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license: mit
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---
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license: mit
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# soft-search
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[![Build Status](https://github.com/PugetSoundClinic-PIT/soft-search/workflows/CI/badge.svg)](https://github.com/PugetSoundClinic-PIT/soft-search/actions)
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[![Documentation](https://github.com/PugetSoundClinic-PIT/soft-search/workflows/Documentation/badge.svg)](https://PugetSoundClinic-PIT.github.io/soft-search)
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searching for software promises in grant applications
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---
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## Installation
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**Stable Release:** `pip install soft-search`<br>
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**Development Head:** `pip install git+https://github.com/PugetSoundClinic-PIT/soft-search.git`
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## Quickstart
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### Apply our Pre-trained Transformer
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```python
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from soft_search import constants, nsf
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from soft_search.label import transformer
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df = nsf.get_nsf_dataset(
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"2016-01-01",
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"2017-01-01",
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dataset_fields=[constants.NSFFields.abstractText],
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)
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predicted = transformer.label(
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df,
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apply_column=constants.NSFFields.abstractText,
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)
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```
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## Documentation
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For full package documentation please visit [PugetSoundClinic-PIT.github.io/soft-search](https://PugetSoundClinic-PIT.github.io/soft-search).
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## Development
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See [CONTRIBUTING.md](CONTRIBUTING.md) for information related to developing the code.
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**MIT License**
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