Instructions to use slimaneMakh/superClass_multiLang_tableClf_14jun_studentMulti14jun-XLMR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slimaneMakh/superClass_multiLang_tableClf_14jun_studentMulti14jun-XLMR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="slimaneMakh/superClass_multiLang_tableClf_14jun_studentMulti14jun-XLMR")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("slimaneMakh/superClass_multiLang_tableClf_14jun_studentMulti14jun-XLMR") model = AutoModelForSequenceClassification.from_pretrained("slimaneMakh/superClass_multiLang_tableClf_14jun_studentMulti14jun-XLMR") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd8b244b613f7189b1547758d849817b284f0f3ae52113a915789f927ee4b164
- Size of remote file:
- 17.1 MB
- SHA256:
- 3a56def25aa40facc030ea8b0b87f3688e4b3c39eb8b45d5702b3a1300fe2a20
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