Comments (1)
koaning
commented on May 27, 2024
It totally does.
File ~/Development/koaning-raft/venv/lib/python3.8/site-packages/embetter/finetune/_forward.py:63, in ForwardFinetuner.partial_fit(self, X, y, classes)
60 self._criterion = nn.CrossEntropyLoss()
62 torch_X = torch.from_numpy(X).detach().float()
---> 63 torch_y = torch.from_numpy(np.array(y)).detach()
65 for _ in range(self.n_epochs):
66 self._optimizer.zero_grad()
TypeError: can't convert np.ndarray of type numpy.str_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.
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Related Issues (20)
- 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
- How to save the learner(contrastivelearner) as pytorch? HOT 3
- 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
- add a comma in __all__ section of `/home/runner/work/embetter/embetter/embetter/text/__init__.py` HOT 4
- Consider adding wav2vec
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