Text Classification
Transformers
Safetensors
ONNX
Tagalog
English
xlm-roberta
sentiment-analysis
filipino
tagalog
taglish
code-switching
low-resource-language
philippine-languages
adversarial-training
cross-lingual-transfer
Eval Results (legacy)
text-embeddings-inference
Instructions to use jjjardev/tagasenti_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jjjardev/tagasenti_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jjjardev/tagasenti_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jjjardev/tagasenti_model") model = AutoModelForSequenceClassification.from_pretrained("jjjardev/tagasenti_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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