HSILA

Year	German Influx to Canada	Germany's Population (Approx.)	German Influx as % of Germany's Pop	Canadian Residents in Germany (Cumulative)	Canadian Influx to Germany (Calculated)	Canada's Population (Approx.)	Canadian Influx as % of Canada's Pop
2014	~2,000	81.0 million	0.0025%	~15,500	500	35.4 million	0.0014%
2015*	~2,050	81.7 million	0.0025%	~15,850	350	35.7 million	0.0010%
2016	~2,100	82.3 million	0.0026%	~16,200	350	36.1 million	0.0010%
2017*	~2,150	82.7 million	0.0026%	~16,600	400	36.5 million	0.0011%
2018	~2,200	82.9 million	0.0027%	~17,000	400	37.1 million	0.0011%
2019	~2,300	83.1 million	0.0028%	~17,500	500	37.6 million	0.0013%
2020	~1,100	83.2 million	0.0013%	~16,500	-1,000**	38.0 million	-0.0026%
2021	~1,500

HSILA

Report: Literature/Academic Retrieval Tasks

Rubric

Included:

• Scientific papers, academic corpora
• Citations, abstracts from scholarly literature
• Scholarly QA grounded in academic papers

Excluded:

HSILA

{
  "dataset_revision": "5377aa18f309ec440ff6325a4c2cd3362c2cb8d7",
  "task_name": "ChemRxivRetrieval",
  "mteb_version": "2.6.8",
  "scores": {
    "test": [
      {
        "ndcg_at_1": 0.625,
        "ndcg_at_3": 0.71766,
        "ndcg_at_5": 0.74022,

HSILA

\documentclass[tikz,border=2pt]{standalone}
\usepackage{amsmath,bm}
\usepackage{tikz}
% \usetikzlibrary{arrows.meta}  % if you need extra arrowheads
\begin{document}
\begin{tikzpicture}
    \node at (0,1.8) (h) {$\longleftarrow\,$Representation$\,\longrightarrow$};
    \node[draw, circle] at (0,-1) (x) {$\bm{x}$};
    \node[draw, circle] at (-2.5,0) (x1) {$\tilde{\bm{x}}_i$};
    \node[draw, circle] at (2.5,0) (x2) {$\tilde{\bm{x}}_j$};

HSILA

import os
import numpy as np
from typing import Any


import torch
import mteb


from transformers import AutoTokenizer

HSILA

import torch
from torch import nn
import onnx
import onnxruntime

a = torch.tensor([12, 0, 5, 154], dtype=torch.float32)

# a.to(torch.bool) = tensor([True, False, True, True])
# ~a.to(torch.bool) = tensor([False, True, False, False])