dschaehi

#!/usr/bin/env sh

comment_box() {
    msg="$1"
    msg_length=${#msg}
    border_width=$((msg_length + 4))  # Padding for spaces around message

    # Generate the top and bottom border using the chosen comment character
    border=$(printf '%*s' "$border_width" '')  # Create empty string of width
    border=$(echo "$border" | tr ' ' '#')  # Replace spaces with '#'

dschaehi

#!/usr/bin/env sh

client_version=$(ollama -v | grep -oP 'version is \K[0-9.]+')
latest_version=$(git ls-remote --tags --sort="v:refname" https://github.com/ollama/ollama.git | grep -E 'refs/tags/v?[0-9]+\.[0-9]+\.[0-9]+$' | tail -n1 | sed 's/.*\/v*//')
echo Client version is $client_version
echo Latest version is $latest_version
if [ "$(printf '%s\n' "$client_version" "$latest_version" | sort -V | head -n1)" != "$latest_version" ]; then
    OLLAMA_CACHE="$XDG_CACHE_HOME/ollama"
    OLLAMA="$XDG_OPT_HOME/ollama"
    mkdir -p $OLLAMA_CACHE

dschaehi

(defun advice-after-TeX-TeX-sentinel (&rest args)
    (unless (TeX-error-report-has-errors-p)
      (run-hook-with-args 'TeX-after-compilation-finished-functions
                          (with-current-buffer TeX-command-buffer
                            (expand-file-name
                             (TeX-active-master (TeX-output-extension)))))))
  (advice-add 'TeX-TeX-sentinel :after #'advice-after-TeX-TeX-sentinel)

dschaehi

if (Translator.BetterBibTeX) {
    if (tex.entrytype === "unpublished" && !tex.has.note) {
        tex.add({ name: "note", value: "unpublished manuscript" });
    }
    if (tex.entrytype === "misc" && zotero.arXiv) {
        if (!tex.has.misc) tex.entrytype = "article";
        if (tex.has.number) tex.remove("number");
        if (tex.has.journal) tex.remove("journal");
        tex.add({
            name: "journal",

dschaehi

type: ActionsTagsBackup
author: jaeheelee
platformVersion: 7.0.3
pluginVersion: 2.0.0
timestamp: '2024-08-31T11:59:12.270Z'
actions:
  1715690567316-0F2mzqBj:
    event: 0
    operation: 4
    data: >

dschaehi

\NeedsTeXFormat{LaTeX2e}
\ProvidesPackage{commenting}[2022/10/29 by Jae Hee Lee (http://jaeheelee.de)]
\RequirePackage{hyperref}
\RequirePackage{ifdraft}
\RequirePackage{graphicx}
\RequirePackage{xcolor}
\RequirePackage{xspace}
\RequirePackage{environ}
\RequirePackage{soul}
\RequirePackage{twemojis}

dschaehi

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...

dschaehi

def flatten_json(json):
    if type(json) == dict:
        for k, v in list(json.items()):
            if type(v) == dict:
                flatten_json(v)
                json.pop(k)
                for k2, v2 in v.items():
                    json[k+"."+k2] = v2

                    

dschaehi

import torch


def hamming_score(pred, answer):
    out = ((pred & answer).sum(dim=1) / (pred | answer).sum(dim=1)).mean()
    if out.isnan():
        out = torch.tensor(1.0)
    return out

answer = torch.tensor([[0, 1, 0], [0, 1, 1], [1, 0, 1], [0, 0, 1]])

dschaehi

from collections import OrderedDict

from torchvision import models


def gen_feature_extractor(model, output_layer):
    layers = OrderedDict()
    for (k, v) in model._modules.items():
        layers[k] = v
        if k == output_layer: