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:
- 54d0380f35b88f3b478c3c687f7cc1df2dfacea073386fefed9206c65bfb666b
- Size of remote file:
- 14.7 kB
- SHA256:
- f1c6b256cd81fd480f32700ceb66fa202f3af47e796a06f16014375bc590f279
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.