Instructions to use sheduele/testXLMMULTI3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sheduele/testXLMMULTI3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sheduele/testXLMMULTI3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sheduele/testXLMMULTI3") model = AutoModelForSequenceClassification.from_pretrained("sheduele/testXLMMULTI3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49de5a8c896247a3a7b085cdf092a6152e3468adf81e3fd68206ab7b37300539
- Size of remote file:
- 17.1 MB
- SHA256:
- 68249140948a4db3cc4a2fb6dde95be5cea632520bee6f6cd86e4bd3e2e654bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.