FEA-Bench / testbed /embeddings-benchmark__mteb /docs /create_tasks_table.py
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from __future__ import annotations
import re
from pathlib import Path
from typing import get_args
import polars as pl
import mteb
from mteb.abstasks.TaskMetadata import PROGRAMMING_LANGS, TASK_TYPE
def author_from_bibtex(bibtex: str | None) -> str:
"""Create (Authors, Year) from bibtex entry (author = {Authors}, year = {Year})"""
if bibtex is None:
return ""
# get authors from bibtex (author = {Authors} or author={Authors})
authors = re.search(r"author\s*=\s*{([^}]*)}", bibtex)
if authors is None:
return ""
authors = authors.group(1)
authors = [a.split(", ") for a in authors.split(" and ")]
author_str_w_et_al = (
authors[0][0] + " et al." if len(authors[0]) > 1 else authors[0][0]
)
# replace any newline characters
author_str_w_et_al = author_str_w_et_al.replace("\n", " ")
year = re.search(r"year\s*=\s*{([^}]*)}", bibtex)
if year is None:
return ""
year_str = year.group(1)
return f" ({author_str_w_et_al}, {year_str})"
def task_to_markdown_row(task: mteb.AbsTask) -> str:
name = task.metadata.name
name_w_reference = (
f"[{name}]({task.metadata.reference})" if task.metadata.reference else name
)
domains = (
"[" + ", ".join(task.metadata.domains) + "]" if task.metadata.domains else ""
)
n_samples = task.metadata.n_samples if task.metadata.n_samples else ""
avg_character_length = (
task.metadata.avg_character_length if task.metadata.avg_character_length else ""
)
name_w_reference += author_from_bibtex(task.metadata.bibtex_citation)
return f"| {name_w_reference} | {task.metadata.languages} | {task.metadata.type} | {task.metadata.category} | {domains} | {n_samples} | {avg_character_length} |"
def create_tasks_table(tasks: list[mteb.AbsTask]) -> str:
table = """
| Name | Languages | Type | Category | Domains | # Samples | Avg. Length (Char.) |
|------|-----------|------|----------|---------|-----------|---------------------|
"""
for task in tasks:
table += task_to_markdown_row(task) + "\n"
return table
def create_task_lang_table(tasks: list[mteb.AbsTask]) -> str:
table_dict = {}
## Group by language. If it is a multilingual dataset, 1 is added to all languages present.
for task in tasks:
for lang in task.metadata.languages:
if lang in PROGRAMMING_LANGS:
lang = "code"
if table_dict.get(lang) is None:
table_dict[lang] = {k: 0 for k in sorted(get_args(TASK_TYPE))}
table_dict[lang][task.metadata.type] += 1
## Wrangle for polars
pl_table_dict = []
for lang, d in table_dict.items():
d.update({"lang": lang})
pl_table_dict.append(d)
df = pl.DataFrame(pl_table_dict).sort(by="lang")
total = df.sum(axis=0)
task_names_md = " | ".join(sorted(get_args(TASK_TYPE)))
horizontal_line_md = "---|---" * len(sorted(get_args(TASK_TYPE)))
table = """
| Language | {} |
|{}|
""".format(task_names_md, horizontal_line_md)
for row in df.iter_rows():
table += f"| {row[-1]} "
for num in row[:-1]:
table += f"| {num} "
table += "|\n"
for row in total.iter_rows():
table += "| Total "
for num in row[:-1]:
table += f"| {num} "
table += "|\n"
return table
def insert_tables(
file_path: str, tables: list[str], tags: list[str] = ["TASKS TABLE"]
) -> None:
"""Insert tables within <!-- TABLE START --> and <!-- TABLE END --> or similar tags."""
md = Path(file_path).read_text()
for table, tag in zip(tables, tags):
start = f"<!-- {tag} START -->"
end = f"<!-- {tag} END -->"
md = md.replace(md[md.index(start) + len(start) : md.index(end)], table)
Path(file_path).write_text(md)
def main():
tasks = mteb.get_tasks()
tasks = sorted(tasks, key=lambda x: x.metadata.name)
tasks_table = create_tasks_table(tasks)
task_lang_table = create_task_lang_table(tasks)
file_path = Path(__file__).parent / "tasks.md"
insert_tables(
file_path,
tables=[tasks_table, task_lang_table],
tags=["TASKS TABLE", "TASK LANG TABLE"],
)
if __name__ == "__main__":
main()