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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
4.5 GB
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  • 2 contributors
History: 7 commits
michal-stefanik's picture
michal-stefanik
Upload tokenizer
6c012cc almost 3 years ago
  • .gitattributes
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  • README.md
    3.62 kB
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  • config.json
    1.18 kB
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  • model.safetensors
    2.24 GB
    xet
    Adding `safetensors` variant of this model (#1) almost 3 years ago
  • pytorch_model.bin
    2.24 GB
    xet
    Upload XLMRobertaForSequenceClassification almost 3 years ago
  • sentencepiece.bpe.model
    5.07 MB
    xet
    Upload tokenizer almost 3 years ago
  • special_tokens_map.json
    167 Bytes
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  • tokenizer.json
    17.1 MB
    xet
    Upload tokenizer almost 3 years ago
  • tokenizer_config.json
    443 Bytes
    Upload tokenizer almost 3 years ago