---
language:
- en
license: mit
tags:
- generated_from_trainer
- nlu
- intent-classification
- text-classification
datasets:
- AmazonScience/massive
metrics:
- accuracy
- f1
base_model: xlm-roberta-base
model-index:
- name: xlm-r-base-amazon-massive-intent-label_smoothing
  results:
  - task:
      type: intent-classification
      name: intent-classification
    dataset:
      name: MASSIVE
      type: AmazonScience/massive
      split: test
    metrics:
    - type: f1
      value: 0.8879
      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-label_smoothing

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: 2.5148
- Accuracy: 0.8879
- F1: 0.8879

## 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
- label_smoothing_factor: 0.4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 3.3945        | 1.0   | 720  | 2.7175          | 0.7900   | 0.7900 |
| 2.7629        | 2.0   | 1440 | 2.5660          | 0.8549   | 0.8549 |
| 2.5143        | 3.0   | 2160 | 2.5389          | 0.8711   | 0.8711 |
| 2.4678        | 4.0   | 2880 | 2.5172          | 0.8883   | 0.8883 |
| 2.4187        | 5.0   | 3600 | 2.5148          | 0.8879   | 0.8879 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2