Instructions to use VMware/electra-large-mrqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VMware/electra-large-mrqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="VMware/electra-large-mrqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("VMware/electra-large-mrqa") model = AutoModelForQuestionAnswering.from_pretrained("VMware/electra-large-mrqa") - Notebooks
- Google Colab
- Kaggle
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README.md
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- **Parent Model:** [ELCTRA-Large-Discriminator](https://huggingface.co/google/electra-large-discriminator)
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- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
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- **Training data size:** 516,819 examples
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- **Training time:**
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- **Language:** English
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- **Framework:** PyTorch
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- **Model version:** 1.0
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- **Parent Model:** [ELCTRA-Large-Discriminator](https://huggingface.co/google/electra-large-discriminator)
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- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
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- **Training data size:** 516,819 examples
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- **Training time:** 28:31:59 on 1 Nvidia V100 32GB GPU
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- **Language:** English
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- **Framework:** PyTorch
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- **Model version:** 1.0
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