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.

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.

The model was trained and evaluated on the tyqiangz/multilingual-sentiments dataset, which includes data in English, Malay, and Chinese.

The model was fine-tuned using the Hugging Face Transformers library.

training_args = TrainingArguments( output_dir="./results", num_train_epochs=5, 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="epoch", save_strategy="epoch", load_best_model_at_end=True, )

'eval_accuracy': 0.7528205128205128, 'eval_f1': 0.7511924805177581, 'eval_precision': 0.7506612130427309, 'eval_recall': 0.7528205128205128

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).