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https://github.com/explosion/spaCy.git
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eb9b1858c4
Instead of a hard-coded NER tag simplification function that was only
intended for NorNE, map NER tags in CoNLL-U converter using a dict
provided as JSON as a command-line option.
Map NER entity types or new tag or to "" for 'O', e.g.:
```
{"PER": "PERSON", "BAD": ""}
=>
B-PER -> B-PERSON
B-BAD -> O
```
188 lines
6.9 KiB
Python
188 lines
6.9 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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from spacy.lang.en import English
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from spacy.cli.converters import conllu2json, iob2json, conll_ner2json
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from spacy.cli.pretrain import make_docs
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def test_cli_converters_conllu2json():
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# from NorNE: https://github.com/ltgoslo/norne/blob/3d23274965f513f23aa48455b28b1878dad23c05/ud/nob/no_bokmaal-ud-dev.conllu
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lines = [
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"1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\tO",
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"2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tB-PER",
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"3\tEilertsen\tEilertsen\tPROPN\t_\t_\t2\tname\t_\tI-PER",
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"4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tO",
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]
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input_data = "\n".join(lines)
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converted = conllu2json(input_data, n_sents=1)
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assert len(converted) == 1
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assert converted[0]["id"] == 0
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assert len(converted[0]["paragraphs"]) == 1
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assert len(converted[0]["paragraphs"][0]["sentences"]) == 1
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sent = converted[0]["paragraphs"][0]["sentences"][0]
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assert len(sent["tokens"]) == 4
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tokens = sent["tokens"]
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assert [t["orth"] for t in tokens] == ["Dommer", "Finn", "Eilertsen", "avstår"]
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assert [t["tag"] for t in tokens] == ["NOUN", "PROPN", "PROPN", "VERB"]
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assert [t["head"] for t in tokens] == [1, 2, -1, 0]
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assert [t["dep"] for t in tokens] == ["appos", "nsubj", "name", "ROOT"]
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assert [t["ner"] for t in tokens] == ["O", "B-PER", "L-PER", "O"]
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def test_cli_converters_conllu2json_name_ner_map():
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lines = [
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"1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\tname=O",
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"2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tSpaceAfter=No|name=B-PER",
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"3\tEilertsen\tEilertsen\tPROPN\t_\t_\t2\tname\t_\tname=I-PER",
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"4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tSpaceAfter=No|name=O",
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"5\t.\t$.\tPUNCT\t_\t_\t4\tpunct\t_\tname=B-BAD",
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]
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input_data = "\n".join(lines)
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converted = conllu2json(input_data, n_sents=1, ner_map={"PER": "PERSON", "BAD": ""})
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assert len(converted) == 1
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assert converted[0]["id"] == 0
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assert len(converted[0]["paragraphs"]) == 1
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assert converted[0]["paragraphs"][0]["raw"] == "Dommer FinnEilertsen avstår."
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assert len(converted[0]["paragraphs"][0]["sentences"]) == 1
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sent = converted[0]["paragraphs"][0]["sentences"][0]
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assert len(sent["tokens"]) == 5
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tokens = sent["tokens"]
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assert [t["orth"] for t in tokens] == ["Dommer", "Finn", "Eilertsen", "avstår", "."]
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assert [t["tag"] for t in tokens] == ["NOUN", "PROPN", "PROPN", "VERB", "PUNCT"]
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assert [t["head"] for t in tokens] == [1, 2, -1, 0, -1]
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assert [t["dep"] for t in tokens] == ["appos", "nsubj", "name", "ROOT", "punct"]
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assert [t["ner"] for t in tokens] == ["O", "B-PERSON", "L-PERSON", "O", "O"]
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def test_cli_converters_iob2json():
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lines = [
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"I|O like|O London|I-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O",
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"I|O like|O London|B-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O",
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"I|PRP|O like|VBP|O London|NNP|I-GPE and|CC|O New|NNP|B-GPE York|NNP|I-GPE City|NNP|I-GPE .|.|O",
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"I|PRP|O like|VBP|O London|NNP|B-GPE and|CC|O New|NNP|B-GPE York|NNP|I-GPE City|NNP|I-GPE .|.|O",
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]
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input_data = "\n".join(lines)
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converted = iob2json(input_data, n_sents=10)
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assert len(converted) == 1
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assert converted[0]["id"] == 0
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assert len(converted[0]["paragraphs"]) == 1
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assert len(converted[0]["paragraphs"][0]["sentences"]) == 4
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for i in range(0, 4):
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sent = converted[0]["paragraphs"][0]["sentences"][i]
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assert len(sent["tokens"]) == 8
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tokens = sent["tokens"]
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# fmt: off
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assert [t["orth"] for t in tokens] == ["I", "like", "London", "and", "New", "York", "City", "."]
