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--- |
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language: |
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- en |
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- ms |
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- zh |
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tags: |
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- sentiment-analysis |
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- text-classification |
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- multilingual |
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license: apache-2.0 |
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datasets: |
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- tyqiangz/multilingual-sentiments |
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- scfengv/TVL_Sentiment_Analysis |
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- argilla/twitter-coronavirus |
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metrics: |
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- accuracy |
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model-index: |
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- name: xlm-roberta-base-sentiment-multilingual-finetuned |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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metrics: |
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- type: accuracy |
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value: 0.8444 |
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--- |
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# xlm-roberta-base-sentiment-multilingual-finetuned |
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## Model description |
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This is a fine-tuned version of the [cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual) model, trained on the [tyqiangz/multilingual-sentiments](https://huggingface.co/datasets/tyqiangz/multilingual-sentiments) dataset. It's designed for multilingual sentiment analysis in English, Malay, and Chinese. |
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## Intended uses & limitations |
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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. |
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## Training and evaluation data |
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The model was trained and evaluated on the [tyqiangz/multilingual-sentiments](https://huggingface.co/datasets/tyqiangz/multilingual-sentiments)[TVL_Sentiment_Analysis](https://huggingface.co/datasets/scfengv/TVL_Sentiment_Analysis) , [argilla/twitter-coronavirus](https://huggingface.co/datasets/argilla/twitter-coronavirus) datasets, which includes data in English, Malay, and Chinese. |
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## Training procedure |
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The model was fine-tuned using the Hugging Face Transformers library. |
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training_args = TrainingArguments( |
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output_dir="./results", |
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num_train_epochs=2, |
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per_device_train_batch_size=16, |
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per_device_eval_batch_size=64, |
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warmup_steps=500, |
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weight_decay=0.01, |
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logging_dir='./logs', |
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logging_steps=10, |
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evaluation_strategy="steps", |
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save_strategy="steps", |
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load_best_model_at_end=True, |
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) |
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## Evaluation results |
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Test results: {'eval_loss': 0.5881872177124023, 'eval_accuracy': 0.8443683409436834, 'eval_f1': 0.8438625655671501, 'eval_precision': 0.8438352235376211, 'eval_recall': 0.8443683409436834} |
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## Environmental impact |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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