mbollmann

# Produced by running https://gist.github.com/mbollmann/827a079023ebdd18b4d06c28566fac0d
# with flags -o -e -c -w on commit 5a875471
1993.tmi.yaml['1993.tmi-1.17']: Value of root['author_string'] changed from "Pierre Isabelle, Marc Dymetman, George Foster, Jean-Marc Jutras,  Elliott" to "Pierre Isabelle, Marc Dymetman, George Foster, 
Jean-Marc Jutras, Elliott".
1993.tmi.yaml['1993.tmi-1.22']: Value of root['author_string'] changed from "Masaru Tomita, Masako Shirai, Junya Tsutsumi, Miki Matsumura,  Yuki" to "Masaru Tomita, Masako Shirai, Junya Tsutsumi, Miki 
Matsumura, Yuki".
2005.iwslt.yaml['2005.iwslt-1.6']: Value of root['author_string'] changed from "Sanjika Hewavitharana, Bing Zhao,  Hildebrand, Almut Silja, Matthias Eck, Chiori Hori, Stephan Vogel, Alex Waibel" to 
"Sanjika Hewavitharana, Bing Zhao, Hildebrand, Almut Silja, Matthias Eck, Chiori Hori, Stephan Vogel, Alex Waibel".
2006.amta.yaml['2006.amta-panel1.0']: Value of root['url'] changed from "https://aclanthology.org/2006.amta-panels.0/" to "h

mbollmann

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright 2024 Marcel Bollmann <[email protected]>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0

mbollmann

#!/usr/bin/env fish

if not command -q kitty
    set error_msg "kitty not found."
else if not command -q xdotool
    set error_msg "xdotool not found."
else if not command -q wmctrl
    set error_msg "wmctrl not found."
end

mbollmann

#!/usr/bin/env python3

# Unicode characters are neatly categorized into different "scripts", as seen on
# the character code chart <http://www.unicode.org/charts/#scripts> and defined
# in Annex #24 <https://www.unicode.org/reports/tr24/>.
#
# Unfortunately, Python's unicodedata module doesn't provide access to this
# information. However, the fontTools library does include this.
# <https://github.com/fonttools/fonttools>
#

mbollmann

#!/usr/bin/env python3
#
# MIT License
#
# Copyright (c) 2021 Marcel Bollmann
#
# 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

mbollmann

#!/usr/bin/env python3

"""Usage: conv_checkpoints_to_model.py MODFILE

Takes a trained model file with multiple saved checkpoints and converts these
checkpoints into standalone models.  This allows the different checkpoints to be
used, e.g., as parts of a model ensemble.

This script will:
- Analyze MODFILE to find all saved model components

mbollmann

--- theano/sandbox/cuda/opt.py	2017-05-31 23:26:09.972668647 +0200
+++ theano/sandbox/cuda/opt_patched.py	2017-06-01 00:49:43.818626738 +0200
@@ -38,10 +38,12 @@
     GpuElemwise, GpuDimShuffle, GpuReshape, GpuCAReduce,
     gpu_flatten,
     GpuSubtensor, GpuAdvancedSubtensor1,
-    GpuAdvancedIncSubtensor1, GpuAdvancedIncSubtensor1_dev20,
+    GpuAdvancedIncSubtensor1,
     GpuIncSubtensor, gpu_alloc, GpuAlloc, gpu_shape, GpuSplit, GpuAllocEmpty)
 from theano.sandbox.cuda.opt_util import pad_dims, unpad_dims

mbollmann

#!/usr/bin/python3
# -*- coding: utf-8 -*-

import argparse
import bibtexparser
from collections import Counter
import matplotlib.pyplot as plt
import seaborn as sns
import sys

mbollmann

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

mbollmann

from keras import backend as K
from keras.layers.recurrent import LSTM

class HiddenStateLSTM(LSTM):
    """LSTM with input/output capabilities for its hidden state.

    This layer behaves just like an LSTM, except that it accepts further inputs
    to be used as its initial states, and returns additional outputs,
    representing the layer's final states.