|
|
--- |
|
|
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. |
