metadata
license: mit
language:
- km
base_model:
- FacebookAI/xlm-roberta-base
pipeline_tag: text-classification
library_name: transformers
tags:
- sentiment
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()])