Create README.md

README.md

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+---
+language: 
+- en
+- tl
+tags:
+- sentiment-analysis
+- filipino
+- english
+- roberta
+- service-reviews
+license: mit
+datasets:
+- custom
+metrics:
+- accuracy
+- f1
+pipeline_tag: text-classification
+---
+
+# HandyHome Sentiment Analysis
+
+This model classifies sentiment in Filipino-English service reviews.
+
+## Model Details
+
+- **Base Model**: RoBERTa
+- **Task**: Sentiment Classification (3 classes)
+- **Languages**: Filipino, English (mixed)
+- **Classes**: 
+  - 0: Negative
+  - 1: Neutral  
+  - 2: Positive
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "YOUR_USERNAME/handyhome-sentiment-roberta"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Predict sentiment
+text = "Magaling yung service, very professional!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.softmax(outputs.logits, dim=1)
+    predicted_class = torch.argmax(predictions, dim=1).item()
+
+labels = ["negative", "neutral", "positive"]
+print(f"Sentiment: {labels[predicted_class]}")
+```
+
+## Training Data
+
+Trained on HandyHome service reviews dataset containing Filipino-English mixed language reviews.