File size: 3,668 Bytes
73cc8d2 | 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 | from __future__ import annotations
import uuid
from typing import Dict, List
import datasets
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval
class HagridRetrieval(AbsTaskRetrieval):
metadata = TaskMetadata(
name="HagridRetrieval",
dataset={
"path": "miracl/hagrid",
"revision": "b2a085913606be3c4f2f1a8bff1810e38bade8fa",
},
reference="https://github.com/project-miracl/hagrid",
description=(
"HAGRID (Human-in-the-loop Attributable Generative Retrieval for Information-seeking Dataset)"
"is a dataset for generative information-seeking scenarios. It consists of queries"
"along with a set of manually labelled relevant passages"
),
type="Retrieval",
category="s2p",
eval_splits=["dev"],
eval_langs=["eng-Latn"],
main_score="ndcg_at_10",
date=None,
form=None,
domains=None,
task_subtypes=None,
license=None,
socioeconomic_status=None,
annotations_creators=None,
dialect=None,
text_creation=None,
bibtex_citation=None,
n_samples=None,
avg_character_length=None,
)
def load_data(self, **kwargs):
"""Loads the different split of the dataset (queries/corpus/relevants)"""
if self.data_loaded:
return
data = datasets.load_dataset(
"miracl/hagrid",
split=self.metadata.eval_splits[0],
revision=self.metadata_dict["dataset"].get("revision", None),
)
proc_data = self.preprocess_data(data)
self.queries = {
self.metadata.eval_splits[0]: {
d["query_id"]: d["query_text"] for d in proc_data
}
}
self.corpus = {
self.metadata.eval_splits[0]: {
d["answer_id"]: {"text": d["answer_text"]} for d in proc_data
}
}
self.relevant_docs = {
self.metadata.eval_splits[0]: {
d["query_id"]: {d["answer_id"]: 1} for d in proc_data
}
}
self.data_loaded = True
def preprocess_data(self, dataset: Dict) -> List[Dict]:
"""Preprocessed the data in a format easirer
to handle for the loading of queries and corpus
------
PARAMS
dataset : the hagrid dataset (json)
"""
preprocessed_data = []
for d in dataset:
# get the best answer among positively rated answers
best_answer = self.get_best_answer(d)
# if no good answer found, skip
if best_answer is not None:
preprocessed_data.append(
{
"query_id": str(d["query_id"]),
"query_text": d["query"],
"answer_id": str(uuid.uuid4()),
"answer_text": best_answer,
}
)
return preprocessed_data
def get_best_answer(self, data: Dict) -> str:
"""Get the best answer among available answers
of a query.
WARNING : May return None if no good answer available
--------
PARAMS:
data: a dict representing one element of the dataset
"""
good_answers = [
a["answer"]
for a in data["answers"]
if a["informative"] == 1 and a["attributable"] == 1
]
# Return 1st one if >=1 good answers else None
return good_answers[0] if len(good_answers) > 0 else None
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