File size: 5,924 Bytes
011bd7a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | from __future__ import annotations
import os
from mteb import MTEB
HEADER = "| Name | Hub URL | Description | Type | Category | #Languages | Train #Samples | Dev #Samples | Test #Samples | Avg. chars / train | Avg. chars / dev | Avg. chars / test"
SEP = "|:-----|:-----|:-----|:-----|:-----|-----:|-----:|-----:|-----:|-----:|-----:|-----:|"
ONE_LINE = "| {} | {} | {} | {} | {} | {} | {} | {} | {} | {} | {} | {} |"
TABLE_STRING = "\n".join([HEADER, SEP])
LEN_KEYS = {
"text",
"sentences",
"sentence1",
"sentence2",
"sent1",
"sent2" "query",
"positive",
"negative" "queries",
"corpus",
"machine_summaries",
"human_summaries",
}
DATAPATH = "/gpfsscratch/rech/six/commun/commun/experiments/muennighoff/mteb"
def load_data(hf_hub_name, subset=None):
"""Load dataset from Hub via cloning for easy offline usage with HF_DATASETS_OFFLINE=1
Can be replaced with just `load_dataset(hf_hub_name, subset)` if preferred
"""
from datasets import load_dataset
path = os.path.join(DATAPATH, hf_hub_name)
if os.path.exists(path):
dataset = load_dataset(path, subset)
else:
from git import Repo
Repo.clone_from("https://huggingface.co/datasets/" + hf_hub_name, path)
dataset = load_dataset(path, subset)
return dataset
def get_ds_stats_beir_hub(hf_hub_name):
"""Not used as some BEIR datasets are still missing on the Hub"""
lens = {}
for subset in ["corpus", "queries"]:
ds = load_data("mteb/hfbeir" + hf_hub_name.replace("BeIR", ""), subset)
splits = list(ds.keys())
len_keys = set(ds[splits[-1]].features.keys()) & LEN_KEYS
for split in splits:
if split not in ds:
continue
lens.setdefault(split, [])
for k in len_keys:
if isinstance(ds[split][k][0], str):
lens[split] += [len(x) for x in ds[split][k]]
elif isinstance(ds[split][k][0], list):
assert isinstance(ds[split][k][0][0], str), f"Too nested: {k}"
lens[split] += [len(y) for x in ds[split][k] for y in x]
else:
raise ValueError(f"Unknown type {type(ds[split][k])}")
all_lens = [x for y in lens.values() for x in y]
avg_len = sum(all_lens) / len(all_lens)
return ["TODO"] * 3 + [round(avg_len, 1)] * 3
def get_ds_stats_beir(hf_hub_name):
from beir.datasets.data_loader import GenericDataLoader as BeirDataLoader
path = os.path.join(DATAPATH, hf_hub_name)
if not os.path.exists(path):
from beir import util
url = f"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{hf_hub_name}.zip"
util.download_and_unzip(url, DATAPATH)
lens = {"train": [], "dev": [], "test": []}
for split in lens.keys():
try:
corpus, queries, relevant_docs = BeirDataLoader(path).load(split=split)
except: # split does not exist # noqa: E722
continue
# + 1 for space added between Title & Text by default in BEIR
avg_lens_c = [len(v["text"]) + len(v["title"]) + 1 for v in corpus.values()]
avg_lens_q = [len(v) for v in queries.values()]
lens[split].extend(avg_lens_c)
lens[split].extend(avg_lens_q)
avg_lens = {
k: round(sum(lens[k]) / len(lens[k]), 1) if lens[k] else 0 for k in lens
}
return (
len(lens["train"]),
len(lens["dev"]),
len(lens["test"]),
avg_lens["train"],
avg_lens["dev"],
avg_lens["test"],
)
def get_ds_stats(hf_hub_name):
ds = load_data(hf_hub_name)
assert "test" in ds, f"No test set for {hf_hub_name}"
len_keys = set(ds["test"].features.keys()) & LEN_KEYS
dev_key = "dev" if "dev" in ds else "validation"
lens = {"train": [], dev_key: [], "test": []}
for split in lens.keys():
if split not in ds:
continue
for k in len_keys:
if isinstance(ds[split][k][0], str):
lens[split] += [len(x) for x in ds[split][k]]
elif isinstance(ds[split][k][0], list):
assert isinstance(ds[split][k][0][0], str), f"Too nested: {k}"
lens[split] += [len(y) for x in ds[split][k] for y in x]
else:
raise ValueError(f"Unknown type {type(ds[split][k])}")
avg_lens = {
k: round(sum(lens[k]) / len(lens[k]), 1) if lens[k] else 0 for k in lens
}
return (
len(lens["train"]),
len(lens[dev_key]),
len(lens["test"]),
avg_lens["train"],
avg_lens[dev_key],
avg_lens["test"],
)
# Select all tasks
for task in MTEB().tasks:
print("Task: ", task)
if "dataset" in task.metadata_dict:
hub_name = hub_url = task.metadata_dict["dataset"]["path"]
ds_stats = get_ds_stats(hub_name.split("/")[-1])
elif "beir_name" in task.metadata_dict:
hub_name = hub_url = "BeIR/" + task.metadata_dict.get("beir_name")
ds_stats = get_ds_stats_beir("/".join(hub_name.split("/")[1:]))
if "cqadupstack" in hub_name:
hub_url = "BeIR/cqadupstack-qrels"
TABLE_STRING += "\n" + ONE_LINE.format(
f"[{task.metadata_dict['name']}]({task.metadata_dict['reference']})",
f"[{hub_name}](https://huggingface.co/datasets/{hub_url})",
task.metadata_dict["description"],
task.metadata_dict["type"],
task.metadata_dict["category"],
len(task.metadata_dict["eval_langs"]),
*ds_stats,
)
with open("./mdtable.md", "w") as f:
f.write(TABLE_STRING)
# Convert to latex
for line in TABLE_STRING.split("\n")[2:]:
if line:
cols = line.split(" | ")
idx = cols[0].index("]")
cols[0] = cols[0][3:idx]
cols[-1] = cols[-1][:-1]
out = " & ".join(cols[:1] + cols[3:]) + " \\\\"
print(out)
|