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

language:

• —en
• —ms
• —zh tags:
• —sentiment-analysis
• —text-classification
• —multilingual license: apache-2.0 datasets:
• —tyqiangz/multilingual-sentiments
• —scfengv/TVLSentimentAnalysis
• —argilla/twitter-coronavirus metrics:
• —accuracy model-index:
• —name: xlm-roberta-base-sentiment-multilingual-finetuned results:
• —task: type: text-classification name: Text Classification metrics:
• —type: accuracy value: 0.8444 ---

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.

trainingargs = TrainingArguments( outputdir="./results", numtrainepochs=2, perdevicetrainbatchsize=16, perdeviceevalbatchsize=64, warmupsteps=500, weightdecay=0.01, loggingdir='./logs', loggingsteps=10, evaluationstrategy="steps", savestrategy="steps", loadbestmodelatend=True, )

Evaluation results

Test results: {'evalloss': 0.5881872177124023, 'evalaccuracy': 0.8443683409436834, 'evalf1': 0.8438625655671501, 'evalprecision': 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).