readme: retrieval and evaluation

.gitignore

@@ -1 +1,2 @@
-./loading.py
+./loading.py
+./retrieval.py

README.md

@@ -244,5 +244,86 @@ for qrel in qrels:
     qrel["url"]  # url to the document in Wikipedia
 ```
 
+## Retrieval and Evaluation
+The following shows an example, how the dataset can be used to build a semantic search application.
+> This example is based on [clddp](https://github.com/kwang2049/clddp/tree/main) (`pip install -U cldpp`). One can further explore this [example](https://github.com/kwang2049/clddp/blob/main/examples/search_fiqa.sh) for convenient multi-GPU exact search.
+
+```python
+# Please install cldpp with `pip install -U cldpp`
+from clddp.retriever import Retriever, RetrieverConfig, Pooling, SimilarityFunction
+from clddp.dm import Separator
+from typing import Dict
+from clddp.dm import Query, Passage
+import torch
+import pytrec_eval
+import numpy as np
+from datasets import load_dataset
+
+
+# Define the retriever (DRAGON+ from https://arxiv.org/abs/2302.07452)
+class DRAGONPlus(Retriever):
+    def __init__(self) -> None:
+        config = RetrieverConfig(
+            query_model_name_or_path="facebook/dragon-plus-query-encoder",
+            passage_model_name_or_path="facebook/dragon-plus-context-encoder",
+            shared_encoder=False,
+            sep=Separator.blank,
+            pooling=Pooling.cls,
+            similarity_function=SimilarityFunction.dot_product,
+            query_max_length=512,
+            passage_max_length=512,
+        )
+        super().__init__(config)
+
+
+# Load data:
+passages = load_dataset("kwang2049/dapr", "ConditionalQA-corpus", split="test")
+queries = load_dataset("kwang2049/dapr", "ConditionalQA-queries", split="test")
+qrels_rows = load_dataset("kwang2049/dapr", "ConditionalQA-qrels", split="test")
+qrels: Dict[str, Dict[str, float]] = {}
+for qrel_row in qrels_rows:
+    qid = qrel_row["query_id"]
+    pid = qrel_row["corpus_id"]
+    rel = qrel_row["score"]
+    qrels.setdefault(qid, {})
+    qrels[qid][pid] = rel
+
+# Encode queries and passages: (refer to https://github.com/kwang2049/clddp/blob/main/examples/search_fiqa.sh for multi-GPU exact search)
+retriever = DRAGONPlus()
+retriever.eval()
+queries = [Query(query_id=query["_id"], text=query["text"]) for query in queries]
+passages = [
+    Passage(passage_id=passage["_id"], text=passage["text"]) for passage in passages
+]
+query_embeddings = retriever.encode_queries(queries)
+with torch.no_grad():  # Takes around a minute on a V100 GPU
+    passage_embeddings, passage_mask = retriever.encode_passages(passages)
+
+# Calculate the similarities and keep top-K:
+similarity_scores = torch.matmul(
+    query_embeddings, passage_embeddings.t()
+)  # (query_num, passage_num)
+topk = torch.topk(similarity_scores, k=10)
+topk_values: torch.Tensor = topk[0]
+topk_indices: torch.LongTensor = topk[1]
+topk_value_lists = topk_values.tolist()
+topk_index_lists = topk_indices.tolist()
+
+# Run evaluation with pytrec_eval:
+retrieval_scores: Dict[str, Dict[str, float]] = {}
+for query_i, (values, indices) in enumerate(zip(topk_value_lists, topk_index_lists)):
+    query_id = queries[query_i].query_id
+    retrieval_scores.setdefault(query_id, {})
+    for value, passage_i in zip(values, indices):
+        passage_id = passages[passage_i].passage_id
+        retrieval_scores[query_id][passage_id] = value
+evaluator = pytrec_eval.RelevanceEvaluator(
+    query_relevance=qrels, measures=["ndcg_cut_10"]
