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README.md
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useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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classifier using the features produced by the BERT model as inputs.
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## Intended uses & limitations
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This model should be used as a question-answering model. You may use it in a question answering pipeline, or use it to output raw results given a query and a context. You may see other use cases in the [task summary](https://huggingface.co/transformers/task_summary.html#extractive-question-answering) of the transformers documentation.## Training data
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useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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classifier using the features produced by the BERT model as inputs.
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This model has the following configuration:
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- 24-layer
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- 1024 hidden dimension
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- 16 attention heads
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- 336M parameters.
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## Intended uses & limitations
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This model should be used as a question-answering model. You may use it in a question answering pipeline, or use it to output raw results given a query and a context. You may see other use cases in the [task summary](https://huggingface.co/transformers/task_summary.html#extractive-question-answering) of the transformers documentation.## Training data
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