spaCy/examples/training/ner_example_data/README.md

## Examples of NER/IOB data that can be converted with `spacy convert`

spacy JSON training files were generated with:

```
python -m spacy convert -c iob -s -n 10 -b en file.iob
```
Updates/bugfixes for NER/IOB converters (#4186) * 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 2019-08-29 13:04:01 +03:00			## Examples of NER/IOB data that can be converted with `spacy convert`

			`spacy JSON training files were generated with:`

			```
			`python -m spacy convert -c iob -s -n 10 -b en file.iob`
			```