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---
license: mit
tags:
- generated_from_trainer
- nlu
- intent-classification
- text-classification
metrics:
- accuracy
- f1
model-index:
- name: xlm-r-base-amazon-massive-intent-label_smoothing
results:
- task:
name: text-classification
type: text-classification
dataset:
name: MASSIVE
type: AmazonScience/massive
split: test
metrics:
- name: F1
type: f1
value: 0.8879
datasets:
- AmazonScience/massive
language:
- en
---
<!-- 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 |