Instructions to use cgt/pert-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cgt/pert-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="cgt/pert-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("cgt/pert-qa") model = AutoModelForQuestionAnswering.from_pretrained("cgt/pert-qa") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#1
by librarian-bot - opened
README.md
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- generated_from_trainer
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datasets:
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- cmrc2018
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model-index:
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- name: pert-qa
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results: []
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- generated_from_trainer
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datasets:
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- cmrc2018
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base_model: hfl/chinese-pert-large
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model-index:
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- name: pert-qa
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results: []
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