PLOD-AbbreviationDetection
nlp
| PLOD-AbbreviationDetection | nlp | |
|---|---|---|
| 1 | 1 | |
| 9 | 0 | |
| - | - | |
| 0.0 | 6.0 | |
| over 1 year ago | 10 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Creative Commons Attribution Share Alike 4.0 | - |
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PLOD-AbbreviationDetection
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Clustering to find abbreviations
Finally, the main problem with unsupervised learning is that you won't be able to reliably measure system performance or improvement. In my view, any time you can spend annotating and collecting data for a (semi-)supervised solution will be well-spent. Existing datasets can also get you started with model development, such as https://github.com/surrey-nlp/PLOD-AbbreviationDetection. Once you have a good model on a conventional dataset, you should be able to start generalizing it to your specific task/dataset.
nlp
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[N] State of GPT by Andrej karpathy in MSBuild 2023
GitHub: https://github.com/iliyaML/nlp/tree/main/microsoft-build-2023/state-of-gpt
What are some alternatives?
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