Comments (2)
I am using the ConfidenceEnhancedSVM.
That makes sense. I guess this validation set is only useful for early stopping in the train scheduler of the transformers model. Might be worthwhile adding to documentation so its clear fit() of a non-transformers model doesnt take a validation set.
Thank you!
from small-text.
Hi,
which sklearn model are you using exactly? Until now, I thought that scikit-learn classifiers either don't have a validation set or create their own from the training data tha tis provided. Therefore, the current intended use would be to omit the validation set when using with scikit learn classifiers.
But maybe this assumption was wrong?
from small-text.
Related Issues (20)
- Class weighting causes nan values in loss
- TransformerBasedClassification: validations_per_epoch > 2 leaves the model in eval mode
- LightweightCoreset should allow for other distance metrics
- Add auto mixed precision flag to Pytorch-based Classifiers
- Make more SetFitClassification parameters configurable
- Cloning a nested DatasetsViews raises an AttributeError
- Inconsistency: TransformerBaseClassification.embed() returns np.float32 while SetFitClassification returns np.float64
- Add ActivePETs query strategy HOT 1
- Batch size in greedy coreset batching is different than expected
- Device-side assertion not passed when training on cuda device and when there are added tokens to the tokenizer HOT 2
- When using EmbeddingBasedQueryStrategy with some transformers, model has an unsupported input `token_type_ids` when creating embeddings. HOT 1
- Update setfit version (>1.0.0)
- Additional options for Discriminative Active Learning HOT 8
- Unify the classifiers' initialize() methods
- EGL throws error HOT 4
- Improve README.md
- Link to the respective publication in the bibliography
- 'SequenceClassifierOutput' object has no attribute 'softmax' HOT 9
- Low values in pytorch integration HOT 5
- Use of large custom transformer model HOT 1
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from small-text.