Instructions to use mbartolo/electra-large-synqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbartolo/electra-large-synqa 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="mbartolo/electra-large-synqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mbartolo/electra-large-synqa") model = AutoModelForQuestionAnswering.from_pretrained("mbartolo/electra-large-synqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Added model card YAML
Browse files
README.md
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# Model Overview
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This is an ELECTRA-Large QA Model trained from https://huggingface.co/google/electra-large-discriminator in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator, and then it is trained on SQuAD and AdversarialQA (https://arxiv.org/abs/2002.00293) in a second stage of fine-tuning.
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---
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language:
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- en
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tags:
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- question-answering
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license: "apache-2.0"
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datasets:
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- adversarial_qa
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- mbartolo/synQA
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- squad
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metrics:
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- exact_match
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- f1
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---
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# Model Overview
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This is an ELECTRA-Large QA Model trained from https://huggingface.co/google/electra-large-discriminator in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator, and then it is trained on SQuAD and AdversarialQA (https://arxiv.org/abs/2002.00293) in a second stage of fine-tuning.
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