added initial script for loading queries and qrels

beir.py

@@ -0,0 +1,159 @@
+import json
+import csv
+import os
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_DESCRIPTION = "BEIR Benchmark"
+_DATASETS = ["fiqa", "trec-covid", ""]
+
+URL = ""
+_URLs = {
+    dataset: {
+        "queries": URL + f"{dataset}/queries.jsonl", 
+        "qrels": {
+            "train": URL + f"{dataset}/qrels/train.tsv",
+            "dev": URL + f"{dataset}/qrels/dev.tsv",
+            "test": URL + f"{dataset}/qrels/test.tsv"
+        }} for dataset in _DATASETS}
+
+
+class BEIR(datasets.GeneratorBasedBuilder):
+    """BEIR BenchmarkDataset."""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=dataset,
+            description=f"This is the {dataset} dataset in BEIR Benchmark.",
+        ) for dataset in _DATASETS
+    ]
+    
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+             features=datasets.Features({
+                "query": datasets.Value("string"), 
+                "relevant": [{
+                    "_id": datasets.Value("string"),
+                    "score": datasets.Value("int32"),
+                }],
+             }),
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        my_urls = _URLs[self.config.name]
+
+        # All train, dev and test splits available for these datasets
+        if self.config.name in ["msmarco", "nfcorpus", "hotpotqa", "fiqa", "fever"]:
+            data_dir = dl_manager.download_and_extract(my_urls)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"query_path": data_dir["queries"],
+                                "qrels_path": data_dir["qrels"]["train"]}
+                ),
+                datasets.SplitGenerator(
+                    name="dev",
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"query_path": data_dir["queries"],
+                                "qrels_path": data_dir["qrels"]["dev"]}
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"query_path": data_dir["queries"],
+                                "qrels_path": data_dir["qrels"]["test"]}
+                ),
+            ]
+        
+        # Only train and test splits available for these datasets
+        elif self.config.name in ["nq", "scifact"]:
+            my_urls["qrels"].pop("dev", None)
+            data_dir = dl_manager.download_and_extract(my_urls)
+            
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"query_path": data_dir["queries"],
+                                "qrels_path": data_dir["qrels"]["train"]}
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"query_path": data_dir["queries"],
+                                "qrels_path": data_dir["qrels"]["test"]}
+                ),
+            ]
+        
+        # Only dev and test splits available for these datasets
+        elif self.config.name in ["dbpedia", "quora"]:
+            my_urls["qrels"].pop("train", None)
+            data_dir = dl_manager.download_and_extract(my_urls)
+            return [
+                datasets.SplitGenerator(
+                    name="dev",
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"query_path": data_dir["queries"],
+                                "qrels_path": data_dir["qrels"]["dev"]}
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"query_path": data_dir["queries"],
+                                "qrels_path": data_dir["qrels"]["test"]}
+                ),
+            ]
+        
+        # Only test split available for these datasets
+        else:
+            for split in ["train", "dev"]:
+                my_urls["qrels"].pop(split, None)
+            data_dir = dl_manager.download_and_extract(my_urls)
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"query_path": data_dir["queries"],
+                                "qrels_path": data_dir["qrels"]["test"]}
+                ),
+            ]
+
+
+    def _generate_examples(self, query_path, qrels_path):
+        """Yields examples."""
+        
+        queries, qrels = {}, {}
+
+        with open(query_path, encoding="utf-8") as fIn:        
+            text = fIn.readlines()
+
+        for line in text:
+            line = json.loads(line)
+            queries[line.get("_id")] = line.get("text", "")
+        
+        reader = csv.reader(open(qrels_path, encoding="utf-8"), 
+                    delimiter="\t", quoting=csv.QUOTE_MINIMAL)
+        
+        next(reader)
+
+        for id, row in enumerate(reader):
+            query_id, corpus_id, score = row[0], row[1], int(row[2])
+            if query_id not in qrels:
+                qrels[query_id] = {corpus_id: score}
+            else:
+                qrels[query_id][corpus_id] = score
+        
+        for i, query_id in enumerate(qrels):
+            yield i, {
+                "query": queries[query_id], 
+                "relevant": [{"_id": doc_id, "score": score
+                    } for doc_id, score in qrels[query_id].items()]
+                    }