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xlm-roberta-base-finetuned-massive
This model is a fine-tuned version of 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
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
