Comments (5)
Leaving as-is for now, we should talk to an NLP expert first to see how models are normally used.
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The input to the model should remain text, but the functions to create tokens from the input string will be made part of the model. The goal is to avoid having to define a lot of boilerplate code in the dianna repository just to be able to run the model.
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Draft PR in the exploration repo: dianna-ai/dianna-exploration#147
The pytorch model is working, but after conversion to onnx it fails because there are no inputs.
Possibly related issue: pytorch/pytorch#44299
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We will not use the simplified model as it doesn't work. This issue can be closed when the simplifications that don't affect the onnx model itself are merged in dianna-exploration.
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@loostrum could you, please, check if this can be closed?
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Related Issues (20)
- Text Experiments (qualitative): RISE HOT 1
- Text Experiments (quantitative): RISE HOT 1
- Caching for github actions HOT 2
- Decide on time series format support HOT 1
- Implement time series masking functionality for RISE (and LIME)
- Implement RISE functionality
- Create issues from dashboard mockup
- Create release
- Text Experiments (quantitative): LIME HOT 1
- Image Experiments (qualitative): LIME HOT 1
- Image Experiments (qualitative): RISE HOT 1
- Image Experiments (qualitative): KernelSHAP HOT 1
- Image Experiments (quantitative): LIME HOT 1
- Image Experiments (quantitative): RISE HOT 1
- Image Experiments (quantitative): KernelSHAP HOT 1
- Literature review for XAI methods (lime, shap, rise) HOT 3
- Self implement lime methodology for time-series
- Code formatting and pre-commit hooks HOT 1
- pylint configuration
- isort import error
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