natural-language-processing

Feature request

We currently have 2 monocular depth estimation models in the library, namely DPT and GLPN.

It would be great to have a pipeline for this task, with the following API:

from transformers import pipeline

pipe = pipeline("depth-estimation")
pipe("ca

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

Adding a Dataset

• Name: Stanford dog dataset
• Description: The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/
• Paper: http://vision.stanford.edu/aditya86/ImageNetDogs/
• Data: *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/Ima

In gensim/models/fasttext.py:

    model = FastText(
        vector_size=m.dim,
        vector_size=m.dim,
        window=m.ws,
        window=m.ws,
        epochs=m.epoch,
        epochs=m.epoch,
        negative=m.neg,
        negative=m.neg,
        # FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
        # or model=3 supervi

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.

Checking the Python files in NLTK with "python -m doctest" reveals that many tests are failing. In many cases, the failures are just cosmetic discrepancies between the expected and the actual output, such as missing a blank line, or unescaped linebreaks. Other cases may be real bugs.

If these failures could be avoided, it would become possible to improve CI by running "python -m doctest" each t

Hey Hackers of this spoopy month! 👻
Welcome to the Ciphey repo(s)!
This issue requires you to add a decoder.

This wiki section walks you through EVERYTHING you need to know, and we've added some more links at the bottom of this issue to detail more about the decoder.
https://github.com/Ciphey/Ciphey/wiki#adding-your-own-crackers--decoders

https://www.dcode.fr/t9-cipher
https://en.wikipe

Some ideas for figures to add to the PPT

• Linear regression, single-layer neural network
• Multilayer Perceptron with hidden layer
• Backpropagation
• Batch Normalization and alternatives
• Computational Graphs
• Dropout
• CNN - padding, stride, pooling,...
• LeNet
• AlexNet
• VGG
• GoogleNet
• ResNet
• DenseNet
• Memory Net