tykea/khmer-text-sentiment-analysis-roberta

This is a fine-tuned version of the XLM-RoBERTa model for sentiment analysis to classify khmer texts into 2 categories; Postive and Negative.

It can process texts up to 512 tokens and performs well on khmer text inputs.

• —Task: Sentiment analysis (binary classification).
• —Languages Supported: Khmer.
• —Intended Use Cases:
• —Analyzing customer reviews.
• —Social media sentiment detection.
• —Limitations:
• —Performance may degrade on languages or domains not present in the training data.
• —Does not handle sarcasm or highly ambiguous inputs well.
• — The model was evaluated on a test set of 400 samples, achieving the following performance:

• —Test Accuracy: 83.25%
• —Precision: 83.55%
• —Recall: 83.25%
• —F1 Score: 83.25%

Confusion Matrix: | Predicted\Actual | Negative | Positive | |-------------------|----------|----------| | Negative | 166 | 42 | | Positive | 25 | 167 | The model supports a maximum sequence length of 512 tokens.

How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("tykea/khmer-text-sentiment-analysis-roberta")
model = AutoModelForSequenceClassification.from_pretrained("tykea/khmer-text-sentiment-analysis-roberta")

text = "អគុណCADT"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)
predictions = outputs.logits.argmax(dim=1)
labels_mapping = {0: 'negative', 1: 'positive'}
print("Predicted Class:", labels_mapping[predictions.item()])