+)
+query_performances: Dict[str, Dict[str, float]] = evaluator.evaluate(retrieval_scores)
+ndcg = np.mean([score["ndcg_cut_10"] for score in query_performances.values()])
+print(ndcg)  # 0.21796083196880855
+```
+
 ## Note
 This dataset was created with `datasets==2.15.0`. Make sure to use this or a newer version of the datasets library.

retrieval.py

@@ -0,0 +1,74 @@
+# Please install cldpp with `pip install -U cldpp`
+from clddp.retriever import Retriever, RetrieverConfig, Pooling, SimilarityFunction
+from clddp.dm import Separator
+from typing import Dict
+from clddp.dm import Query, Passage
+import torch
+import pytrec_eval
+import numpy as np
+from datasets import load_dataset
+
+
+# Define the retriever (DRAGON+ from https://arxiv.org/abs/2302.07452)
+class DRAGONPlus(Retriever):
+    def __init__(self) -> None:
+        config = RetrieverConfig(
+            query_model_name_or_path="facebook/dragon-plus-query-encoder",
+            passage_model_name_or_path="facebook/dragon-plus-context-encoder",
+            shared_encoder=False,
+            sep=Separator.blank,
+            pooling=Pooling.cls,
+            similarity_function=SimilarityFunction.dot_product,
+            query_max_length=512,
+            passage_max_length=512,
+        )
+        super().__init__(config)
+
+
+# Load data:
+passages = load_dataset("kwang2049/dapr", "ConditionalQA-corpus", split="test")
+queries = load_dataset("kwang2049/dapr", "ConditionalQA-queries", split="test")
+qrels_rows = load_dataset("kwang2049/dapr", "ConditionalQA-qrels", split="test")
+qrels: Dict[str, Dict[str, float]] = {}
+for qrel_row in qrels_rows:
+    qid = qrel_row["query_id"]
+    pid = qrel_row["corpus_id"]
+    rel = qrel_row["score"]
+    qrels.setdefault(qid, {})
+    qrels[qid][pid] = rel
+
+# Encode queries and passages: (refer to https://github.com/kwang2049/clddp/blob/main/examples/search_fiqa.sh for multi-GPU exact search)
+retriever = DRAGONPlus()
+retriever.eval()
+queries = [Query(query_id=query["_id"], text=query["text"]) for query in queries]
+passages = [
+    Passage(passage_id=passage["_id"], text=passage["text"]) for passage in passages
+]
+query_embeddings = retriever.encode_queries(queries)
+with torch.no_grad():  # Takes around a minute on a V100 GPU
+    passage_embeddings, passage_mask = retriever.encode_passages(passages)
+
+# Calculate the similarities and keep top-K:
+similarity_scores = torch.matmul(
+    query_embeddings, passage_embeddings.t()
+)  # (query_num, passage_num)
+topk = torch.topk(similarity_scores, k=10)
+topk_values: torch.Tensor = topk[0]
+topk_indices: torch.LongTensor = topk[1]
+topk_value_lists = topk_values.tolist()
+topk_index_lists = topk_indices.tolist()
+
+# Run evaluation with pytrec_eval:
+retrieval_scores: Dict[str, Dict[str, float]] = {}
+for query_i, (values, indices) in enumerate(zip(topk_value_lists, topk_index_lists)):
+    query_id = queries[query_i].query_id
+    retrieval_scores.setdefault(query_id, {})
+    for value, passage_i in zip(values, indices):
+        passage_id = passages[passage_i].passage_id
+        retrieval_scores[query_id][passage_id] = value
+evaluator = pytrec_eval.RelevanceEvaluator(
+    query_relevance=qrels, measures=["ndcg_cut_10"]
+)
+query_performances: Dict[str, Dict[str, float]] = evaluator.evaluate(retrieval_scores)
+ndcg = np.mean([score["ndcg_cut_10"] for score in query_performances.values()])
+print(ndcg)  # 0.21796083196880855