PyTorch

Feature request

Is the addition of the 'OPTforSequenceClassification' class scheduled?
Is someone handling it?
When adding these functions, I wonder if it is possible to PR one by one, or if I have to PR all classes supported by other models.

Motivation

Added function of OPT class, which is being actively discussed recently

Your contribution

I personally use the forSequenceCla

Reporters do not apply sort_by_metric if metric is passed through tune.run.

I tried to pass metric argument to CLIReporter, however, I obtained this error:

raise ValueError(
                "You passed a `metric` or `mode` argument to `tune.run()`, but "
                "the reporter you are using was already instantiated with their "
                "own `metric` and `mode

https://github.com/open-mmlab/mmdetection/blob/7a9bc498d5cc972171ec4f7332afcd70bb50e60e/tools/analysis_tools/coco_error_analysis.py#L43
This I believe is for coco format, but I couldn't find any files for plotting precision or precision vs recall chart for pascal voc format.

README.md contains an execution example of the program in mnist_examples, but mnist_examples does not exist. If the code exists in any branch or in the past, recover it.

I wanted to solve the broken link of lightning_lite.rst together with this problem, but the PR Lightning-AI/lightning#13331 of only the fix of the link has been issued.

cc @Borda @rohitgr7

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

🚀 Feature

Motivation

paper "LEARNING TO REPRESENT PROGRAMS WITH GRAPHS" which encode computer programs as graphs, with rich semantic information, however, most code implementation on this dataset VarMisuse is based on TensorFlow, like [tf-gnn-samples](https://github.com/microsof

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

https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272

Hello,
Thanks you for this library .

I was using it and I had one edge case. my home folder name ends with .py it is /home/espoir.py so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code

We should sort imports with isort to keep the import section clean

Bug with GPU Model

Currently, while using pruning methods like TaylorFOWeight Pruner, If I use a model on GPU for getting the metrics (as calculated for getting masks), it fails on line while creating masks. The reason why it fails i