---
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},
}
```