PLOD-AbbreviationDetection
transformers-interpret
| PLOD-AbbreviationDetection | transformers-interpret | |
|---|---|---|
| 1 | 3 | |
| 9 | 1,213 | |
| - | - | |
| 0.0 | 2.9 | |
| over 1 year ago | 8 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Creative Commons Attribution Share Alike 4.0 | Apache License 2.0 |
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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.
transformers-interpret
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[P] XAI Recipes for the HuggingFace 🤗 Image Classification Models
Very cool, I like seeing this. I also noticed the transformers interpret package has released support for an image classification explainer: https://github.com/cdpierse/transformers-interpret
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Using LIME to explain the predictions from a BERT model, it looks like "the", "and", "or" are "very important" features, and thus I don't think the model is learning anything interesting. Any tips?
You could look at the Transformers Interpret python library: https://github.com/cdpierse/transformers-interpret
- Show HN: Transformers Interpret – Explain and visualize Transformer models
What are some alternatives?
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