683	0.8405	0.9333

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},
}