mirror of https://github.com/explosion/spaCy.git
Add ENT_ID and NORM to DocBin strings (#8054)
Save strings for token attributes `ENT_ID` and `NORM` in `DocBin` strings.
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@ -64,13 +64,15 @@ def test_serialize_doc_span_groups(en_vocab):
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def test_serialize_doc_bin():
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doc_bin = DocBin(attrs=["LEMMA", "ENT_IOB", "ENT_TYPE"], store_user_data=True)
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doc_bin = DocBin(attrs=["LEMMA", "ENT_IOB", "ENT_TYPE", "NORM", "ENT_ID"], store_user_data=True)
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texts = ["Some text", "Lots of texts...", "..."]
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cats = {"A": 0.5}
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nlp = English()
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for doc in nlp.pipe(texts):
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doc.cats = cats
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doc.spans["start"] = [doc[0:2]]
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doc[0].norm_ = "UNUSUAL_TOKEN_NORM"
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doc[0].ent_id_ = "UNUSUAL_TOKEN_ENT_ID"
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doc_bin.add(doc)
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bytes_data = doc_bin.to_bytes()
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@ -82,6 +84,8 @@ def test_serialize_doc_bin():
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assert doc.text == texts[i]
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assert doc.cats == cats
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assert len(doc.spans) == 1
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assert doc[0].norm_ == "UNUSUAL_TOKEN_NORM"
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assert doc[0].ent_id_ == "UNUSUAL_TOKEN_ENT_ID"
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def test_serialize_doc_bin_unknown_spaces(en_vocab):
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@ -103,10 +103,12 @@ class DocBin:
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self.strings.add(token.text)
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self.strings.add(token.tag_)
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self.strings.add(token.lemma_)
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self.strings.add(token.norm_)
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self.strings.add(str(token.morph))
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self.strings.add(token.dep_)
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self.strings.add(token.ent_type_)
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self.strings.add(token.ent_kb_id_)
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self.strings.add(token.ent_id_)
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self.cats.append(doc.cats)
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self.user_data.append(srsly.msgpack_dumps(doc.user_data))
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self.span_groups.append(doc.spans.to_bytes())
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