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* Support nowrap setting in util.prints * Tidy up and fix whitespace * Simplify script and use read_jsonl helper * Add JSON schemas (see #2928) * Deprecate Doc.print_tree Will be replaced with Doc.to_json, which will produce a unified format * Add Doc.to_json() method (see #2928) Converts Doc objects to JSON using the same unified format as the training data. Method also supports serializing selected custom attributes in the doc._. space. * Remove outdated test * Add write_json and write_jsonl helpers * WIP: Update spacy train * Tidy up spacy train * WIP: Use wasabi for formatting * Add GoldParse helpers for JSON format * WIP: add debug-data command * Fix typo * Add missing import * Update wasabi pin * Add missing import * 💫 Refactor CLI (#2943) To be merged into #2932. ## Description - [x] refactor CLI To use [`wasabi`](https://github.com/ines/wasabi) - [x] use [`black`](https://github.com/ambv/black) for auto-formatting - [x] add `flake8` config - [x] move all messy UD-related scripts to `cli.ud` - [x] make converters function that take the opened file and return the converted data (instead of having them handle the IO) ### Types of change enhancement ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Update wasabi pin * Delete old test * Update errors * Fix typo * Tidy up and format remaining code * Fix formatting * Improve formatting of messages * Auto-format remaining code * Add tok2vec stuff to spacy.train * Fix typo * Update wasabi pin * Fix path checks for when train() is called as function * Reformat and tidy up pretrain script * Update argument annotations * Raise error if model language doesn't match lang * Document new train command
86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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from ...gold import iob_to_biluo
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def conllubio2json(input_data, n_sents=10, use_morphology=False, lang=None):
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"""
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Convert conllu files into JSON format for use with train cli.
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use_morphology parameter enables appending morphology to tags, which is
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useful for languages such as Spanish, where UD tags are not so rich.
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"""
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# by @dvsrepo, via #11 explosion/spacy-dev-resources
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docs = []
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sentences = []
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conll_tuples = read_conllx(input_data, use_morphology=use_morphology)
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for i, (raw_text, tokens) in enumerate(conll_tuples):
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sentence, brackets = tokens[0]
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sentences.append(generate_sentence(sentence))
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# Real-sized documents could be extracted using the comments on the
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# conluu document
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if len(sentences) % n_sents == 0:
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doc = create_doc(sentences, i)
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docs.append(doc)
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sentences = []
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return docs
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def read_conllx(input_data, use_morphology=False, n=0):
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i = 0
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for sent in input_data.strip().split("\n\n"):
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lines = sent.strip().split("\n")
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if lines:
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while lines[0].startswith("#"):
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lines.pop(0)
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tokens = []
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for line in lines:
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parts = line.split("\t")
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id_, word, lemma, pos, tag, morph, head, dep, _1, ner = parts
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if "-" in id_ or "." in id_:
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continue
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try:
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id_ = int(id_) - 1
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head = (int(head) - 1) if head != "0" else id_
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dep = "ROOT" if dep == "root" else dep
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tag = pos if tag == "_" else tag
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tag = tag + "__" + morph if use_morphology else tag
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ner = ner if ner else "O"
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tokens.append((id_, word, tag, head, dep, ner))
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except: # noqa: E722
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print(line)
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raise
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tuples = [list(t) for t in zip(*tokens)]
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yield (None, [[tuples, []]])
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i += 1
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if n >= 1 and i >= n:
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break
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def generate_sentence(sent):
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(id_, word, tag, head, dep, ner) = sent
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sentence = {}
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tokens = []
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ner = iob_to_biluo(ner)
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for i, id in enumerate(id_):
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token = {}
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token["orth"] = word[i]
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token["tag"] = tag[i]
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token["head"] = head[i] - id
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token["dep"] = dep[i]
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token["ner"] = ner[i]
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tokens.append(token)
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sentence["tokens"] = tokens
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return sentence
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def create_doc(sentences, id):
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doc = {}
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paragraph = {}
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doc["id"] = id
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doc["paragraphs"] = []
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paragraph["sentences"] = sentences
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doc["paragraphs"].append(paragraph)
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return doc
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