Text Classification
Transformers
ONNX
Safetensors
English
Tagalog
xlm-roberta
sentiment-analysis
filipino
taglish
text-embeddings-inference
Instructions to use cjcastro/xlm-roberta-Rise-On-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cjcastro/xlm-roberta-Rise-On-AI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cjcastro/xlm-roberta-Rise-On-AI")# 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") - Notebooks
- Google Colab
- Kaggle
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Download README.md from cjcastro/xlm-roberta-Rise-On-AI: direct link, hf CLI and curl.
- Browser
- Download file 594 Bytes
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https://huggingface.co/cjcastro/xlm-roberta-Rise-On-AI/resolve/main/README.md
- Command line
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hf download hf://cjcastro/xlm-roberta-Rise-On-AI/README.md
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curl -L -o README.md https://huggingface.co/cjcastro/xlm-roberta-Rise-On-AI/resolve/main/README.md
594 Bytes
metadata
language:
- en
- tl
license: apache-2.0
library_name: transformers
tags:
- text-classification
- xlm-roberta
- sentiment-analysis
- filipino
- taglish
pipeline_tag: text-classification
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!'))