---
language:
- en
license: mit
tags:
- generated_from_trainer
- nlu
- slot-tagging
datasets:
- AmazonScience/massive
metrics:
- precision
- recall
- f1
- accuracy
base_model: xlm-roberta-base
model-index:
- name: xlm-r-base-amazon-massive-slot
  results:
  - task:
      type: slot-filling
      name: slot-filling
    dataset:
      name: MASSIVE
      type: AmazonScience/massive
      split: test
    metrics:
    - type: f1
      value: 0.8405
      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-slot

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5006
- Precision: 0.8144
- Recall: 0.8683
- F1: 0.8405
- Accuracy: 0.9333

## 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: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.1445        | 1.0   | 720   | 0.5446          | 0.6681    | 0.6770 | 0.6725 | 0.8842   |
| 0.5908        | 2.0   | 1440  | 0.3869          | 0.7331    | 0.7706 | 0.7514 | 0.9083   |
| 0.3228        | 3.0   | 2160  | 0.3285          | 0.7658    | 0.8288 | 0.7961 | 0.9219   |
| 0.2561        | 4.0   | 2880  | 0.3063          | 0.7819    | 0.8402 | 0.8100 | 0.9257   |
| 0.1808        | 5.0   | 3600  | 0.3000          | 0.8011    | 0.8429 | 0.8214 | 0.9305   |
| 0.1487        | 6.0   | 4320  | 0.2982          | 0.8201    | 0.8492 | 0.8344 | 0.9361   |
| 0.1156        | 7.0   | 5040  | 0.3252          | 0.8009    | 0.8569 | 0.8280 | 0.9313   |
| 0.094         | 8.0   | 5760  | 0.3481          | 0.8127    | 0.8502 | 0.8310 | 0.9333   |
| 0.0843        | 9.0   | 6480  | 0.3764          | 0.7990    | 0.8613 | 0.8290 | 0.9304   |
| 0.0641        | 10.0  | 7200  | 0.3822          | 0.7930    | 0.8609 | 0.8256 | 0.9280   |
| 0.0547        | 11.0  | 7920  | 0.3889          | 0.8223    | 0.8649 | 0.8431 | 0.9354   |
| 0.04          | 12.0  | 8640  | 0.4416          | 0.8019    | 0.8633 | 0.8314 | 0.9288   |
| 0.0368        | 13.0  | 9360  | 0.4339          | 0.8117    | 0.8606 | 0.8354 | 0.9328   |
| 0.0297        | 14.0  | 10080 | 0.4698          | 0.8062    | 0.8623 | 0.8333 | 0.9314   |
| 0.0227        | 15.0  | 10800 | 0.4763          | 0.8058    | 0.8656 | 0.8346 | 0.9327   |
| 0.0185        | 16.0  | 11520 | 0.4793          | 0.8124    | 0.8613 | 0.8361 | 0.9326   |
| 0.0182        | 17.0  | 12240 | 0.4835          | 0.8191    | 0.8629 | 0.8404 | 0.9341   |
| 0.0147        | 18.0  | 12960 | 0.4981          | 0.8140    | 0.8693 | 0.8407 | 0.9336   |
| 0.0111        | 19.0  | 13680 | 0.5002          | 0.8099    | 0.8719 | 0.8398 | 0.9340   |
| 0.0128        | 20.0  | 14400 | 0.5006          | 0.8144    | 0.8683 | 0.8405 | 0.9333   |


### Framework versions

- Transformers 4.22.2
- 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},
}
```