Comments (10)
This is the case because the classifiers are not spacy
specific. They use torch
or scikit-learn
.
from classy-classification.
This is the case because the classifiers are not
spacy
specific. They usetorch
orscikit-learn
.
Thank you, did you mean underlying spacy library is using torch and scikit-learn? Because I haven't imported either of those for this project. Is there a way I can make it consistent by any chance?
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@swageeth does it make sense to you to align the seed with the spacy seed?
from classy-classification.
@swageeth does it make sense to you to align the seed with the spacy seed?
how can I make it align with the spacy seed in my code?
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As of now, that isn`t possible, but I can include it in a next release.
from classy-classification.
As of now, that isn`t possible, but I can include it in a next release.
yes, please
from classy-classification.
@swageeth I will take a look the week after Christmas.
from classy-classification.
@swageeth this was handled in the new 0.5.4 release. Happy Holidays 🎄
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Thanks @davidberenstein1957. Happy holidays to you too! :)
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Related Issues (20)
- ImportError: cannot import name 'cached_path' from 'transformers.file_utils' (/opt/conda/lib/python3.7/site-packages/transformers/file_utils.py) HOT 3
- Saving and loading models
- retrain on saved pickle model? HOT 2
- add zero-shot `onnx`support
- Misaligned pairings of labels and scores? HOT 8
- add `https://onnx.ai/sklearn-onnx/` support HOT 1
- add saving and loading support for `standalone` reproducability HOT 3
- between zero shot and few shot HOT 1
- setfit in classy classification HOT 7
- Drastic performance drop HOT 5
- Running the first example we get a different score HOT 2
- Error when using spacy _trf models HOT 6
- Different language models HOT 9
- Standalone usage without spaCy setting embeddings post adding the data makes the classifications run twice HOT 1
- Token indices sequence length HOT 1
- Spacy embeddings vs sentence transformer embeddings HOT 1
- Example code gives error
- Would be great to also apply the classifier on arbitrary Spans HOT 2
- The current version of package is unstable and exceptions occur HOT 2
- Installations on Ubuntu
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