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", device_map="auto")# 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
Commit ·
27232e5
1
Parent(s): de54e91
Adding `safetensors` variant of this model (#1)
Browse files- Adding `safetensors` variant of this model (366f85032f76bae8793e1cda05d85c67b39dae33)
Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>
- model.safetensors +3 -0
model.safetensors
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oid sha256:b4caad920ad10066f71fb6a86494cb2dba40bbe3996a3e107218fc6c3569f1aa
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