xlm-roberta-base-sentiment-multilingual-finetuned
Model description
This is a fine-tuned version of the
cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual model, trained on the
tyqiangz/multilingual-sentiments dataset. It's designed for multilingual sentiment analysis in English, Malay, and Chinese.
Intended uses & limitations
This model is intended for sentiment analysis tasks in English, Malay, and Chinese. It can classify text into three sentiment categories: positive, negative, and neutral.
Training and evaluation data
The model was trained and evaluated on the
tyqiangz/multilingual-sentimentsTVL_Sentiment_Analysis ,
argilla/twitter-coronavirus datasets, which includes data in English, Malay, and Chinese.
Training procedure
The model was fine-tuned using the Hugging Face Transformers library.
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=2,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
warmup_steps=500,
weight_decay=0.01,
logging_dir='./logs',
logging_steps=10,
evaluation_strategy="steps",
save_strategy="steps",
load_best_model_at_end=True,
)
Evaluation results
Test results: {'eval_loss': 0.5881872177124023, 'eval_accuracy': 0.8443683409436834, 'eval_f1': 0.8438625655671501, 'eval_precision': 0.8438352235376211, 'eval_recall': 0.8443683409436834}
Environmental impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
