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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 * Remove outdated test * Add write_json and write_jsonl helpers * WIP: Update spacy train * Tidy up spacy train * WIP: Use wasabi for formatting * Add GoldParse helpers for JSON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
22 lines
664 B
Python
22 lines
664 B
Python
# coding: utf8
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from __future__ import unicode_literals
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import ujson
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from ...util import get_lang_class
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from .._messages import Messages
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def ner_jsonl2json(input_data, lang=None, n_sents=10, use_morphology=False):
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if lang is None:
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raise ValueError(Messages.M054)
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json_docs = []
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input_tuples = [ujson.loads(line) for line in input_data]
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nlp = get_lang_class(lang)()
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for i, (raw_text, ents) in enumerate(input_tuples):
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doc = nlp.make_doc(raw_text)
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doc[0].is_sent_start = True
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doc.ents = [doc.char_span(s, e, label=L) for s, e, L in ents["entities"]]
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json_docs.append(doc.to_json())
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return json_docs
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