random 10 + skip unfound

mmarco-train.py

@@ -20,6 +20,7 @@ from collections import defaultdict
 from gc import collect
 import datasets
 from tqdm import tqdm
+import random
 
 
 _CITATION = """
@@ -181,6 +182,9 @@ class MMarco(datasets.GeneratorBasedBuilder):
 
         # it would generate language by language so that it would be easier to constrain that each batch only contain one language;
         for lang in tqdm(languages, desc=f"Preparing training example for {len(languages)} languages."):
+            n_missed_q = 0
+            n_missed_d = 0
+
             collection_path, queries_path = args["collection"][lang], args["queries"][lang]
 
             collection = {}
@@ -194,7 +198,20 @@ class MMarco(datasets.GeneratorBasedBuilder):
  
             assert len(runs) == self.size_per_lang[lang]
             for query_id, (pos_ids, neg_ids) in runs.items():
+                if query_id not in queries:
+                    n_missed_q += 1
+                    continue
+ 
                 pos_ids, neg_ids = list(pos_ids), list(neg_ids)
+                pos_ids = [d for d in pos_ids if d in collection]
+                neg_ids = [d for d in neg_ids if d in collection]
+                if len(neg_ids) == 0 or len(pos_ids) == 0:
+                    n_missed_d += 1
+                    continue
+
+                NNEG = min(10, len(neg_ids))
+                neg_ids = random.choices(neg_ids, k=NNEG)
+
                 features = {
                     "query_id": query_id,
                     "query": queries[query_id],
@@ -207,4 +224,5 @@ class MMarco(datasets.GeneratorBasedBuilder):
                         "text": collection[neg_id],
                     } for neg_id in neg_ids],
                 }
-                yield f"{lang}-{query_id}-{idx}", features
+                yield f"{lang}-{query_id}-{idx}", features
+            print(f'Number of missed Q: {n_missed_q}. Number of missed D: {n_missed_d}')