Instructions to use chcaa/grc_odycy_joint_sm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use chcaa/grc_odycy_joint_sm with spaCy:
!pip install https://huggingface.co/chcaa/grc_odycy_joint_sm/resolve/main/grc_odycy_joint_sm-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("grc_odycy_joint_sm") # Importing as module. import grc_odycy_joint_sm nlp = grc_odycy_joint_sm.load() - Notebooks
- Google Colab
- Kaggle
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README.md
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value: 0.6895810956
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| Feature | Description |
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| --- | --- |
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type: f_score
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value: 0.6895810956
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<p align="center">
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<img width="200" src="https://github.com/centre-for-humanities-computing/odyCy/raw/main/docs/_static/logo_with_text_below.svg">
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<div align="center" style="color: #2c5882; font-weight: bold; font-size: 18px; margin-top: -20px;">
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A general-purpose NLP pipeline for Ancient-Greek.
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</div>
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</p>
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[](https://colab.research.google.com/github/centre-for-humanities-computing/odyCy/blob/main/tutorials/01_odycy_getting_started.ipynb#&offline=true&sandboxMode=true)
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Check out our Documentation on [Basic Usage](https://centre-for-humanities-computing.github.io/odyCy/getting_started.html).
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## Performance
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odyCy achieves state of the art performance on multiple tasks on unseen test data from the Universal Dependencies Perseus treebank,
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and performs second best on the PROIEL treebank’s test set on even more tasks.
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In addition performance also seems relatively stable across the two evaluation datasets in comparison with other NLP pipelines.
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For plots and tables on OdyCy's performance, check out the Documentation page on [Performance](https://centre-for-humanities-computing.github.io/odyCy/performance.html)
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| Feature | Description |
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| --- | --- |
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