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
Update README: declare pipeline_tag + PEFT tags for Inference API
Browse files
README.md
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license: apache-2.0
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---
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---
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language:
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- en
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- tl
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license: apache-2.0
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tags:
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- text-classification
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- xlm-roberta
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- peft
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- lora
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- sentiment-analysis
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pipeline_tag: text-classification
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base_model: FacebookAI/xlm-roberta-base
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---
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# XLM-RoBERTa Rise On AI — Sentiment Analysis
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Fine-tuned XLM-RoBERTa (LoRA) for Filipino/English/Taglish 3-class sentiment.
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**Labels:** positive · negative · distress
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**Accuracy:** 82.6% · **F1 macro:** 0.815
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## Usage
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```python
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from transformers import pipeline
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clf = pipeline('text-classification', model='cjcastro/xlm-roberta-Rise-On-AI', top_k=None)
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print(clf('I feel happy today!'))
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```
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