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Deep learning

Deep learning is an AI function and subset of machine learning, used for processing large amounts of complex data.

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transformers
SaulLu
SaulLu commented Apr 6, 2022

🚀 Add missing tokenizer test files

Several tokenizers currently have no associated tests. I think that adding the test file for one of these tokenizers could be a very good way to make a first contribution to transformers.

Tokenizers concerned

not yet claimed

  • LED

  • RemBert

  • MobileBert

  • ConvBert

  • RetriBert

claimed

seemethere
seemethere commented Mar 16, 2022

🚀 The feature, motivation and pitch

After the revert of pytorch/pytorch@7cf9b94 we've identified a need to add a lint that checks file names to ensure that they're compatible with Windows machines.

Observed error: (from example commit)

Error: error: invalid path 'test/test_ops_gradients.py '

A simple check on chang

module: bootcamp good first issue module: ci triaged

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

  • Updated Apr 3, 2022
  • Python
gjoliver
gjoliver commented Apr 13, 2022

Description

There are multiple user requests of using GraphNN data (node and edge lists) as sample batches into a custom RLlib model.

https://discuss.ray.io/t/rllib-variable-length-observation-spaces-without-padding/726
https://discuss.ray.io/t/working-with-graph-neural-networks-varying-state-space/5730/2

The recommended method today is to use Repeated observation space and VariableVal

good first issue enhancement P2 rllib-models
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