{}
language:
- ms tags:
- sentiment-analysis
- text-classification
- multilingual license: apache-2.0 datasets:
- tyqiangz/multilingual-sentiments metrics:
- accuracy model-index:
- name: xlm-roberta-base-sentiment-multilingual-finetuned
results:
task: type: text-classification name: Text Classification dataset: chinese: scfengv/TVL_Sentiment_Analysis malay : tyqiangz/multilingual-sentiments", "malay" english: "argilla/twitter-coronavirus"
metrics:
- type: accuracy value: 0.7244
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 Malay. 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-sentiments dataset.
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
est results: {'eval_loss': 0.6420313119888306, 'eval_accuracy': 0.7243781094527363, 'eval_f1': 0.712778066703921, 'eval_precision': 0.7391632387942287, 'eval_recall': 0.7243781094527363, 'eval_runtime': 4.681, 'eval_samples_per_second': 214.696, 'eval_steps_per_second': 3.418, 'epoch': 2.0}
Environmental impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).