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--- |
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language: |
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- en |
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license: mit |
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tags: |
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- generated_from_trainer |
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- nlu |
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- intent-classification |
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datasets: |
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- AmazonScience/massive |
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metrics: |
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- accuracy |
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- f1 |
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base_model: xlm-roberta-base |
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model-index: |
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- name: xlm-r-base-amazon-massive-intent |
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results: |
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- task: |
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type: intent-classification |
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name: intent-classification |
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dataset: |
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name: MASSIVE |
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type: AmazonScience/massive |
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split: test |
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metrics: |
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- type: f1 |
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value: 0.8775 |
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name: F1 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# xlm-r-base-amazon-massive-intent |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on |
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[Amazon Massive](https://huggingface.co/datasets/AmazonScience/massive) dataset (only en-US subset). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5439 |
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- Accuracy: 0.8775 |
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- F1: 0.8775 |
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## Results |
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| domain | train-accuracy | test-accuracy | |
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|:------:|:--------------:|:-------------:| |
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|alarm|0.967|0.9846| |
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|audio|0.7458|0.659| |
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|calendar|0.9797|0.3181| |
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|cooking|0.9714|0.9571| |
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|datetime|0.9777|0.9402| |
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|email|0.9727|0.9296| |
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|general|0.8952|0.5949| |
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|iot|0.9329|0.9122| |
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|list|0.9792|0.9538| |
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|music|0.9355|0.8837| |
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|news|0.9607|0.8764| |
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|play|0.9419|0.874| |
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|qa|0.9677|0.8591| |
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|recommendation|0.9515|0.8764| |
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|social|0.9671|0.8932| |
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|takeaway|0.9192|0.8478| |
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|transport|0.9425|0.9193| |
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|weather|0.9895|0.93| |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 2.734 | 1.0 | 720 | 1.1883 | 0.7196 | 0.7196 | |
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| 1.2774 | 2.0 | 1440 | 0.7162 | 0.8342 | 0.8342 | |
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| 0.6301 | 3.0 | 2160 | 0.5817 | 0.8672 | 0.8672 | |
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| 0.4901 | 4.0 | 2880 | 0.5555 | 0.8770 | 0.8770 | |
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| 0.3398 | 5.0 | 3600 | 0.5439 | 0.8775 | 0.8775 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.5.1 |
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- Tokenizers 0.12.1 |
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## Citation |
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```bibtex |
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@article{kubis2023back, |
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title={Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors}, |
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author={Kubis, Marek and Sk{\'o}rzewski, Pawe{\l} and Sowa{\'n}ski, Marcin and Zi{\k{e}}tkiewicz, Tomasz}, |
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journal={arXiv preprint arXiv:2310.16609}, |
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year={2023} |
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eprint={2310.16609}, |
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archivePrefix={arXiv}, |
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} |
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``` |
