Comments (3)
I'd like to have a method for this at some point, but I'm not 100% sure what the cleanest solution would be for the entire API.
Until then, you can confirm in the code that a trained learner would have a .network_
property that's a PyTorch object. You should be able to save that as you normally would.
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Thanks for the reply. one more question. i see there is a accuracy drop in using this method of contrastive learner + logistic regression vs setfit method. will u comment something on this?
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If you share the benchmark code I might be better able to comment. What happens if you compare with the SbertLearner
? Theoretically that should be the same. Unless I made an implementation mistake or if I misunderstood their implementation.
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Related Issues (20)
- add a comma in __all__ section of `/home/runner/work/embetter/embetter/embetter/text/__init__.py` HOT 4
- Consider adding wav2vec
- Make finetuners both a tranformer and predictor
- Ugly warning when using cache
- The external providers should be auth'd via env keys
- Revistit constrastive finetuner HOT 1
- Word2Vec and Doc2Vec support HOT 3
- Contrastive Modelling HOT 1
- Dedup model: might make for a nice util HOT 1
- OpenCLIP
- consider nomic
- consider crossencoders
- Add `TextPrefixer`
- Add Mistral Embeddings?
- Add Mamba models?
- Add mixedbread
- `MatryoshkaEncoder`
- Support quantization
- Change MatroushkaEncoder to MatryoshkaEncoder in embetter/text/_sbert.py HOT 2
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