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Append to MS MARCO replication log and fix spacy error #277

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Append to MS MARCO replication log and fix spacy error #277

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5 changes: 3 additions & 2 deletions docs/experiments-msmarco-document.md
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Original file line number Diff line number Diff line change
Expand Up @@ -85,7 +85,7 @@ precision@1 0.2
recall@3 0.56
recall@50 0.84
recall@1000 0.88
mrr 0.38882
mrr 0.3888
mrr@10 0.38271
```

Expand All @@ -103,7 +103,7 @@ precision@1 0.28
recall@3 0.32
recall@50 0.8
recall@1000 0.88
mrr 0.33617
mrr 0.33614
mrr@10 0.31978
```

Expand All @@ -126,3 +126,4 @@ If you were able to replicate these results, please submit a PR adding to the re
+ Results replicated by [@stephaniewhoo](https://github.com/stephaniewhoo) on 2020-10-25 (commit[`e815051`](https://github.com/castorini/pygaggle/commit/e815051f2cee1af98b370ee030b66c07a8a287f3)) (Tesla V100 on Compute Canada)
+ Results replicated by [@Dahlia-Chehata](https://github.com/Dahlia-Chehata) on 2021-01-20 (commit[`623285a`](https://github.com/castorini/pygaggle/commit/623285ae5092a9b27bc15a4a3b72bbe25910db49)) (Tesla K80 on Colab)
+ Results replicated by [@KaiSun314](https://github.com/KaiSun314) on 2021-01-27 (commit[`46bb71d`](https://github.com/castorini/pygaggle/commit/46bb71d39bfaa636ba44624434b83c0dc42654e8)) (Nvidia GeForce GTX 1060)
+ Results replicated by [@jx3yang](https://github.com/jx3yang) on 2022-05-23 (commit[`9728299`](https://github.com/castorini/pygaggle/commit/9728299b64f24d649b0fedca0a08a8b7d39064af)) (Tesla P100 on Colab)
4 changes: 2 additions & 2 deletions pygaggle/data/segmentation.py
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Expand Up @@ -26,7 +26,7 @@ class SegmentProcessor:
"""
def __init__(self, max_characters=10000000):
self.nlp = spacy.blank("en")
self.nlp.add_pipe(self.nlp.create_pipe("sentencizer"))
self.nlp.add_pipe("sentencizer")
self.max_characters = max_characters
self.aggregate_methods = {
"max": self._max_aggregate,
Expand All @@ -49,7 +49,7 @@ def segment(self, documents: List[Text], seg_size: int, stride: int) -> SegmentG
segmented_docs, doc_end_indexes, end_idx = [], [0], 0
for document in documents:
doc = self.nlp(document.text[:self.max_characters])
sentences = [sent.string.strip() for sent in doc.sents]
sentences = [sent.text.strip() for sent in doc.sents]
# If the text is empty (i.e. there are no sentences), the segment_text is solely the title of the document.
if len(sentences) == 0:
segment_text = document.title
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