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
Commit History
30 days ago
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fix: set library_name to transformers for HF Inference API compatibility
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Re-add full merged model weights for HF Inference API
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Add onnx/model_quantized.onnx
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Update README: declare pipeline_tag + PEFT tags for Inference API
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Remove full model weights — use PEFT adapter instead (saves 1 GB, enables free HF Inference API)
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Add adapter_model.safetensors
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Add adapter_config.json
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