Update README.md

README.md

@@ -6,4 +6,43 @@ 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
+```python
+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()])