---
license: mit
tags:
- generated_from_trainer
- nlu
- domain-classificatoin
metrics:
- accuracy
- f1
model-index:
- name: xlm-r-base-amazon-massive-domain
  results:
  - task:
      name: text-classification
      type: text-classification
    dataset:
      name: MASSIVE
      type: AmazonScience/massive
      split: test
    metrics:
    - name: F1
      type: f1
      value: 0.9213
datasets:
- AmazonScience/massive
language:
- en
arxiv: 2310.16609
---

<!-- 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-domain

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Amazon Massive](https://huggingface.co/datasets/AmazonScience/massive) dataset (only en-US subset).
It achieves the following results on the evaluation set:
- Loss: 0.3788
- Accuracy: 0.9213
- F1: 0.9213

## Model description

Domain classifier trained from Amazon Massive dataset.

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.382         | 1.0   | 720  | 0.4533          | 0.8795   | 0.8795 |
| 0.4598        | 2.0   | 1440 | 0.3448          | 0.9026   | 0.9026 |
| 0.2547        | 3.0   | 2160 | 0.3762          | 0.9065   | 0.9065 |
| 0.1986        | 4.0   | 2880 | 0.3748          | 0.9139   | 0.9139 |
| 0.1358        | 5.0   | 3600 | 0.3788          | 0.9213   | 0.9213 |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1