SuperShinyEyes

def f1_loss(y_true:torch.Tensor, y_pred:torch.Tensor, is_training=False) -> torch.Tensor:
    '''Calculate F1 score. Can work with gpu tensors
    
    The original implmentation is written by Michal Haltuf on Kaggle.
    
    Returns
    -------
    torch.Tensor
        `ndim` == 1. 0 <= val <= 1
    

bittlingmayer

# See https://github.com/facebookresearch/fastText/blob/master/get-wikimedia.sh
#
# From https://github.com/facebookresearch/fastText/issues/161:
#
# We now have a script called 'get-wikimedia.sh', that you can use to download and
# process a recent wikipedia dump of any language. This script applies the preprocessing
# we used to create the published word vectors.
#
# The parameters we used to build the word vectors are the default skip-gram settings,
# except with a dimensionality of 300 as indicated on the top of the list of word

bittlingmayer

"""

Takes two files produced by fastText's print-word-vectors or print-sentence-vectors and compares the vectors by similarity.

(See https://github.com/facebookresearch/fastText.)

This can be useful for benchmarking output or even generating benchmark data.

For example:

MihailJP

#!/bin/bash

# Script to control the fan speed automatically

setFanSpeed() {
	eval "nvidia-settings -a GPUFanControlState=1 -a [fan:0]/GPUCurrentFanSpeed=$1 > /dev/null"
}

cleanup() {
	eval "nvidia-settings -a GPUFanControlState=0"