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assert [t["ner"] for t in tokens] == ["O", "O", "U-GPE", "O", "B-GPE", "I-GPE", "L-GPE", "O"]
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# fmt: on
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def test_cli_converters_conll_ner2json():
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lines = [
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"-DOCSTART- -X- O O",
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"",
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"I\tO",
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"like\tO",
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"London\tB-GPE",
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"and\tO",
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"New\tB-GPE",
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"York\tI-GPE",
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"City\tI-GPE",
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".\tO",
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"",
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"I O",
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"like O",
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"London B-GPE",
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"and O",
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"New B-GPE",
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"York I-GPE",
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"City I-GPE",
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". O",
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"",
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"I PRP O",
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"like VBP O",
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"London NNP B-GPE",
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"and CC O",
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"New NNP B-GPE",
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"York NNP I-GPE",
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"City NNP I-GPE",
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". . O",
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"",
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"I PRP _ O",
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"like VBP _ O",
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"London NNP _ B-GPE",
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"and CC _ O",
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"New NNP _ B-GPE",
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"York NNP _ I-GPE",
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"City NNP _ I-GPE",
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". . _ O",
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"",
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"I\tPRP\t_\tO",
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"like\tVBP\t_\tO",
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"London\tNNP\t_\tB-GPE",
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"and\tCC\t_\tO",
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"New\tNNP\t_\tB-GPE",
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"York\tNNP\t_\tI-GPE",
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"City\tNNP\t_\tI-GPE",
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".\t.\t_\tO",
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]
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input_data = "\n".join(lines)
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converted = conll_ner2json(input_data, n_sents=10)
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assert len(converted) == 1
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assert converted[0]["id"] == 0
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assert len(converted[0]["paragraphs"]) == 1
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assert len(converted[0]["paragraphs"][0]["sentences"]) == 5
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for i in range(0, 5):
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sent = converted[0]["paragraphs"][0]["sentences"][i]
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assert len(sent["tokens"]) == 8
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tokens = sent["tokens"]
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# fmt: off
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assert [t["orth"] for t in tokens] == ["I", "like", "London", "and", "New", "York", "City", "."]
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assert [t["ner"] for t in tokens] == ["O", "O", "U-GPE", "O", "B-GPE", "I-GPE", "L-GPE", "O"]
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# fmt: on
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def test_pretrain_make_docs():
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nlp = English()
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valid_jsonl_text = {"text": "Some text"}
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docs, skip_count = make_docs(nlp, [valid_jsonl_text], 1, 10)
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assert len(docs) == 1
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assert skip_count == 0
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valid_jsonl_tokens = {"tokens": ["Some", "tokens"]}
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docs, skip_count = make_docs(nlp, [valid_jsonl_tokens], 1, 10)
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assert len(docs) == 1
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assert skip_count == 0
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invalid_jsonl_type = 0
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with pytest.raises(TypeError):
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make_docs(nlp, [invalid_jsonl_type], 1, 100)
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invalid_jsonl_key = {"invalid": "Does not matter"}
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with pytest.raises(ValueError):
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make_docs(nlp, [invalid_jsonl_key], 1, 100)
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empty_jsonl_text = {"text": ""}
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docs, skip_count = make_docs(nlp, [empty_jsonl_text], 1, 10)
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assert len(docs) == 0
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assert skip_count == 1
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empty_jsonl_tokens = {"tokens": []}
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docs, skip_count = make_docs(nlp, [empty_jsonl_tokens], 1, 10)
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assert len(docs) == 0
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assert skip_count == 1
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too_short_jsonl = {"text": "This text is not long enough"}
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docs, skip_count = make_docs(nlp, [too_short_jsonl], 10, 15)
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assert len(docs) == 0
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assert skip_count == 0
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too_long_jsonl = {"text": "This text contains way too much tokens for this test"}
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docs, skip_count = make_docs(nlp, [too_long_jsonl], 1, 5)
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assert len(docs) == 0
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assert skip_count == 0
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