minusminus

app.py

@@ -1,17 +1,27 @@
+import pyterrier as pt
+pt.init()
+import numpy as np
 import pandas as pd
 import gradio as gr
-from pyterrier_doc2query import Doc2Query
+from pyterrier_doc2query import Doc2Query, QueryScorer, QueryFilter
+from pyterrier_dr import ElectraScorer
 from pyterrier_gradio import Demo, MarkdownFile, interface, df2code, code2md, EX_D
 
 MODEL = 'macavaney/doc2query-t5-base-msmarco'
+SCORE_MODEL = 'crystina-z/monoELECTRA_LCE_nneg31'
+PERCENTILES_BY_5 = np.array([-3.80468750e+00, -2.21679688e+00, -1.25683594e+00, -5.58105469e-01, -7.65323639e-04, 4.69482422e-01, 8.83300781e-01, 1.25878906e+00, 1.61035156e+00, 1.94335938e+00, 2.26562500e+00, 2.58007812e+00, 2.89648438e+00, 3.21484375e+00, 3.54687500e+00, 3.90039062e+00, 4.30078125e+00, 4.77343750e+00, 5.37109375e+00])
+COLORS = ['rgb(252, 132, 100)','rgb(252, 148, 116)','rgb(252, 166, 137)','rgb(252, 183, 156)','rgb(253, 200, 178)','rgb(254, 215, 198)','rgb(255, 228, 216)','rgb(255, 237, 228)','rgb(256, 245, 240)','rgb(256, 256, 256)','rgb(247, 252, 245)','rgb(240, 250, 237)','rgb(233, 247, 228)','rgb(222, 242, 216)','rgb(209, 237, 203)','rgb(195, 232, 188)','rgb(180, 225, 173)','rgb(163, 218, 157)','rgb(145, 210, 142)','rgb(125, 201, 126)']
 
 doc2query = Doc2Query(MODEL, append=True, num_samples=5)
+electra = ElectraScorer()
+query_scorer = QueryScorer(electra)
 
 COLAB_NAME = 'pyterrier_doc2query.ipynb'
 COLAB_INSTALL = '''
 !pip install -q git+https://github.com/terrier-org/pyterrier
 !pip install -q git+https://github.com/terrierteam/pyterrier_doc2query
 '''.strip()
+COLAB_INSTALL_MM = COLAB_INSTALL + '\n!pip install -q git+https://github.com/terrierteam/pyterrier_dr faiss-cpu'
 
 def predict(input, model, append, num_samples):
   assert model == MODEL
@@ -24,7 +34,68 @@ doc2query = Doc2Query({repr(model)}, append={append}, num_samples={num_samples})
 
 doc2query({df2code(input)})
 '''
-  return (doc2query(input), code2md(code, COLAB_INSTALL, COLAB_NAME))
+  res = doc2query(input)
+  vis = generate_vis(res)
+  return (doc2query(input), code2md(code, COLAB_INSTALL, COLAB_NAME), vis)
+
+def generate_vis(df):
+  result = []
+  for row in df.itertuples(index=False):
+    qs = []
+    if hasattr(row, 'querygen_score'):
+      for q, score in zip(row.querygen.split('\n'), row.querygen_score):
+        bucket = np.searchsorted(PERCENTILES_BY_5, score)
+        color = COLORS[bucket]
+        percentile = bucket * 5
+        qs.append(f'''
+<div>
+<span title="score={score:.4f}, in the {percentile}th percentile of scores" style="border: 1px solid #888; border-radius: 3px; font-size: 0.6em; font-family: monospace; background-color: {color}; padding: 1px 3px;">{percentile}th</span> {q}
+</div>
+''')
+    elif hasattr(row, 'querygen'):
+      for q in row.querygen.split('\n'):
+        qs.append(f'''
+<div>{q}</div>
+''')
+    qs = '\n'.join(qs)
+    if qs:
+      qs = f'''
+<div><strong>Expansion Queries:</strong></div>
+{qs}
+'''
+    text = row.text.replace('\n', '<br/>')
+    result.append(f'''
+<div style="font-size: 1.2em;">Document: <strong>{row.docno}</strong></div>
+<div style="margin: 4px 0 16px; padding: 4px; border: 1px solid black;">
+<div>
+{text}
+</div>
+{qs}
+</div>
+''')
+  return '\n'.join(result)
+
+def predict_mm(input, model, num_samples, score_model):
+  assert model == MODEL
+  assert score_model == SCORE_MODEL
+  doc2query.append = False
+  doc2query.num_samples = num_samples
+  pipeline = doc2query >> query_scorer
+  code = f'''import pyterrier as pt ; pt.init()
+import pandas as pd
+from pyterrier_doc2query import Doc2Query, QueryScorer
+from pyterrier_dr import ElectraScorer
+
+doc2query = Doc2Query({repr(model)}, append=False, num_samples={num_samples})
+scorer = ElectraScorer({repr(score_model)})
+pipeline = doc2query >> QueryScorer(scorer)
+
+pipeline({df2code(input)})
+'''
+  res = pipeline(input)
+  vis = generate_vis(res)
+  res['querygen_score'] = res['querygen_score'].apply(lambda x: '[ ' + ', '.join(str(v) for v in x) + ' ]')
+  return (res, code2md(code, COLAB_INSTALL_MM, COLAB_NAME), vis)
 
