Text Classification
Transformers
Safetensors
Kabyle
ber
xlm-roberta
kabyle
tamazight
emotion-classification
sentiment-analysis
low-resource
cross-lingual-transfer
text-embeddings-inference
Instructions to use boffire/kabyle-emotion-xlmr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boffire/kabyle-emotion-xlmr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="boffire/kabyle-emotion-xlmr")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("boffire/kabyle-emotion-xlmr") model = AutoModelForSequenceClassification.from_pretrained("boffire/kabyle-emotion-xlmr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12fc2aac0bb0e422d1849f9e716f42c47fb1504c583d3c548663013c19f818e2
- Size of remote file:
- 16.8 MB
- SHA256:
- 7a5451f31fe3f899dcd75ec2ad93f415528c9b5f58bb7a5a1c6dd5884fb56257
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