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README.md

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+
+---
+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](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 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](https://huggingface.co/datasets/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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+