 interface(
   MarkdownFile('README.md'),
@@ -48,5 +119,28 @@ interface(
       label='# Queries'
     )],
   ),
+  MarkdownFile('mm.md'),
+  Demo(
+    predict_mm,
+    EX_D,
+    [
+    gr.Dropdown(
+      choices=[MODEL],
+      value=MODEL,
+      label='Model',
+      interactive=False,
+    ), gr.Slider(
+      minimum=1,
+      maximum=10,
+      value=doc2query.num_samples,
+      step=1.,
+      label='# Queries'
+    ), gr.Dropdown(
+      choices=[SCORE_MODEL],
+      value=SCORE_MODEL,
+      label='Filter',
+      interactive=False,
+    )],
+  ),
   MarkdownFile('wrapup.md'),
-).launch(share=False)
+).launch(share=True)

mm.md

@@ -0,0 +1,19 @@
+### Doc2Query−−: When Less is More
+
+You might notice that not all the generated queries look related to the source text. This is due
+to a defect that can appear in sequence-to-sequence models known as "[hallucination](https://aclanthology.org/2020.acl-main.173/)".
+
+Doc2Query−− can filter out these low-quality queries by measuring the relevance between them and the text that
+generated them using a scoring transformer `S`. It is applied as two transformers that follow the Doc2Query generator:
+
+<div class="pipeline">
+  <div class="df" title="Document Frame">D</div>
+  <div class="transformer" title="Doc2Query Transformer">Doc2Query</div>
+  <div class="df" title="Document Frame">D</div>
+  <div class="transformer attn" title="Doc2Query Transformer">QueryScorer
+    <div class="artefact" title="Scorer Transformer">S</div>
+  </div>
+  <div class="df" title="Document Frame">D</div>
+  <div class="transformer attn" title="Doc2Query Transformer">QueryFilter</div>
+  <div class="df" title="Document Frame">D</div>
+</div>

requirements.txt

@@ -1,5 +1,7 @@
 git+https://github.com/seanmacavaney/pyterrier_gradio@v0.0.4
 git+https://github.com/terrier-org/pyterrier
-git+https://github.com/terrierteam/pyterrier_doc2query@master
+git+https://github.com/terrierteam/pyterrier_doc2query@minusminus
+git+https://github.com/terrierteam/pyterrier_dr
 ir_datasets
 ir_measures
+faiss-cpu

wrapup.md

@@ -1,10 +1,10 @@
 ### Putting it all together
 
-You can use Doc2Query in an indexing pipeline to build an index of the expanded documents:
+You can use Doc2Query or Doc2Query-- in an indexing pipeline to build an index of the expanded documents:
 
 <div class="pipeline">
   <div class="df" title="Document Frame">D</div>
-  <div class="transformer attn" title="Doc2Query Transformer">Doc2Query</div>
+  <div class="transformer attn" title="Doc2Query or Doc2Query&minus;&minus; Transformer">Doc2Query[&minus;&minus;]</div>
   <div class="df" title="Document Frame">D</div>
   <div class="transformer" title="Indexer">Indexer</div>
   <div class="artefact" title="Doc2Query Index">IDX</div>
@@ -39,4 +39,5 @@ bm25 = pt.BatchRetrieve('./msmarco_psg', wmodel="BM25")
 ### References & Credits
 
  - Rodrigo Nogueira and Jimmy Lin. [From doc2query to docTTTTTquery](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf).
+ - Mitko Gospodinov, Sean MacAvaney, and Craig Macdonald. Doc2Query--: When Less is More. ECIR 2023.
  - Craig Macdonald, Nicola Tonellotto, Sean MacAvaney, Iadh Ounis. [PyTerrier: Declarative Experimentation in Python from BM25 to Dense Retrieval](https://dl.acm.org/doi/abs/10.1145/3459637.3482013). CIKM 2021.