mxnet
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Feature Request
System information
ONNX version (you are using): 1.12
What is the problem that this feature solves?
It would be great if ONNX Hub can download test_data_set. It will be helpful for testing ONNX Model Zoo.
Describe the feature
Currently users can only download models through ONNX Hub. There is no easy API for them to download the test_data_set from.tar.gz
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Hi,
I need to download the something-to-something and jester datasets. But the 20bn website "https://20bn.com" are not available for weeks, the error message is "503 Service Temporarily Unavailable".
I have already downloaded the video data of something-to-something v2, and I need the label dataset. For the Jester, I need both video and label data. Can someone share me the
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Aug 10, 2022 - Python
DALI + Catalyst = 🚀
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Jun 22, 2022 - Jupyter Notebook
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Aug 7, 2022
I have the same hardware envs, same network, but I could not get the result as you, almost half as you. Any best practices and experience? thanks very much! for bytePS with 1 instance and 8 GPU, I have similar testing result.
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Feb 14, 2022
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Sep 23, 2021 - Python
Description
I created a simple random dataset, added a single NAN value in the first instance at the start of the last window before forecast, and compared evaluation metrics of 3 trivial estimators: Mean, Constant and Identity (code below). The ConstantEstimator is not affected at all - all metrics are normal. IdentifyPredictor ends up with NA for the metrics on the first instance and norma
Implement TabNet
Description
This issue is to create the TabNet model and add it to the basic model zoo. TabNet is a good example of a deep learning model that will work with the tabular modality. Then, it can be trained or tested with an implementation of the CsvDataset such as AirfoilRandomAccess or AmesRandomAccess.
References
- Paper: [TabNet: Attentive Interpretable Tabular Learning](htt
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Jul 4, 2022 - Python
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Aug 8, 2022 - Python
[Error Message] Improve error message in SentencepieceTokenizer when arguments are not expected.
Description
While using tokenizers.create with the model and vocab file for a custom corpus, the code throws an error and is not able to generate the BERT vocab file
Error Message
ValueError: Mismatch vocabulary! All special tokens specified must be control tokens in the sentencepiece vocabulary.
To Reproduce
from gluonnlp.data import tokenizers
tokenizers.create('spm', model_p
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Description
This is a documentation bug. The parameter of API
mxnet.test_utils.check_numeric_gradientis not consistent between signature and Parameter section. There is a parametercheck_epsin the Parameter section, but it is not in the signature.Link to document: https://mxnet.apache.org/versions/1.6/api/python/docs/api/mxnet/test_utils/index.html#mxnet.test_utils.check_numeric_gra