Comments (1)
Thanks for reaching out. Nothing should stop you from using LIT with an SVM model. To use LIT with your own model/dataset, you just need to define what information is in the dataset, and what the model inputs and outputs are. See https://github.com/PAIR-code/lit/blob/main/docs/python_api.md#adding-models-and-data for more details. The models don't need to be neural networks, or use any specific framework. As long as you can call into the model to perform prediction using python (or even use python to call a server that does the model prediction), then you can use LIT.
What are the input features and outputs of the model you want to analyze?
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Related Issues (20)
- Would LiT support Object Detection Computer Vision models? HOT 1
- Defective pip dependencies: numba, numpy and protobuf HOT 3
- Most important words for false positives / false negatives
- Integration with TransformerLens ?
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- error when using the pytorch demo HOT 2
- How to deploy this on a webserver HOT 2
- Potential performance issue: .to_dict method slow in pandas below 2.2 HOT 1
- Running LIT for a fine-tuned LLM HOT 4
- Does it support embedding as input HOT 6
- Default docker install instructions shows scary errors and seems to fail... HOT 6
- Default source install instructions shows scary errors and seems to fail... HOT 2
- Excessive Duplicated Sentences in LIME Text Output HOT 3
- Widget on Google Cloud Vertex AI Workbench Notebook HOT 12
- TCAV with a DeBERTa model HOT 2
- Unable to Render LIT Widget in Jupyter Notebook on Server HOT 3
- Relax shap version HOT 4
- Errors while trying to run the examples HOT 7
- How to analyze remote models on a server? HOT 1
- In the demo, only single-tower structures are shown. Are there examples of twin-tower structures, similar to Sentence-BERT? HOT 2
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