steven2358

FFmpeg cheat sheet

A list of useful commands for the FFmpeg command line tool.

Download FFmpeg: https://www.ffmpeg.org/download.html

Full documentation: https://www.ffmpeg.org/ffmpeg.html

Basic conversion

MichaelPolla

Markdown - Resize pictures in GitHub

I found that the "best" way is to use HTML, as it works both in Readme/.md files and also in comments (within Issues, Gist...)

E.g. when adding/editing a comment (within Issues, Gist...) :

• Upload the picture by drag-and-drop in the text field
• replace ![image](https://your-image-url.type) with <img src="https://your-image-url.type" width="100" height="100">

As mentioned by @cbestow (thanks!), it's not mandatory to set both width and height. If only one is set, the other will be adjusted accordingly to preserve the aspect ratio of the image.

simonw

How to upgrade a Heroku PostgreSQL database to a new plan

I started a project on a Hobby Dev plan (free, limit 10,000 rows), and then later needed to upgrade it to Hobby Basic ($9/month, limit 10,000,000 rows).

After assigning the new database, I had two databases attached to the application. They looked something like this:

• HEROKU_POSTGRESQL_OLIVE (postgresql-dimensional-3321) Old, free-tier (Hobby Dev) database

noelbundick

MIT License

Copyright (c) 2018 Noel Bundick

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

thomwolf

model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...

finiteautomata

"""
This is a workaround for `examples/run_mlm.py` for pretraining models 
with big text files line-by-line. 

For the time being, `datasets` is facing some issues dealing with really 
big text files, so we use a custom dataset until this is fixed. 

August 3th 2021

Author: Juan Manuel Pérez

pszemraj

AutoTA Example

• A series of transformer models that embed your course documents, search for them when you ask it a question, and construct a reply in coherent english using a text2text generator.
• Based on the work done by deepset.ai in their Haystack library.
• As the notebook preview does not always load on GitHub, click here for the Colab notebook / demo. Instructions are provided on what to edit to use the notebook both in simple cases (i.e. just changing the questions) to adapting the notebook for other purposes.

Example: The model's explanation of K-Means

question to model: What is the k-means algorithm?

pszemraj

pszemraj

pszemraj