update

README.md

@@ -1,12 +1,27 @@
 ---
-title: Monot5
-emoji: 👁
-colorFrom: yellow
-colorTo: yellow
+title: PyTerrier MonoT5
+emoji: 🐕
+colorFrom: green
+colorTo: green
 sdk: gradio
-sdk_version: 3.8.2
+sdk_version: 3.7
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🐕 PyTerrier: MonoT5
+
+This is a demonstration of [PyTerrier's T5 package](https://github.com/terrierteam/pyterrier_t5).
+
+MonoT5 functions as a `R→R` (reranking, result-to-result) transformer and can be used in pipelines accordingly. For example, you will
+often pipe the output of a first-stage retrieval function into MonoT5:
+
+<div class="pipeline">
+  <div class="df" title="Query Frame">Q</div>
+  <div class="transformer" title="PisaRetrieve Transformer">TerrierRetrieve</div>
+  <div class="df" title="Result Frame">R</div>
+  <div class="transformer" title="get_text Transformer">get_text</div>
+  <div class="df" title="Result Frame">R</div>
+  <div class="transformer attn" title="MonoT5 Transformer">MonoT5</div>
+  <div class="df" title="Result Frame">R</div>
+</div>

app.py

@@ -0,0 +1,37 @@
+import pandas as pd
+import gradio as gr
+import pyterrier as pt
+pt.init()
+from pyterrier_gradio import Demo, MarkdownFile, interface, df2code, code2md, EX_R
+from pyterrier_t5 import MonoT5ReRanker
+
+model = MonoT5ReRanker()
+
+COLAB_NAME = 'pyterrier_t5.ipynb'
+COLAB_INSTALL = '''
+!pip install -q git+https://github.com/terrier-org/pyterrier_t5
+'''.strip()
+
+def predict(input):
+  code = f'''import pandas as pd
+import pyterrier as pt ; pt.init()
+from pyterrier_t5 import MonoT5ReRanker
+
+model = MonoT5ReRanker()
+
+model({df2code(input)})
+'''
+  res = model(input)
+  res['score'] = res['score'].map(lambda x: round(x, 4))
+  res = res.sort_values(['qid', 'rank'])
+  return (res, code2md(code, COLAB_INSTALL, COLAB_NAME, colab=False))
+
+interface(
+  MarkdownFile('README.md'),
+  Demo(
+    predict,
+    EX_R,
+    []
+  ),
+  MarkdownFile('wrapup.md'),
+).launch(share=False)

packages.txt

@@ -0,0 +1,5 @@
+openjdk-11-jdk
+openjdk-11-jre-headless
+openjdk-11-jre
+openjdk-11-jre-headless
+debianutils

requirements.txt

@@ -0,0 +1,5 @@
+git+https://github.com/seanmacavaney/pyterrier_gradio@v0.0.5
+git+https://github.com/terrier-org/pyterrier
+git+https://github.com/terrier-org/pyterrier_t5
+ir_datasets
+ir_measures

wrapup.md

@@ -0,0 +1,4 @@
+### References & Credits
+
+ - Ronak Pradeep, Rodrigo Nogueira, and Jimmy Lin. [The Expando-Mono-Duo Design Pattern for Text Ranking withPretrained Sequence-to-Sequence Models.](https://arxiv.org/pdf/2101.05667.pdf)
+ - 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.