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