| --- |
| language: |
| - en |
| license: mit |
| tags: |
| - generated_from_trainer |
| - nlu |
| - intent-classification |
| datasets: |
| - AmazonScience/massive |
| metrics: |
| - accuracy |
| - f1 |
| base_model: xlm-roberta-base |
| model-index: |
| - name: xlm-r-base-amazon-massive-intent |
| results: |
| - task: |
| type: intent-classification |
| name: intent-classification |
| dataset: |
| name: MASSIVE |
| type: AmazonScience/massive |
| split: test |
| metrics: |
| - type: f1 |
| value: 0.8775 |
| name: F1 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # xlm-r-base-amazon-massive-intent |
|
|
| This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on |
| [Amazon Massive](https://huggingface.co/datasets/AmazonScience/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| |
| |email|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 |
| ```bibtex |
| @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}, |
| } |
| ``` |
