---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: XLMRoberta-base-amazon-massive-Intent
  results: []
widget:
- text: staubsauge den flur
datasets:
- AmazonScience/massive
language:
- en
- ru
---

<!-- 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. -->

# XLMRoberta-base-amazon-massive-Intent

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the MASSIVE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5620
- Accuracy: 0.8751
- F1: 0.8269

## 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: 7e-06
- 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 2.4641        | 1.0   | 1440  | 1.4258          | 0.6709   | 0.4126 |
| 1.1447        | 2.0   | 2880  | 0.8477          | 0.8060   | 0.6318 |
| 0.7437        | 3.0   | 4320  | 0.6688          | 0.8409   | 0.7060 |
| 0.5543        | 4.0   | 5760  | 0.6006          | 0.8601   | 0.7813 |
| 0.4375        | 5.0   | 7200  | 0.5780          | 0.8635   | 0.7937 |
| 0.3763        | 6.0   | 8640  | 0.5748          | 0.8694   | 0.8170 |
| 0.3265        | 7.0   | 10080 | 0.5620          | 0.8751   | 0.8269 |
| 0.2916        | 8.0   | 11520 | 0.5701          | 0.8756   | 0.8260 |
| 0.2628        | 9.0   | 12960 | 0.5728          | 0.8760   | 0.8271 |
| 0.2474        | 10.0  | 14400 | 0.5740          | 0.8770   | 0.8288 |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1