terrencewee12/xlm-roberta-base-sentiment-multilingual-finetuned-v2

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).