updated qampari.py

mapped_qampari.py

@@ -12,6 +12,25 @@ from datasets.fingerprint import Hasher
 import pickle
 
 
+
+def to_dict_element(el, cols):
+    bucked_fields = more_itertools.bucket(cols, key=lambda x: x.split(".")[0])
+    final_dict = {}
+    for parent_name in set(x.split(".")[0] for x in cols):
+
+        fields = [y.split(".")[-1] for y in list(bucked_fields[parent_name])]
+        if len(fields) == 1 and fields[0] == parent_name:
+            final_dict[parent_name] = el[fields[0]]
+        else:
+            parent_list = []
+            zipped_fields = list(zip(*[el[f"{parent_name}.{child}"] for child in fields]))
+            for x in zipped_fields:
+                parent_list.append({k: v for k, v in zip(fields, x)})
+            final_dict[parent_name] = parent_list
+    return final_dict
+
+
+
 def mega_hash(func,dataset_name,dataset_config,dataset_obj,split):
     hasher = Hasher()
     hasher.update(repr(dataset_obj))
@@ -29,28 +48,27 @@ logger = datasets.logging.get_logger(__name__)
 _CITATION = """ """
 
 _DESCRIPTION = """ """
+base_features = {"source": Value(dtype="string"),
+                "meta":{
+                        "id":Value(dtype="string"),
+                        "qid":Value(dtype="string"),
+                        "question":Value(dtype="string"),
+                        "title":Value(dtype="string"),
+                        "text":Value(dtype="string"),
+                        }
+}
 
 
+reranking_mapped_features = Features({**base_features,"target": Value(dtype="string"),})
 
-
-
-mapped_features = Features({"source": Value(dtype="string"),
-                    "target": Value(dtype="string"),
-                    "meta":{
-                            "chunk_id":Value(dtype="string"),
-                            "qid":Value(dtype="string"),
-                            "question":Value(dtype="string"),
-                            "title":Value(dtype="string"),
-                            "text":Value(dtype="string"),
-                            }
-})
+inference_mapped_features = Features(base_features)
 
 
 
 class MappedQampariConfig(datasets.BuilderConfig):
     """BuilderConfig for MappedQampariDPR."""
 
-    def __init__(self, features=None, retriever=None, feature_format=None, **kwargs):
+    def __init__(self, features=None, retriever=None,feature_format=None, **kwargs):
 
         super(MappedQampariConfig, self).__init__(**kwargs)
         self.features = features
@@ -79,7 +97,7 @@ def to_source_target(example):
         for c, a, t, _, _, cid in zip(ctx, ans_for, title, question, qids, cids):
             source.append(f"Title: {t}\nText: {c}\nQuestion: {question}\n")
             target.append(f"Answer: {ans_mapping[a.split('__')[-2]]}")
-            meta_list.append({"chunk_id": cid, "qid": qids, "question": question, "title": t, "text": c})
+            meta_list.append({"id": cid, "qid": qids, "question": question, "title": t, "text": c})
     for ctx, title, question, qids, cids in zip(
         example["hard_negative_ctxs.text"],
         example["hard_negative_ctxs.title"],
@@ -90,7 +108,7 @@ def to_source_target(example):
         for c, t, _, _, cid in zip(ctx, title, question, qids, cids):
             source.append(f"Title: {t}\nText: {c}\nQuestion: {question}\n")
             target.append("Not relevant")
-            meta_list.append({"chunk_id": cid, "qid": qids, "question": question, "title": t, "text": c})
+            meta_list.append({"id": cid, "qid": qids, "question": question, "title": t, "text": c})
     return {"target": target, "source": source, "meta": meta_list}
 
 
@@ -116,9 +134,33 @@ class MappedQampari(datasets.GeneratorBasedBuilder):
             name="reranking_bm25",
             version=datasets.Version("1.0.1", ""),
             description="MappedQampari dataset in DPR format with the bm25 retrieval results",
-            features=mapped_features,
+            features=reranking_mapped_features,
+            retriever="bm25",
+            feature_format="reranking",
+        ),
+            MappedQampariConfig(
+            name="reranking_dprnq",
+            version=datasets.Version("1.0.1", ""),
+            description="MappedQampari dataset in DPR format with the bm25 retrieval results",
+            features=reranking_mapped_features,
+            retriever="dprnq",
+            feature_format="reranking",
+        ),
+        MappedQampariConfig(
+            name="inference_dprnq",
+            version=datasets.Version("1.0.1", ""),
+            description="MappedQampari dataset in DPR format with the bm25 retrieval results",
+            features=inference_mapped_features,
+            retriever="dprnq",
+            feature_format="inference",
+        ),
+        MappedQampariConfig(
+            name="inference_bm25",
+            version=datasets.Version("1.0.1", ""),
+            description="MappedQampari dataset in DPR format with the bm25 retrieval results",
+            features=inference_mapped_features,
             retriever="bm25",
-            feature_format="dpr",
+            feature_format="inference",
         ),
     ]
 
@@ -158,19 +200,35 @@ class MappedQampari(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, split):
         """This function returns the examples in the raw (text) form."""
-        dataset_config = "reranking_bm25"
-        flattened_dataset = load_dataset("/home/ohadr/ssd/dalle-mini/qampari/qampari.py",dataset_config,split=split).flatten()
-        fingerprint = mega_hash(to_source_target,"qampari",
-                            dataset_config,flattened_dataset,split)
-        transformed_dataset = flattened_dataset.map(
-                    to_source_target,
-                    batched=True,
-                    remove_columns=flattened_dataset.column_names,
-                    new_fingerprint = fingerprint
-                )
-
-        for i,element in enumerate(transformed_dataset):
-            yield i,element
+        flattened_dataset = load_dataset("/home/ohadr/ssd/dalle-mini/qampari/qampari.py",
+                                                    self.info.config_name,
+                                                    split=split).flatten()
+        if self.feature_format=="reranking":
+            fingerprint = mega_hash(to_source_target,"qampari",
+                                self.info.config_name,flattened_dataset,split)
+            transformed_dataset = flattened_dataset.map(
+                        to_source_target,
+                        batched=True,
+                        remove_columns=flattened_dataset.column_names,
+                        new_fingerprint = fingerprint
+                    )
+            for i,element in enumerate(transformed_dataset):
+                yield i,element
+        elif self.feature_format=="inference":
+            for i,element in enumerate(flattened_dataset):
+                element = to_dict_element(element,cols=flattened_dataset.column_names)
+                for j,ctx in enumerate(element['ctxs']):
+                    qid,ctx,question = element['qid'],ctx,element["question"]
+                    ctx.pop("score",None)
+                    source_element = {"source": f"Title: {ctx['title']}\nText: {ctx['text']}\nQuestion: {question}\n",
+                                        "meta":{**ctx,
+                                                "qid":qid,
+                                                "question":question}
+            }
+                    yield f"{qid}__{ctx['id']}__{j}", source_element
+
+        else:
+            assert False
 
 
 

qampari.py

@@ -191,8 +191,9 @@ class Qampari(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         filepath = dl_manager.download_and_extract(_URL)
+        # filepath = "/home/ohadr/.cache/huggingface/datasets/downloads/extracted/2e80c1d1256188099e12bd5542e05f537e4741d2cfbd2bfc83ae9aaf90623c09/qampari_retrievers"
         filepath = f"{filepath}/qampari_retrievers"
-        print(filepath)
+        # print(filepath)
         # filepath = "/home/joberant/home/ohadr/testbed/notebooks/qampari_retrievers"
 
         return [