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. The model was trained to classify texts into 2 categories: [Positive, Negative]. It can process texts up to 512 tokens and performs well on khmer text inputs. - Task: Sentiment analysis (binary classification). - Languages Supported: [List languages, e.g., English, Khmer, etc.]. - 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 [Number] 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()])