Text Classification
Transformers
Safetensors
English
Urdu
xlm-roberta
emotion-detection
multilingual
roman-urdu
code-mixed
pakistan
accuracy
f1
text-embeddings-inference
Instructions to use muhammadsuleman1533/xlm-roberta-multilingual-emotion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muhammadsuleman1533/xlm-roberta-multilingual-emotion-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="muhammadsuleman1533/xlm-roberta-multilingual-emotion-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("muhammadsuleman1533/xlm-roberta-multilingual-emotion-classifier") model = AutoModelForSequenceClassification.from_pretrained("muhammadsuleman1533/xlm-roberta-multilingual-emotion-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa2c7481a642580a0a0df2b70899a179c680df971c0dc5a49854ade114ee7f62
- Size of remote file:
- 16.8 MB
- SHA256:
- e5b633524ba90477daaba16ec27580a08a2856ae0ee8c33d9f5f9358378d3b35
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