* Add convert CLI option to merge CoNLL-U subtokens
Add `-T` option to convert CLI that merges CoNLL-U subtokens into one
token in the converted data. Each CoNLL-U sentence is read into a `Doc`
and the `Retokenizer` is used to merge subtokens with features as
follows:
* `orth` is the merged token orth (should correspond to raw text and `#
text`)
* `tag` is all subtoken tags concatenated with `_`, e.g. `ADP_DET`
* `pos` is the POS of the syntactic root of the span (as determined by
the Retokenizer)
* `morph` is all morphological features merged
* `lemma` is all subtoken lemmas concatenated with ` `, e.g. `de o`
* with `-m` all morphological features are combined with the tag using
the separator `__`, e.g.
`ADP_DET__Definite=Def|Gender=Masc|Number=Sing|PronType=Art`
* `dep` is the dependency relation for the syntactic root of the span
(as determined by the Retokenizer)
Concatenated tags will be mapped to the UD POS of the syntactic root
(e.g., `ADP`) and the morphological features will be the combined
features.
In many cases, the original UD subtokens can be reconstructed from the
available features given a language-specific lookup table, e.g.,
Portuguese `do / ADP_DET /
Definite=Def|Gender=Masc|Number=Sing|PronType=Art` is `de / ADP`, `o /
DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` or lookup rules
for forms containing open class words like Spanish `hablarlo / VERB_PRON
/
Case=Acc|Gender=Masc|Number=Sing|Person=3|PrepCase=Npr|PronType=Prs|VerbForm=Inf`.
* Clean up imports
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
```
* Updates/bugfixes for NER/IOB converters
* Converter formats `ner` and `iob` use autodetect to choose a converter if
possible
* `iob2json` is reverted to handle sentence-per-line data like
`word1|pos1|ent1 word2|pos2|ent2`
* Fix bug in `merge_sentences()` so the second sentence in each batch isn't
skipped
* `conll_ner2json` is made more general so it can handle more formats with
whitespace-separated columns
* Supports all formats where the first column is the token and the final
column is the IOB tag; if present, the second column is the POS tag
* As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O`
separates documents
* Add option for segmenting sentences (new flag `-s`)
* Parser-based sentence segmentation with a provided model, otherwise with
sentencizer (new option `-b` to specify model)
* Can group sentences into documents with `n_sents` as long as sentence
segmentation is available
* Only applies automatic segmentation when there are no existing delimiters
in the data
* Provide info about settings applied during conversion with warnings and
suggestions if settings conflict or might not be not optimal.
* Add tests for common formats
* Add '(default)' back to docs for -c auto
* Add document count back to output
* Revert changes to converter output message
* Use explicit tabs in convert CLI test data
* Adjust/add messages for n_sents=1 default
* Add sample NER data to training examples
* Update README
* Add links in docs to example NER data
* Define msg within converters
* Add check for empty input file to CLI pretrain
* Raise error if JSONL is not a dict or contains neither `tokens` nor `text` key
* Skip empty values for correct pretrain keys and log a counter as warning
* Add tests for CLI pretrain core function make_docs.
* Add a short hint for the `tokens` key to the CLI pretrain docs
* Add success message to CLI pretrain
* Update model loading to fix the tests
* Skip empty values and do not create docs out of it
* merging conllu/conll and conllubio scripts
* tabs to spaces
* removing conllubio2json from converters/__init__.py
* Move not-really-CLI tests to misc
* Add converter test using no-ud data
* Fix test I broke
* removing include_biluo parameter
* fixing read_conllx
* remove include_biluo from convert.py
