Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

MU-NLPC
/
XLM-R-large-reflective-conf4

Text Classification
Transformers
PyTorch
Safetensors
xlm-roberta
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

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
XLM-R-large-reflective-conf4
4.48 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 4 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
366f850 almost 3 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • README.md
    3.46 kB
    Create README.md almost 3 years ago
  • config.json
    1.18 kB
    Upload XLMRobertaForSequenceClassification almost 3 years ago
  • model.safetensors
    2.24 GB
    xet
    Adding `safetensors` variant of this model almost 3 years ago
  • pytorch_model.bin
    2.24 GB
    xet
    Upload XLMRobertaForSequenceClassification almost 3 years ago