Modelpublic

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

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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Model Card

language:

—en
—ms
—zh 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: type: tyqiangz/multilingual-sentiments name: Multilingual Sentiments metrics:
—type: accuracy value: 0.7528205128205128

Baseline Scores: Classification Report: Negative: Precision: 0.6153 Recall: 0.8292 F1-score: 0.7064 Support: 1680 Neutral: Precision: 0.5381 Recall: 0.3035 F1-score: 0.3881 Support: 1443 Positive: Precision: 0.7607 Recall: 0.7803 F1-score: 0.7704 Support: 1752 Metrics: Accuracy: Value: 0.6560 Support: 4875 Macro Avg: Value: 0.6380 Support: 4875 Weighted Avg: Value: 0.6447 Support: 4875

Finetuned Scores: Classification Report: Negative: Precision: 0.7487 Recall: 0.7875 F1-score: 0.7676 Support: 1680 Neutral: Precision: 0.6775 Recall: 0.6216 F1-score: 0.6484 Support: 1443 Positive: Precision: 0.8128 Recall: 0.8276 F1-score: 0.8201 Support: 1752 Metrics: Accuracy: Value: 0.7528 Support: 4875 Macro Avg: Value: 0.7463 Support: 4875 Weighted Avg: Value: 0.7507 Support: 4875 ---

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-sentiments dataset, 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=5, perdevicetrainbatchsize=16, perdeviceevalbatchsize=64, warmupsteps=500, weightdecay=0.01, loggingdir='./logs', loggingsteps=10, evaluationstrategy="epoch", savestrategy="epoch", loadbestmodelatend=True, )

Evaluation results

'evalaccuracy': 0.7528205128205128, 'evalf1': 0.7511924805177581, 'evalprecision': 0.7506612130427309, 'evalrecall': 0.7528205128205128

Test Score :

Environmental impact

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