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---
language:
- en
- ms
- zh
tags:
- sentiment-analysis
- text-classification
- multilingual
license: apache-2.0
datasets:
- tyqiangz/multilingual-sentiments
- scfengv/TVL_Sentiment_Analysis
- 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](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.
## 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](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.
## 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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