---
license: mit
language:
  - ja
base_model: xlm-roberta-base
tags:
- generated_from_trainer
- massive
- bert
datasets:
- AmazonScience/massive
widget:
  - text: 明日の予定を教えて
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-finetuned-massive
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: massive
      type: massive
      config: ja-JP
      split: validation
      args: ja-JP
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8327594687653713
    - name: F1
      type: f1
      value: 0.8192120367052886
---

<!-- 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-roberta-base-finetuned-massive

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: 0.7539
- Accuracy: 0.8328
- F1: 0.8192

## Model description

More information needed

## Intended uses & limitations

```python
from transformers import pipeline

model_name = "thkkvui/xlm-roberta-base-finetuned-massive"
classifier = pipeline("text-classification", model=model_name)

text = ["今日の天気を教えて", "ニュースある？", "予定をチェックして", "ドル円は？"]

for t in text:
    output = classifier(t)
    print(output)
```

## 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
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.9836        | 0.69  | 500  | 1.6188          | 0.6257   | 0.5524 |
| 1.4569        | 1.39  | 1000 | 1.0347          | 0.7575   | 0.7251 |
| 1.0211        | 2.08  | 1500 | 0.8186          | 0.8205   | 0.8024 |
| 0.7799        | 2.78  | 2000 | 0.7539          | 0.8328   | 0.8192 |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3