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123tarunanand
/
roberta-base-finetuned

Question Answering
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Model card Files Files and versions
xet
Metrics Training metrics Community
6

Instructions to use 123tarunanand/roberta-base-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use 123tarunanand/roberta-base-finetuned with Transformers:

    # Use a pipeline as a high-level helper
    # Warning: Pipeline type "question-answering" is no longer supported in transformers v5.
    # You must load the model directly (see below) or downgrade to v4.x with:
    # pip install "transformers<5.0.0"
    from transformers import pipeline
    
    pipe = pipeline("question-answering", model="123tarunanand/roberta-base-finetuned")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForQuestionAnswering
    
    tokenizer = AutoTokenizer.from_pretrained("123tarunanand/roberta-base-finetuned")
    model = AutoModelForQuestionAnswering.from_pretrained("123tarunanand/roberta-base-finetuned", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Add evaluation results on the adversarialQA config and validation split of adversarial_qa

#6 opened about 3 years ago by
autoevaluator

Add evaluation results on the adversarialQA config and validation split of adversarial_qa

#5 opened about 3 years ago by
autoevaluator

Adding `safetensors` variant of this model

#4 opened over 3 years ago by
SFconvertbot

Add evaluation results on the default config and test split of cuad

1
#3 opened almost 4 years ago by
autoevaluator

Add evaluation results on the adversarialQA config and validation split of adversarial_qa

#2 opened almost 4 years ago by
autoevaluator

Add evaluation results on the squad_v2 config of squad_v2

#1 opened about 4 years ago by
autoevaluator
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