Current implementation of Go binding can not specify options.

GPUOptions struct is in internal package. And go generate doesn't work for protobuf directory. So we can't specify GPUOptions for NewSession.

🚀 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

none

claimed

• LED @nnlnr
• Flaubert @anmolsjoshi
• Longformer @tgadeliya
• Electra @rajathb

Although the results look nice and ideal in all TensorFlow plots and are consistent across all frameworks, there is a small difference (more of a consistency issue). The result training loss/accuracy plots look like they are sampling on a lesser number of points. It looks more straight and smooth and less wiggly as compared to PyTorch or MXNet.

It can be clearly seen in chapter 6([CNN Lenet](ht

Describe the bug

Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash.

Steps to reproduce the bug

import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets

ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True).with_format("

New Operator

Describe the operator

Why is this operator necessary? What does it accomplish?

This is a frequently used operator in tensorflow/keras

Can this operator be constructed using existing onnx operators?

If so, why not add it as a function?

I don't know.

Is this operator used by any model currently? Which one?

Are you willing to contribute it?

/kind feature

Why you need this feature:

Sub-issue of kubeflow/kubeflow#6353

To have support for K8s 1.22 we need to ensure all our crud web apps, Jupyter, TensorBoards, Volumes, are using the v1 version of SubjectAccessReviews. https://kubernetes.io/docs/reference/using-api/deprec

Describe the issue:
During computing Channel Dependencies reshape_break_channel_dependency does following code to ensure that the number of input channels equals the number of output channels:

in_shape = op_node.auxiliary['in_shape']
out_shape = op_node.auxiliary['out_shape']
in_channel = in_shape[1]
out_channel = out_shape[1]
return in_channel != out_channel

This is correct