xlm-r-base-amazon-massive-intent
This model is a fine-tuned version of xlm-roberta-base on Amazon Massive dataset (only en-US subset). It achieves the following results on the evaluation set:
- Loss: 0.5439
- Accuracy: 0.8775
- F1: 0.8775
Results
| domain | train-accuracy | test-accuracy |
|---|---|---|
| alarm | 0.967 | 0.9846 |
| audio | 0.7458 | 0.659 |
| calendar | 0.9797 | 0.3181 |
| cooking | 0.9714 | 0.9571 |
| datetime | 0.9777 | 0.9402 |
| 0.9727 | 0.9296 | |
| general | 0.8952 | 0.5949 |
| iot | 0.9329 | 0.9122 |
| list | 0.9792 | 0.9538 |
| music | 0.9355 | 0.8837 |
| news | 0.9607 | 0.8764 |
| play | 0.9419 | 0.874 |
| qa | 0.9677 | 0.8591 |
| recommendation | 0.9515 | 0.8764 |
| social | 0.9671 | 0.8932 |
| takeaway | 0.9192 | 0.8478 |
| transport | 0.9425 | 0.9193 |
| weather | 0.9895 | 0.93 |
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 2.734 | 1.0 | 720 | 1.1883 | 0.7196 | 0.7196 |
| 1.2774 | 2.0 | 1440 | 0.7162 | 0.8342 | 0.8342 |
| 0.6301 | 3.0 | 2160 | 0.5817 | 0.8672 | 0.8672 |
| 0.4901 | 4.0 | 2880 | 0.5555 | 0.8770 | 0.8770 |
| 0.3398 | 5.0 | 3600 | 0.5439 | 0.8775 | 0.8775 |
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},
}
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