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---
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
|