Instructions to use MU-NLPC/XLM-R-large-reflective-conf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MU-NLPC/XLM-R-large-reflective-conf4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MU-NLPC/XLM-R-large-reflective-conf4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MU-NLPC/XLM-R-large-reflective-conf4") model = AutoModelForSequenceClassification.from_pretrained("MU-NLPC/XLM-R-large-reflective-conf4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f8ed5d72cbcc5b4f0d811ddd469c8ad94cb509578e867ece52762a0374e37e8
- Size of remote file:
- 2.24 GB
- SHA256:
- 321e8b90af9ca6a68a9cec205d0edb821a3294e732fd24ad449cc060e519c57b
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