Related to #5142, AlbertTokenizer (which uses SentencePiece) doesn't decode special tokens (like [CLS], [MASK]) properly. This issue was discovered when adding the Nystromformer model (#14659), which uses this tokenizer.

To reproduce (Transformers v4.15 or below):

!pip install -q transformers sentencepiece

from transformers import AlbertTokenizer

tokenizer = AlbertTokenizer.from

I want to train a detector based on object365 dataset, but object365 is pretty large, and caused out of memory error in my computer.
I want to split the annotation file to 10, such as ann1,ann2,...ann10, then build 10 datasets and concatenate them, but I'm not sure whether it's
gonna work or not.
Any better suggestion?

📚 Documentation

On the following page (On the following page (https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html#predict-dataloader) I stumbled upon the predict_dataloader() method.

Should we reference is as an option in the LightningDataModule method doc?
I.e., currently it has the section

https://github.com/PyTorchLightning/pytorch-lightning/blob/b3c

Change tensor.data to tensor.detach() due to
pytorch/pytorch#6990 (comment)
tensor.detach() is more robust than tensor.data.

🚀 The feature, motivation and pitch

I came across different homophily measures including the ones available in https://pytorch-geometric.readthedocs.io/en/latest/modules/utils.html#torch_geometric.utils.homophily. I found the one proposed by the LINKX paper will be a good addition to the utility func

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

Is your feature request related to a problem? Please describe.

I am uploading our dataset and models for the "Constructing interval measures" method we've developed, which uses item response theory to convert multiple discrete labels into a continuous spectrum for hate speech. Once we have this outcome our NLP models conduct regression rather than classification, so binary metrics are not r

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?

Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.