Text Classification
Transformers
ONNX
Safetensors
English
Tagalog
xlm-roberta
sentiment-analysis
filipino
taglish
text-embeddings-inference
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("cjcastro/xlm-roberta-Rise-On-AI")
model = AutoModelForSequenceClassification.from_pretrained("cjcastro/xlm-roberta-Rise-On-AI", device_map="auto")Quick Links
XLM-RoBERTa Rise On AI — Sentiment Analysis
Fine-tuned XLM-RoBERTa for Filipino/English/Taglish 3-class sentiment.
Labels: positive · negative · distress
Accuracy: 82.6% · F1 macro: 0.815
Usage
from transformers import pipeline
clf = pipeline('text-classification', model='cjcastro/xlm-roberta-Rise-On-AI', top_k=None)
print(clf('I feel happy today!'))
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cjcastro/xlm-roberta-Rise-On-AI")