metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
  - nlu
  - slot-tagging
datasets:
  - AmazonScience/massive
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: xlm-roberta-base
model-index:
  - name: xlm-r-base-amazon-massive-slot
    results:
      - task:
          type: slot-filling
          name: slot-filling
        dataset:
          name: MASSIVE
          type: AmazonScience/massive
          split: test
        metrics:
          - type: f1
            value: 0.8405
            name: F1

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},
}