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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b3549b33628177de78f5f8e2344627cc76dfbaabbf016e37e03c75a361319f7
- Size of remote file:
- 2.24 GB
- SHA256:
- b4caad920ad10066f71fb6a86494cb2dba40bbe3996a3e107218fc6c3569f1aa
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