metadata

language:
  - en
license: mit
tags:
  - generated_from_trainer
  - nlu
  - domain-classificatoin
  - 'arxiv: 2310.16609'
datasets:
  - AmazonScience/massive
metrics:
  - accuracy
  - f1
base_model: xlm-roberta-base
model-index:
  - name: xlm-r-base-amazon-massive-domain
    results:
      - task:
          type: text-classification
          name: text-classification
        dataset:
          name: MASSIVE
          type: AmazonScience/massive
          split: test
        metrics:
          - type: f1
            value: 0.9213
            name: F1

xlm-r-base-amazon-massive-domain

This model is a fine-tuned version of xlm-roberta-base on the Amazon Massive dataset (only en-US subset). It achieves the following results on the evaluation set:

Loss: 0.3788
Accuracy: 0.9213
F1: 0.9213

Model description

Domain classifier trained from Amazon Massive dataset.

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
1.382	1.0	720	0.4533	0.8795	0.8795
0.4598	2.0	1440	0.3448	0.9026	0.9026
0.2547	3.0	2160	0.3762	0.9065	0.9065
0.1986	4.0	2880	0.3748	0.9139	0.9139
0.1358	5.0	3600	0.3788	0.9213	0.9213

Framework versions

Transformers 4.22.1
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},
}